10 Real World Data Science Case Studies Projects with Example

Top 10 Data Science Case Studies Projects with Examples and Solutions in Python to inspire your data science learning in 2023.

10 Real World Data Science Case Studies Projects with Example

BelData science has been a trending buzzword in recent times. With wide applications in various sectors like healthcare , education, retail, transportation, media, and banking -data science applications are at the core of pretty much every industry out there. The possibilities are endless: analysis of frauds in the finance sector or the personalization of recommendations on eCommerce businesses.  We have developed ten exciting data science case studies to explain how data science is leveraged across various industries to make smarter decisions and develop innovative personalized products tailored to specific customers.

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Walmart Sales Forecasting Data Science Project

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Data science case studies in retail , data science case study examples in entertainment industry , data analytics case study examples in travel industry , case studies for data analytics in social media , real world data science projects in healthcare, data analytics case studies in oil and gas, what is a case study in data science, how do you prepare a data science case study, 10 most interesting data science case studies with examples.

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So, without much ado, let's get started with data science business case studies !

With humble beginnings as a simple discount retailer, today, Walmart operates in 10,500 stores and clubs in 24 countries and eCommerce websites, employing around 2.2 million people around the globe. For the fiscal year ended January 31, 2021, Walmart's total revenue was $559 billion showing a growth of $35 billion with the expansion of the eCommerce sector. Walmart is a data-driven company that works on the principle of 'Everyday low cost' for its consumers. To achieve this goal, they heavily depend on the advances of their data science and analytics department for research and development, also known as Walmart Labs. Walmart is home to the world's largest private cloud, which can manage 2.5 petabytes of data every hour! To analyze this humongous amount of data, Walmart has created 'Data Café,' a state-of-the-art analytics hub located within its Bentonville, Arkansas headquarters. The Walmart Labs team heavily invests in building and managing technologies like cloud, data, DevOps , infrastructure, and security.

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Walmart is experiencing massive digital growth as the world's largest retailer . Walmart has been leveraging Big data and advances in data science to build solutions to enhance, optimize and customize the shopping experience and serve their customers in a better way. At Walmart Labs, data scientists are focused on creating data-driven solutions that power the efficiency and effectiveness of complex supply chain management processes. Here are some of the applications of data science  at Walmart:

i) Personalized Customer Shopping Experience

Walmart analyses customer preferences and shopping patterns to optimize the stocking and displaying of merchandise in their stores. Analysis of Big data also helps them understand new item sales, make decisions on discontinuing products, and the performance of brands.

ii) Order Sourcing and On-Time Delivery Promise

Millions of customers view items on Walmart.com, and Walmart provides each customer a real-time estimated delivery date for the items purchased. Walmart runs a backend algorithm that estimates this based on the distance between the customer and the fulfillment center, inventory levels, and shipping methods available. The supply chain management system determines the optimum fulfillment center based on distance and inventory levels for every order. It also has to decide on the shipping method to minimize transportation costs while meeting the promised delivery date.

iii) Packing Optimization 

Also known as Box recommendation is a daily occurrence in the shipping of items in retail and eCommerce business. When items of an order or multiple orders for the same customer are ready for packing, Walmart has developed a recommender system that picks the best-sized box which holds all the ordered items with the least in-box space wastage within a fixed amount of time. This Bin Packing problem is a classic NP-Hard problem familiar to data scientists .

Whenever items of an order or multiple orders placed by the same customer are picked from the shelf and are ready for packing, the box recommendation system determines the best-sized box to hold all the ordered items with a minimum of in-box space wasted. This problem is known as the Bin Packing Problem, another classic NP-Hard problem familiar to data scientists.

Here is a link to a sales prediction data science case study to help you understand the applications of Data Science in the real world. Walmart Sales Forecasting Project uses historical sales data for 45 Walmart stores located in different regions. Each store contains many departments, and you must build a model to project the sales for each department in each store. This data science case study aims to create a predictive model to predict the sales of each product. You can also try your hands-on Inventory Demand Forecasting Data Science Project to develop a machine learning model to forecast inventory demand accurately based on historical sales data.

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Amazon is an American multinational technology-based company based in Seattle, USA. It started as an online bookseller, but today it focuses on eCommerce, cloud computing , digital streaming, and artificial intelligence . It hosts an estimate of 1,000,000,000 gigabytes of data across more than 1,400,000 servers. Through its constant innovation in data science and big data Amazon is always ahead in understanding its customers. Here are a few data analytics case study examples at Amazon:

i) Recommendation Systems

Data science models help amazon understand the customers' needs and recommend them to them before the customer searches for a product; this model uses collaborative filtering. Amazon uses 152 million customer purchases data to help users to decide on products to be purchased. The company generates 35% of its annual sales using the Recommendation based systems (RBS) method.

Here is a Recommender System Project to help you build a recommendation system using collaborative filtering. 

ii) Retail Price Optimization

Amazon product prices are optimized based on a predictive model that determines the best price so that the users do not refuse to buy it based on price. The model carefully determines the optimal prices considering the customers' likelihood of purchasing the product and thinks the price will affect the customers' future buying patterns. Price for a product is determined according to your activity on the website, competitors' pricing, product availability, item preferences, order history, expected profit margin, and other factors.

Check Out this Retail Price Optimization Project to build a Dynamic Pricing Model.

iii) Fraud Detection

Being a significant eCommerce business, Amazon remains at high risk of retail fraud. As a preemptive measure, the company collects historical and real-time data for every order. It uses Machine learning algorithms to find transactions with a higher probability of being fraudulent. This proactive measure has helped the company restrict clients with an excessive number of returns of products.

You can look at this Credit Card Fraud Detection Project to implement a fraud detection model to classify fraudulent credit card transactions.

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Let us explore data analytics case study examples in the entertainment indusry.

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Netflix started as a DVD rental service in 1997 and then has expanded into the streaming business. Headquartered in Los Gatos, California, Netflix is the largest content streaming company in the world. Currently, Netflix has over 208 million paid subscribers worldwide, and with thousands of smart devices which are presently streaming supported, Netflix has around 3 billion hours watched every month. The secret to this massive growth and popularity of Netflix is its advanced use of data analytics and recommendation systems to provide personalized and relevant content recommendations to its users. The data is collected over 100 billion events every day. Here are a few examples of data analysis case studies applied at Netflix :

i) Personalized Recommendation System

Netflix uses over 1300 recommendation clusters based on consumer viewing preferences to provide a personalized experience. Some of the data that Netflix collects from its users include Viewing time, platform searches for keywords, Metadata related to content abandonment, such as content pause time, rewind, rewatched. Using this data, Netflix can predict what a viewer is likely to watch and give a personalized watchlist to a user. Some of the algorithms used by the Netflix recommendation system are Personalized video Ranking, Trending now ranker, and the Continue watching now ranker.

ii) Content Development using Data Analytics

Netflix uses data science to analyze the behavior and patterns of its user to recognize themes and categories that the masses prefer to watch. This data is used to produce shows like The umbrella academy, and Orange Is the New Black, and the Queen's Gambit. These shows seem like a huge risk but are significantly based on data analytics using parameters, which assured Netflix that they would succeed with its audience. Data analytics is helping Netflix come up with content that their viewers want to watch even before they know they want to watch it.

iii) Marketing Analytics for Campaigns

Netflix uses data analytics to find the right time to launch shows and ad campaigns to have maximum impact on the target audience. Marketing analytics helps come up with different trailers and thumbnails for other groups of viewers. For example, the House of Cards Season 5 trailer with a giant American flag was launched during the American presidential elections, as it would resonate well with the audience.

Here is a Customer Segmentation Project using association rule mining to understand the primary grouping of customers based on various parameters.

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In a world where Purchasing music is a thing of the past and streaming music is a current trend, Spotify has emerged as one of the most popular streaming platforms. With 320 million monthly users, around 4 billion playlists, and approximately 2 million podcasts, Spotify leads the pack among well-known streaming platforms like Apple Music, Wynk, Songza, amazon music, etc. The success of Spotify has mainly depended on data analytics. By analyzing massive volumes of listener data, Spotify provides real-time and personalized services to its listeners. Most of Spotify's revenue comes from paid premium subscriptions. Here are some of the examples of case study on data analytics used by Spotify to provide enhanced services to its listeners:

i) Personalization of Content using Recommendation Systems

Spotify uses Bart or Bayesian Additive Regression Trees to generate music recommendations to its listeners in real-time. Bart ignores any song a user listens to for less than 30 seconds. The model is retrained every day to provide updated recommendations. A new Patent granted to Spotify for an AI application is used to identify a user's musical tastes based on audio signals, gender, age, accent to make better music recommendations.

Spotify creates daily playlists for its listeners, based on the taste profiles called 'Daily Mixes,' which have songs the user has added to their playlists or created by the artists that the user has included in their playlists. It also includes new artists and songs that the user might be unfamiliar with but might improve the playlist. Similar to it is the weekly 'Release Radar' playlists that have newly released artists' songs that the listener follows or has liked before.

ii) Targetted marketing through Customer Segmentation

With user data for enhancing personalized song recommendations, Spotify uses this massive dataset for targeted ad campaigns and personalized service recommendations for its users. Spotify uses ML models to analyze the listener's behavior and group them based on music preferences, age, gender, ethnicity, etc. These insights help them create ad campaigns for a specific target audience. One of their well-known ad campaigns was the meme-inspired ads for potential target customers, which was a huge success globally.

iii) CNN's for Classification of Songs and Audio Tracks

Spotify builds audio models to evaluate the songs and tracks, which helps develop better playlists and recommendations for its users. These allow Spotify to filter new tracks based on their lyrics and rhythms and recommend them to users like similar tracks ( collaborative filtering). Spotify also uses NLP ( Natural language processing) to scan articles and blogs to analyze the words used to describe songs and artists. These analytical insights can help group and identify similar artists and songs and leverage them to build playlists.

Here is a Music Recommender System Project for you to start learning. We have listed another music recommendations dataset for you to use for your projects: Dataset1 . You can use this dataset of Spotify metadata to classify songs based on artists, mood, liveliness. Plot histograms, heatmaps to get a better understanding of the dataset. Use classification algorithms like logistic regression, SVM, and Principal component analysis to generate valuable insights from the dataset.

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Below you will find case studies for data analytics in the travel and tourism industry.

Airbnb was born in 2007 in San Francisco and has since grown to 4 million Hosts and 5.6 million listings worldwide who have welcomed more than 1 billion guest arrivals in almost every country across the globe. Airbnb is active in every country on the planet except for Iran, Sudan, Syria, and North Korea. That is around 97.95% of the world. Using data as a voice of their customers, Airbnb uses the large volume of customer reviews, host inputs to understand trends across communities, rate user experiences, and uses these analytics to make informed decisions to build a better business model. The data scientists at Airbnb are developing exciting new solutions to boost the business and find the best mapping for its customers and hosts. Airbnb data servers serve approximately 10 million requests a day and process around one million search queries. Data is the voice of customers at AirBnB and offers personalized services by creating a perfect match between the guests and hosts for a supreme customer experience. 

i) Recommendation Systems and Search Ranking Algorithms

Airbnb helps people find 'local experiences' in a place with the help of search algorithms that make searches and listings precise. Airbnb uses a 'listing quality score' to find homes based on the proximity to the searched location and uses previous guest reviews. Airbnb uses deep neural networks to build models that take the guest's earlier stays into account and area information to find a perfect match. The search algorithms are optimized based on guest and host preferences, rankings, pricing, and availability to understand users’ needs and provide the best match possible.

ii) Natural Language Processing for Review Analysis

Airbnb characterizes data as the voice of its customers. The customer and host reviews give a direct insight into the experience. The star ratings alone cannot be an excellent way to understand it quantitatively. Hence Airbnb uses natural language processing to understand reviews and the sentiments behind them. The NLP models are developed using Convolutional neural networks .

Practice this Sentiment Analysis Project for analyzing product reviews to understand the basic concepts of natural language processing.

iii) Smart Pricing using Predictive Analytics

The Airbnb hosts community uses the service as a supplementary income. The vacation homes and guest houses rented to customers provide for rising local community earnings as Airbnb guests stay 2.4 times longer and spend approximately 2.3 times the money compared to a hotel guest. The profits are a significant positive impact on the local neighborhood community. Airbnb uses predictive analytics to predict the prices of the listings and help the hosts set a competitive and optimal price. The overall profitability of the Airbnb host depends on factors like the time invested by the host and responsiveness to changing demands for different seasons. The factors that impact the real-time smart pricing are the location of the listing, proximity to transport options, season, and amenities available in the neighborhood of the listing.

Here is a Price Prediction Project to help you understand the concept of predictive analysis which is widely common in case studies for data analytics. 

Uber is the biggest global taxi service provider. As of December 2018, Uber has 91 million monthly active consumers and 3.8 million drivers. Uber completes 14 million trips each day. Uber uses data analytics and big data-driven technologies to optimize their business processes and provide enhanced customer service. The Data Science team at uber has been exploring futuristic technologies to provide better service constantly. Machine learning and data analytics help Uber make data-driven decisions that enable benefits like ride-sharing, dynamic price surges, better customer support, and demand forecasting. Here are some of the real world data science projects used by uber:

i) Dynamic Pricing for Price Surges and Demand Forecasting

Uber prices change at peak hours based on demand. Uber uses surge pricing to encourage more cab drivers to sign up with the company, to meet the demand from the passengers. When the prices increase, the driver and the passenger are both informed about the surge in price. Uber uses a predictive model for price surging called the 'Geosurge' ( patented). It is based on the demand for the ride and the location.

ii) One-Click Chat

Uber has developed a Machine learning and natural language processing solution called one-click chat or OCC for coordination between drivers and users. This feature anticipates responses for commonly asked questions, making it easy for the drivers to respond to customer messages. Drivers can reply with the clock of just one button. One-Click chat is developed on Uber's machine learning platform Michelangelo to perform NLP on rider chat messages and generate appropriate responses to them.

iii) Customer Retention

Failure to meet the customer demand for cabs could lead to users opting for other services. Uber uses machine learning models to bridge this demand-supply gap. By using prediction models to predict the demand in any location, uber retains its customers. Uber also uses a tier-based reward system, which segments customers into different levels based on usage. The higher level the user achieves, the better are the perks. Uber also provides personalized destination suggestions based on the history of the user and their frequently traveled destinations.

You can take a look at this Python Chatbot Project and build a simple chatbot application to understand better the techniques used for natural language processing. You can also practice the working of a demand forecasting model with this project using time series analysis. You can look at this project which uses time series forecasting and clustering on a dataset containing geospatial data for forecasting customer demand for ola rides.

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7) LinkedIn 

LinkedIn is the largest professional social networking site with nearly 800 million members in more than 200 countries worldwide. Almost 40% of the users access LinkedIn daily, clocking around 1 billion interactions per month. The data science team at LinkedIn works with this massive pool of data to generate insights to build strategies, apply algorithms and statistical inferences to optimize engineering solutions, and help the company achieve its goals. Here are some of the real world data science projects at LinkedIn:

i) LinkedIn Recruiter Implement Search Algorithms and Recommendation Systems

LinkedIn Recruiter helps recruiters build and manage a talent pool to optimize the chances of hiring candidates successfully. This sophisticated product works on search and recommendation engines. The LinkedIn recruiter handles complex queries and filters on a constantly growing large dataset. The results delivered have to be relevant and specific. The initial search model was based on linear regression but was eventually upgraded to Gradient Boosted decision trees to include non-linear correlations in the dataset. In addition to these models, the LinkedIn recruiter also uses the Generalized Linear Mix model to improve the results of prediction problems to give personalized results.

ii) Recommendation Systems Personalized for News Feed

The LinkedIn news feed is the heart and soul of the professional community. A member's newsfeed is a place to discover conversations among connections, career news, posts, suggestions, photos, and videos. Every time a member visits LinkedIn, machine learning algorithms identify the best exchanges to be displayed on the feed by sorting through posts and ranking the most relevant results on top. The algorithms help LinkedIn understand member preferences and help provide personalized news feeds. The algorithms used include logistic regression, gradient boosted decision trees and neural networks for recommendation systems.

iii) CNN's to Detect Inappropriate Content

To provide a professional space where people can trust and express themselves professionally in a safe community has been a critical goal at LinkedIn. LinkedIn has heavily invested in building solutions to detect fake accounts and abusive behavior on their platform. Any form of spam, harassment, inappropriate content is immediately flagged and taken down. These can range from profanity to advertisements for illegal services. LinkedIn uses a Convolutional neural networks based machine learning model. This classifier trains on a training dataset containing accounts labeled as either "inappropriate" or "appropriate." The inappropriate list consists of accounts having content from "blocklisted" phrases or words and a small portion of manually reviewed accounts reported by the user community.

Here is a Text Classification Project to help you understand NLP basics for text classification. You can find a news recommendation system dataset to help you build a personalized news recommender system. You can also use this dataset to build a classifier using logistic regression, Naive Bayes, or Neural networks to classify toxic comments.

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Pfizer is a multinational pharmaceutical company headquartered in New York, USA. One of the largest pharmaceutical companies globally known for developing a wide range of medicines and vaccines in disciplines like immunology, oncology, cardiology, and neurology. Pfizer became a household name in 2010 when it was the first to have a COVID-19 vaccine with FDA. In early November 2021, The CDC has approved the Pfizer vaccine for kids aged 5 to 11. Pfizer has been using machine learning and artificial intelligence to develop drugs and streamline trials, which played a massive role in developing and deploying the COVID-19 vaccine. Here are a few data analytics case studies by Pfizer :

i) Identifying Patients for Clinical Trials

Artificial intelligence and machine learning are used to streamline and optimize clinical trials to increase their efficiency. Natural language processing and exploratory data analysis of patient records can help identify suitable patients for clinical trials. These can help identify patients with distinct symptoms. These can help examine interactions of potential trial members' specific biomarkers, predict drug interactions and side effects which can help avoid complications. Pfizer's AI implementation helped rapidly identify signals within the noise of millions of data points across their 44,000-candidate COVID-19 clinical trial.

ii) Supply Chain and Manufacturing

Data science and machine learning techniques help pharmaceutical companies better forecast demand for vaccines and drugs and distribute them efficiently. Machine learning models can help identify efficient supply systems by automating and optimizing the production steps. These will help supply drugs customized to small pools of patients in specific gene pools. Pfizer uses Machine learning to predict the maintenance cost of equipment used. Predictive maintenance using AI is the next big step for Pharmaceutical companies to reduce costs.

iii) Drug Development

Computer simulations of proteins, and tests of their interactions, and yield analysis help researchers develop and test drugs more efficiently. In 2016 Watson Health and Pfizer announced a collaboration to utilize IBM Watson for Drug Discovery to help accelerate Pfizer's research in immuno-oncology, an approach to cancer treatment that uses the body's immune system to help fight cancer. Deep learning models have been used recently for bioactivity and synthesis prediction for drugs and vaccines in addition to molecular design. Deep learning has been a revolutionary technique for drug discovery as it factors everything from new applications of medications to possible toxic reactions which can save millions in drug trials.

You can create a Machine learning model to predict molecular activity to help design medicine using this dataset . You may build a CNN or a Deep neural network for this data analyst case study project.

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9) Shell Data Analyst Case Study Project

Shell is a global group of energy and petrochemical companies with over 80,000 employees in around 70 countries. Shell uses advanced technologies and innovations to help build a sustainable energy future. Shell is going through a significant transition as the world needs more and cleaner energy solutions to be a clean energy company by 2050. It requires substantial changes in the way in which energy is used. Digital technologies, including AI and Machine Learning, play an essential role in this transformation. These include efficient exploration and energy production, more reliable manufacturing, more nimble trading, and a personalized customer experience. Using AI in various phases of the organization will help achieve this goal and stay competitive in the market. Here are a few data analytics case studies in the petrochemical industry:

i) Precision Drilling

Shell is involved in the processing mining oil and gas supply, ranging from mining hydrocarbons to refining the fuel to retailing them to customers. Recently Shell has included reinforcement learning to control the drilling equipment used in mining. Reinforcement learning works on a reward-based system based on the outcome of the AI model. The algorithm is designed to guide the drills as they move through the surface, based on the historical data from drilling records. It includes information such as the size of drill bits, temperatures, pressures, and knowledge of the seismic activity. This model helps the human operator understand the environment better, leading to better and faster results will minor damage to machinery used. 

ii) Efficient Charging Terminals

Due to climate changes, governments have encouraged people to switch to electric vehicles to reduce carbon dioxide emissions. However, the lack of public charging terminals has deterred people from switching to electric cars. Shell uses AI to monitor and predict the demand for terminals to provide efficient supply. Multiple vehicles charging from a single terminal may create a considerable grid load, and predictions on demand can help make this process more efficient.

iii) Monitoring Service and Charging Stations

Another Shell initiative trialed in Thailand and Singapore is the use of computer vision cameras, which can think and understand to watch out for potentially hazardous activities like lighting cigarettes in the vicinity of the pumps while refueling. The model is built to process the content of the captured images and label and classify it. The algorithm can then alert the staff and hence reduce the risk of fires. You can further train the model to detect rash driving or thefts in the future.

Here is a project to help you understand multiclass image classification. You can use the Hourly Energy Consumption Dataset to build an energy consumption prediction model. You can use time series with XGBoost to develop your model.

10) Zomato Case Study on Data Analytics

Zomato was founded in 2010 and is currently one of the most well-known food tech companies. Zomato offers services like restaurant discovery, home delivery, online table reservation, online payments for dining, etc. Zomato partners with restaurants to provide tools to acquire more customers while also providing delivery services and easy procurement of ingredients and kitchen supplies. Currently, Zomato has over 2 lakh restaurant partners and around 1 lakh delivery partners. Zomato has closed over ten crore delivery orders as of date. Zomato uses ML and AI to boost their business growth, with the massive amount of data collected over the years from food orders and user consumption patterns. Here are a few examples of data analyst case study project developed by the data scientists at Zomato:

i) Personalized Recommendation System for Homepage

Zomato uses data analytics to create personalized homepages for its users. Zomato uses data science to provide order personalization, like giving recommendations to the customers for specific cuisines, locations, prices, brands, etc. Restaurant recommendations are made based on a customer's past purchases, browsing history, and what other similar customers in the vicinity are ordering. This personalized recommendation system has led to a 15% improvement in order conversions and click-through rates for Zomato. 

You can use the Restaurant Recommendation Dataset to build a restaurant recommendation system to predict what restaurants customers are most likely to order from, given the customer location, restaurant information, and customer order history.

ii) Analyzing Customer Sentiment

Zomato uses Natural language processing and Machine learning to understand customer sentiments using social media posts and customer reviews. These help the company gauge the inclination of its customer base towards the brand. Deep learning models analyze the sentiments of various brand mentions on social networking sites like Twitter, Instagram, Linked In, and Facebook. These analytics give insights to the company, which helps build the brand and understand the target audience.

iii) Predicting Food Preparation Time (FPT)

Food delivery time is an essential variable in the estimated delivery time of the order placed by the customer using Zomato. The food preparation time depends on numerous factors like the number of dishes ordered, time of the day, footfall in the restaurant, day of the week, etc. Accurate prediction of the food preparation time can help make a better prediction of the Estimated delivery time, which will help delivery partners less likely to breach it. Zomato uses a Bidirectional LSTM-based deep learning model that considers all these features and provides food preparation time for each order in real-time. 

Data scientists are companies' secret weapons when analyzing customer sentiments and behavior and leveraging it to drive conversion, loyalty, and profits. These 10 data science case studies projects with examples and solutions show you how various organizations use data science technologies to succeed and be at the top of their field! To summarize, Data Science has not only accelerated the performance of companies but has also made it possible to manage & sustain their performance with ease.

FAQs on Data Analysis Case Studies

A case study in data science is an in-depth analysis of a real-world problem using data-driven approaches. It involves collecting, cleaning, and analyzing data to extract insights and solve challenges, offering practical insights into how data science techniques can address complex issues across various industries.

To create a data science case study, identify a relevant problem, define objectives, and gather suitable data. Clean and preprocess data, perform exploratory data analysis, and apply appropriate algorithms for analysis. Summarize findings, visualize results, and provide actionable recommendations, showcasing the problem-solving potential of data science techniques.

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Top 10 real-world data science case studies.

Data Science Case Studies

Aditya Sharma

Aditya is a content writer with 5+ years of experience writing for various industries including Marketing, SaaS, B2B, IT, and Edtech among others. You can find him watching anime or playing games when he’s not writing.

Frequently Asked Questions

Real-world data science case studies differ significantly from academic examples. While academic exercises often feature clean, well-structured data and simplified scenarios, real-world projects tackle messy, diverse data sources with practical constraints and genuine business objectives. These case studies reflect the complexities data scientists face when translating data into actionable insights in the corporate world.

Real-world data science projects come with common challenges. Data quality issues, including missing or inaccurate data, can hinder analysis. Domain expertise gaps may result in misinterpretation of results. Resource constraints might limit project scope or access to necessary tools and talent. Ethical considerations, like privacy and bias, demand careful handling.

Lastly, as data and business needs evolve, data science projects must adapt and stay relevant, posing an ongoing challenge.

Real-world data science case studies play a crucial role in helping companies make informed decisions. By analyzing their own data, businesses gain valuable insights into customer behavior, market trends, and operational efficiencies.

These insights empower data-driven strategies, aiding in more effective resource allocation, product development, and marketing efforts. Ultimately, case studies bridge the gap between data science and business decision-making, enhancing a company's ability to thrive in a competitive landscape.

Key takeaways from these case studies for organizations include the importance of cultivating a data-driven culture that values evidence-based decision-making. Investing in robust data infrastructure is essential to support data initiatives. Collaborating closely between data scientists and domain experts ensures that insights align with business goals.

Finally, continuous monitoring and refinement of data solutions are critical for maintaining relevance and effectiveness in a dynamic business environment. Embracing these principles can lead to tangible benefits and sustainable success in real-world data science endeavors.

Data science is a powerful driver of innovation and problem-solving across diverse industries. By harnessing data, organizations can uncover hidden patterns, automate repetitive tasks, optimize operations, and make informed decisions.

In healthcare, for example, data-driven diagnostics and treatment plans improve patient outcomes. In finance, predictive analytics enhances risk management. In transportation, route optimization reduces costs and emissions. Data science empowers industries to innovate and solve complex challenges in ways that were previously unimaginable.

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20+ Data Science Case Study Interview Questions (with Solutions)

2023 Guide: 20+ Essential Data Science Case Study Interview Questions

Case studies are often the most challenging aspect of data science interview processes. They are crafted to resemble a company’s existing or previous projects, assessing a candidate’s ability to tackle prompts, convey their insights, and navigate obstacles.

To excel in data science case study interviews, practice is crucial. It will enable you to develop strategies for approaching case studies, asking the right questions to your interviewer, and providing responses that showcase your skills while adhering to time constraints.

The best way of doing this is by using a framework for answering case studies. For example, you could use the product metrics framework and the A/B testing framework to answer most case studies that come up in data science interviews.

There are four main types of data science case studies:

  • Product Case Studies - This type of case study tackles a specific product or feature offering, often tied to the interviewing company. Interviewers are generally looking for a sense of business sense geared towards product metrics.
  • Data Analytics Case Study Questions - Data analytics case studies ask you to propose possible metrics in order to investigate an analytics problem. Additionally, you must write a SQL query to pull your proposed metrics, and then perform analysis using the data you queried, just as you would do in the role.
  • Modeling and Machine Learning Case Studies - Modeling case studies are more varied and focus on assessing your intuition for building models around business problems.
  • Business Case Questions - Similar to product questions, business cases tackle issues or opportunities specific to the organization that is interviewing you. Often, candidates must assess the best option for a certain business plan being proposed, and formulate a process for solving the specific problem.

How Case Study Interviews Are Conducted

Oftentimes as an interviewee, you want to know the setting and format in which to expect the above questions to be asked. Unfortunately, this is company-specific: Some prefer real-time settings, where candidates actively work through a prompt after receiving it, while others offer some period of days (say, a week) before settling in for a presentation of your findings.

It is therefore important to have a system for answering these questions that will accommodate all possible formats, such that you are prepared for any set of circumstances (we provide such a framework below).

Why Are Case Study Questions Asked?

Case studies assess your thought process in answering data science questions. Specifically, interviewers want to see that you have the ability to think on your feet, and to work through real-world problems that likely do not have a right or wrong answer. Real-world case studies that are affecting businesses are not binary; there is no black-and-white, yes-or-no answer. This is why it is important that you can demonstrate decisiveness in your investigations, as well as show your capacity to consider impacts and topics from a variety of angles. Once you are in the role, you will be dealing directly with the ambiguity at the heart of decision-making.

Perhaps most importantly, case interviews assess your ability to effectively communicate your conclusions. On the job, data scientists exchange information across teams and divisions, so a significant part of the interviewer’s focus will be on how you process and explain your answer.

Quick tip: Because case questions in data science interviews tend to be product- and company-focused, it is extremely beneficial to research current projects and developments across different divisions , as these initiatives might end up as the case study topic.

How to Answer Data Science Case Study Questions (The Framework)

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There are four main steps to tackling case questions in Data Science interviews, regardless of the type: clarify, make assumptions, gather context, and provide data points and analysis.

Step 1: Clarify

Clarifying is used to gather more information . More often than not, these case studies are designed to be confusing and vague. There will be unorganized data intentionally supplemented with extraneous or omitted information, so it is the candidate’s responsibility to dig deeper, filter out bad information, and fill gaps. Interviewers will be observing how an applicant asks questions and reach their solution.

For example, with a product question, you might take into consideration:

  • What is the product?
  • How does the product work?
  • How does the product align with the business itself?

Step 2: Make Assumptions

When you have made sure that you have evaluated and understand the dataset, start investigating and discarding possible hypotheses. Developing insights on the product at this stage complements your ability to glean information from the dataset, and the exploration of your ideas is paramount to forming a successful hypothesis. You should be communicating your hypotheses with the interviewer, such that they can provide clarifying remarks on how the business views the product, and to help you discard unworkable lines of inquiry. If we continue to think about a product question, some important questions to evaluate and draw conclusions from include:

  • Who uses the product? Why?
  • What are the goals of the product?
  • How does the product interact with other services or goods the company offers?

The goal of this is to reduce the scope of the problem at hand, and ask the interviewer questions upfront that allow you to tackle the meat of the problem instead of focusing on less consequential edge cases.

Step 3: Propose a Solution

Now that a hypothesis is formed that has incorporated the dataset and an understanding of the business-related context, it is time to apply that knowledge in forming a solution. Remember, the hypothesis is simply a refined version of the problem that uses the data on hand as its basis to being solved. The solution you create can target this narrow problem, and you can have full faith that it is addressing the core of the case study question.

Keep in mind that there isn’t a single expected solution, and as such, there is a certain freedom here to determine the exact path for investigation.

Step 4: Provide Data Points and Analysis

Finally, providing data points and analysis in support of your solution involves choosing and prioritizing a main metric. As with all prior factors, this step must be tied back to the hypothesis and the main goal of the problem. From that foundation, it is important to trace through and analyze different examples– from the main metric–in order to validate the hypothesis.

Quick tip: Every case question tends to have multiple solutions. Therefore, you should absolutely consider and communicate any potential trade-offs of your chosen method. Be sure you are communicating the pros and cons of your approach.

Note: In some special cases, solutions will also be assessed on the ability to convey information in layman’s terms. Regardless of the structure, applicants should always be prepared to solve through the framework outlined above in order to answer the prompt.

The Role of Effective Communication

There have been multiple articles and discussions conducted by interviewers behind the Data Science Case Study portion, and they all boil down success in case studies to one main factor: effective communication.

All the analysis in the world will not help if interviewees cannot verbally work through and highlight their thought process within the case study. Again, interviewers are keyed at this stage of the hiring process to look for well-developed “soft-skills” and problem-solving capabilities. Demonstrating those traits is key to succeeding in this round.

To this end, the best advice possible would be to practice actively going through example case studies, such as those available in the Interview Query questions bank . Exploring different topics with a friend in an interview-like setting with cold recall (no Googling in between!) will be uncomfortable and awkward, but it will also help reveal weaknesses in fleshing out the investigation.

Don’t worry if the first few times are terrible! Developing a rhythm will help with gaining self-confidence as you become better at assessing and learning through these sessions.

Product Case Study Questions

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With product data science case questions , the interviewer wants to get an idea of your product sense intuition. Specifically, these questions assess your ability to identify which metrics should be proposed in order to understand a product.

1. How would you measure the success of private stories on Instagram, where only certain close friends can see the story?

Start by answering: What is the goal of the private story feature on Instagram? You can’t evaluate “success” without knowing what the initial objective of the product was, to begin with.

One specific goal of this feature would be to drive engagement. A private story could potentially increase interactions between users, and grow awareness of the feature.

Now, what types of metrics might you propose to assess user engagement? For a high-level overview, we could look at:

  • Average stories per user per day
  • Average Close Friends stories per user per day

However, we would also want to further bucket our users to see the effect that Close Friends stories have on user engagement. By bucketing users by age, date joined, or another metric, we could see how engagement is affected within certain populations, giving us insight on success that could be lost if looking at the overall population.

2. How would you measure the success of acquiring new users through a 30-day free trial at Netflix?

More context: Netflix is offering a promotion where users can enroll in a 30-day free trial. After 30 days, customers will automatically be charged based on their selected package. How would you measure acquisition success, and what metrics would you propose to measure the success of the free trial?

One way we can frame the concept specifically to this problem is to think about controllable inputs, external drivers, and then the observable output . Start with the major goals of Netflix:

  • Acquiring new users to their subscription plan.
  • Decreasing churn and increasing retention.

Looking at acquisition output metrics specifically, there are several top-level stats that we can look at, including:

  • Conversion rate percentage
  • Cost per free trial acquisition
  • Daily conversion rate

With these conversion metrics, we would also want to bucket users by cohort. This would help us see the percentage of free users who were acquired, as well as retention by cohort.

3. How would you measure the success of Facebook Groups?

Start by considering the key function of Facebook Groups . You could say that Groups are a way for users to connect with other users through a shared interest or real-life relationship. Therefore, the user’s goal is to experience a sense of community, which will also drive our business goal of increasing user engagement.

What general engagement metrics can we associate with this value? An objective metric like Groups monthly active users would help us see if Facebook Groups user base is increasing or decreasing. Plus, we could monitor metrics like posting, commenting, and sharing rates.

There are other products that Groups impact, however, specifically the Newsfeed. We need to consider Newsfeed quality and examine if updates from Groups clog up the content pipeline and if users prioritize those updates over other Newsfeed items. This evaluation will give us a better sense of if Groups actually contribute to higher engagement levels.

4. How would you analyze the effectiveness of a new LinkedIn chat feature that shows a “green dot” for active users?

Note: Given engineering constraints, the new feature is impossible to A/B test before release. When you approach case study questions, remember always to clarify any vague terms. In this case, “effectiveness” is very vague. To help you define that term, you would want first to consider what the goal is of adding a green dot to LinkedIn chat.

Data Science Product Case Study (LinkedIn InMail, Facebook Chat)

5. How would you diagnose why weekly active users are up 5%, but email notification open rates are down 2%?

What assumptions can you make about the relationship between weekly active users and email open rates? With a case question like this, you would want to first answer that line of inquiry before proceeding.

Hint: Open rate can decrease when its numerator decreases (fewer people open emails) or its denominator increases (more emails are sent overall). Taking these two factors into account, what are some hypotheses we can make about our decrease in the open rate compared to our increase in weekly active users?

Data Analytics Case Study Questions

Data analytics case studies ask you to dive into analytics problems. Typically these questions ask you to examine metrics trade-offs or investigate changes in metrics. In addition to proposing metrics, you also have to write SQL queries to generate the metrics, which is why they are sometimes referred to as SQL case study questions .

6. Using the provided data, generate some specific recommendations on how DoorDash can improve.

In this DoorDash analytics case study take-home question you are provided with the following dataset:

  • Customer order time
  • Restaurant order time
  • Driver arrives at restaurant time
  • Order delivered time
  • Customer ID
  • Amount of discount
  • Amount of tip

With a dataset like this, there are numerous recommendations you can make. A good place to start is by thinking about the DoorDash marketplace, which includes drivers, riders and merchants. How could you analyze the data to increase revenue, driver/user retention and engagement in that marketplace?

7. After implementing a notification change, the total number of unsubscribes increases. Write a SQL query to show how unsubscribes are affecting login rates over time.

This is a Twitter data science interview question , and let’s say you implemented this new feature using an A/B test. You are provided with two tables: events (which includes login, nologin and unsubscribe ) and variants (which includes control or variant ).

We are tasked with comparing multiple different variables at play here. There is the new notification system, along with its effect of creating more unsubscribes. We can also see how login rates compare for unsubscribes for each bucket of the A/B test.

Given that we want to measure two different changes, we know we have to use GROUP BY for the two variables: date and bucket variant. What comes next?

8. Write a query to disprove the hypothesis: Data scientists who switch jobs more often end up getting promoted faster.

More context: You are provided with a table of user experiences representing each person’s past work experiences and timelines.

This question requires a bit of creative problem-solving to understand how we can prove or disprove the hypothesis. The hypothesis is that a data scientist that ends up switching jobs more often gets promoted faster.

Therefore, in analyzing this dataset, we can prove this hypothesis by separating the data scientists into specific segments on how often they jump in their careers.

For example, if we looked at the number of job switches for data scientists that have been in their field for five years, we could prove the hypothesis that the number of data science managers increased as the number of career jumps also rose.

  • Never switched jobs: 10% are managers
  • Switched jobs once: 20% are managers
  • Switched jobs twice: 30% are managers
  • Switched jobs three times: 40% are managers

9. Write a SQL query to investigate the hypothesis: Click-through rate is dependent on search result rating.

More context: You are given a table with search results on Facebook, which includes query (search term), position (the search position), and rating (human rating from 1 to 5). Each row represents a single search and includes a column has_clicked that represents whether a user clicked or not.

This question requires us to formulaically do two things: create a metric that can analyze a problem that we face and then actually compute that metric.

Think about the data we want to display to prove or disprove the hypothesis. Our output metric is CTR (clickthrough rate). If CTR is high when search result ratings are high and CTR is low when the search result ratings are low, then our hypothesis is proven. However, if the opposite is true, CTR is low when the search result ratings are high, or there is no proven correlation between the two, then our hypothesis is not proven.

With that structure in mind, we can then look at the results split into different search rating buckets. If we measure the CTR for queries that all have results rated at 1 and then measure CTR for queries that have results rated at lower than 2, etc., we can measure to see if the increase in rating is correlated with an increase in CTR.

10. How would you help a supermarket chain determine which product categories should be prioritized in their inventory restructuring efforts?

You’re working as a Data Scientist in a local grocery chain’s data science team. The business team has decided to allocate store floor space by product category (e.g., electronics, sports and travel, food and beverages). Help the team understand which product categories to prioritize as well as answering questions such as how customer demographics affect sales, and how each city’s sales per product category differs.

Check out our Data Analytics Learning Path .

Modeling and Machine Learning Case Questions

Machine learning case questions assess your ability to build models to solve business problems. These questions can range from applying machine learning to solve a specific case scenario to assessing the validity of a hypothetical existing model . The modeling case study requires a candidate to evaluate and explain any certain part of the model building process.

11. Describe how you would build a model to predict Uber ETAs after a rider requests a ride.

Common machine learning case study problems like this are designed to explain how you would build a model. Many times this can be scoped down to specific parts of the model building process. Examining the example above, we could break it up into:

How would you evaluate the predictions of an Uber ETA model?

What features would you use to predict the Uber ETA for ride requests?

Our recommended framework breaks down a modeling and machine learning case study to individual steps in order to tackle each one thoroughly. In each full modeling case study, you will want to go over:

  • Data processing
  • Feature Selection
  • Model Selection
  • Cross Validation
  • Evaluation Metrics
  • Testing and Roll Out

12. How would you build a model that sends bank customers a text message when fraudulent transactions are detected?

Additionally, the customer can approve or deny the transaction via text response.

Let’s start out by understanding what kind of model would need to be built. We know that since we are working with fraud, there has to be a case where either a fraudulent transaction is or is not present .

Hint: This problem is a binary classification problem. Given the problem scenario, what considerations do we have to think about when first building this model? What would the bank fraud data look like?

13. How would you design the inputs and outputs for a model that detects potential bombs at a border crossing?

Additional questions. How would you test the model and measure its accuracy? Remember the equation for precision:

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Because we can not have high TrueNegatives, recall should be high when assessing the model.

14. Which model would you choose to predict Airbnb booking prices: Linear regression or random forest regression?

Start by answering this question: What are the main differences between linear regression and random forest?

Random forest regression is based on the ensemble machine learning technique of bagging . The two key concepts of random forests are:

  • Random sampling of training observations when building trees.
  • Random subsets of features for splitting nodes.

Random forest regressions also discretize continuous variables, since they are based on decision trees and can split categorical and continuous variables.

Linear regression, on the other hand, is the standard regression technique in which relationships are modeled using a linear predictor function, the most common example represented as y = Ax + B.

Let’s see how each model is applicable to Airbnb’s bookings. One thing we need to do in the interview is to understand more context around the problem of predicting bookings. To do so, we need to understand which features are present in our dataset.

We can assume the dataset will have features like:

  • Location features.
  • Seasonality.
  • Number of bedrooms and bathrooms.
  • Private room, shared, entire home, etc.
  • External demand (conferences, festivals, sporting events).

Which model would be the best fit for this feature set?

15. Using a binary classification model that pre-approves candidates for a loan, how would you give each rejected application a rejection reason?

More context: You do not have access to the feature weights. Start by thinking about the problem like this: How would the problem change if we had ten, one thousand, or ten thousand applicants that had gone through the loan qualification program?

Pretend that we have three people: Alice, Bob, and Candace that have all applied for a loan. Simplifying the financial lending loan model, let us assume the only features are the total number of credit cards , the dollar amount of current debt , and credit age . Here is a scenario:

Alice: 10 credit cards, 5 years of credit age, $\$20K$ in debt - **Bob:** 10 credit cards, 5 years of credit age, $\$15K$ in debt

Candace: 10 credit cards, 5 years of credit age, $\$10K$ in debt If Candace is approved, we can logically point to the fact that Candace’s $\$10K$ in debt swung the model to approve her for a loan. How did we reason this out?

If the sample size analyzed was instead thousands of people who had the same number of credit cards and credit age with varying levels of debt, we could figure out the model’s average loan acceptance rate for each numerical amount of current debt. Then we could plot these on a graph to model the y-value (average loan acceptance) versus the x-value (dollar amount of current debt). These graphs are called partial dependence plots.

Business Case Questions

In data science interviews, business case study questions task you with addressing problems as they relate to the business. You might be asked about topics like estimation and calculation, as well as applying problem-solving to a larger case. One tip: Be sure to read up on the company’s products and ventures before your interview to expose yourself to possible topics.

16. How would you estimate the average lifetime value of customers at a business that has existed for just over one year?

More context: You know that the product costs $\$100$ per month, averages 10% in monthly churn, and the average customer stays for 3.5 months. Remember that lifetime value is defined by the prediction of the net revenue attributed to the entire future relationship with all customers averaged. Therefore, $\$100$ * 3.5 = $\$350$… But is it that simple?

Because this company is so new, our average customer length (3.5 months) is biased from the short possible length of time that anyone could have been a customer (one year maximum). How would you then model out LTV knowing the churn rate and product cost?

17. How would you go about removing duplicate product names (e.g. iPhone X vs. Apple iPhone 10) in a massive database?

See the full solution for this Amazon business case question on YouTube:

data science case studies with solutions

18. What metrics would you monitor to know if a 50% discount promotion is a good idea for a ride-sharing company?

This question has no correct answer and is rather designed to test your reasoning and communication skills related to product/business cases. First, start by stating your assumptions. What are the goals of this promotion? It is likely that the goal of the discount is to grow revenue and increase retention. A few other assumptions you might make include:

  • The promotion will be applied uniformly across all users.
  • The 50% discount can only be used for a single ride.

How would we be able to evaluate this pricing strategy? An A/B test between the control group (no discount) and test group (discount) would allow us to evaluate Long-term revenue vs average cost of the promotion. Using these two metrics how could we measure if the promotion is a good idea?

19. A bank wants to create a new partner card, e.g. Whole Foods Chase credit card). How would you determine what the next partner card should be?

More context: Say you have access to all customer spending data. With this question, there are several approaches you can take. As your first step, think about the business reason for credit card partnerships: they help increase acquisition and customer retention.

One of the simplest solutions would be to sum all transactions grouped by merchants. This would identify the merchants who see the highest spending amounts. However, the one issue might be that some merchants have a high-spend value but low volume. How could we counteract this potential pitfall? Is the volume of transactions even an important factor in our credit card business? The more questions you ask, the more may spring to mind.

20. How would you assess the value of keeping a TV show on a streaming platform like Netflix?

Say that Netflix is working on a deal to renew the streaming rights for a show like The Office , which has been on Netflix for one year. Your job is to value the benefit of keeping the show on Netflix.

Start by trying to understand the reasons why Netflix would want to renew the show. Netflix mainly has three goals for what their content should help achieve:

  • Acquisition: To increase the number of subscribers.
  • Retention: To increase the retention of active subscribers and keep them on as paying members.
  • Revenue: To increase overall revenue.

One solution to value the benefit would be to estimate a lower and upper bound to understand the percentage of users that would be affected by The Office being removed. You could then run these percentages against your known acquisition and retention rates.

21. How would you determine which products are to be put on sale?

Let’s say you work at Amazon. It’s nearing Black Friday, and you are tasked with determining which products should be put on sale. You have access to historical pricing and purchasing data from items that have been on sale before. How would you determine what products should go on sale to best maximize profit during Black Friday?

To start with this question, aggregate data from previous years for products that have been on sale during Black Friday or similar events. You can then compare elements such as historical sales volume, inventory levels, and profit margins.

Learn More About Feature Changes

This course is designed teach you everything you need to know about feature changes:

More Data Science Interview Resources

Case studies are one of the most common types of data science interview questions . Practice with the data science course from Interview Query, which includes product and machine learning modules.

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Top 12 Data Science Case Studies: Across Various Industries

Home Blog Data Science Top 12 Data Science Case Studies: Across Various Industries

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Data science has become popular in the last few years due to its successful application in making business decisions. Data scientists have been using data science techniques to solve challenging real-world issues in healthcare, agriculture, manufacturing, automotive, and many more. For this purpose, a data enthusiast needs to stay updated with the latest technological advancements in AI . An excellent way to achieve this is through reading industry data science case studies. I recommend checking out Data Science With Python course syllabus to start your data science journey. In this discussion, I will present some case studies to you that contain detailed and systematic data analysis of people, objects, or entities focusing on multiple factors present in the dataset. Aspiring and practising data scientists can motivate themselves to learn more about the sector, an alternative way of thinking, or methods to improve their organization based on comparable experiences. Almost every industry uses data science in some way. You can learn more about data science fundamentals in this data science course content . From my standpoint, data scientists may use it to spot fraudulent conduct in insurance claims. Automotive data scientists may use it to improve self-driving cars. In contrast, e-commerce data scientists can use it to add more personalization for their consumers—the possibilities are unlimited and unexplored. Let’s look at the top eight data science case studies in this article so you can understand how businesses from many sectors have benefitted from data science to boost productivity, revenues, and more. Read on to explore more or use the following links to go straight to the case study of your choice.

data science case studies with solutions

Examples of Data Science Case Studies

  • Hospitality:  Airbnb focuses on growth by  analyzing  customer voice using data science.  Qantas uses predictive analytics to mitigate losses  
  • Healthcare:  Novo Nordisk  is  Driving innovation with NLP.  AstraZeneca harnesses data for innovation in medicine  
  • Covid 19:  Johnson and Johnson use s  d ata science  to fight the Pandemic  
  • E-commerce:  Amazon uses data science to personalize shop p ing experiences and improve customer satisfaction  
  • Supply chain management :  UPS optimizes supp l y chain with big data analytics
  • Meteorology:  IMD leveraged data science to achieve a rec o rd 1.2m evacuation before cyclone ''Fani''  
  • Entertainment Industry:  Netflix  u ses data science to personalize the content and improve recommendations.  Spotify uses big   data to deliver a rich user experience for online music streaming  
  • Banking and Finance:  HDFC utilizes Big  D ata Analytics to increase income and enhance  the  banking experience  

Top 8 Data Science Case Studies  [For Various Industries]

1. data science in hospitality industry.

In the hospitality sector, data analytics assists hotels in better pricing strategies, customer analysis, brand marketing , tracking market trends, and many more.

Airbnb focuses on growth by analyzing customer voice using data science.  A famous example in this sector is the unicorn '' Airbnb '', a startup that focussed on data science early to grow and adapt to the market faster. This company witnessed a 43000 percent hypergrowth in as little as five years using data science. They included data science techniques to process the data, translate this data for better understanding the voice of the customer, and use the insights for decision making. They also scaled the approach to cover all aspects of the organization. Airbnb uses statistics to analyze and aggregate individual experiences to establish trends throughout the community. These analyzed trends using data science techniques impact their business choices while helping them grow further.  

Travel industry and data science

Predictive analytics benefits many parameters in the travel industry. These companies can use recommendation engines with data science to achieve higher personalization and improved user interactions. They can study and cross-sell products by recommending relevant products to drive sales and increase revenue. Data science is also employed in analyzing social media posts for sentiment analysis, bringing invaluable travel-related insights. Whether these views are positive, negative, or neutral can help these agencies understand the user demographics, the expected experiences by their target audiences, and so on. These insights are essential for developing aggressive pricing strategies to draw customers and provide better customization to customers in the travel packages and allied services. Travel agencies like Expedia and Booking.com use predictive analytics to create personalized recommendations, product development, and effective marketing of their products. Not just travel agencies but airlines also benefit from the same approach. Airlines frequently face losses due to flight cancellations, disruptions, and delays. Data science helps them identify patterns and predict possible bottlenecks, thereby effectively mitigating the losses and improving the overall customer traveling experience.  

How Qantas uses predictive analytics to mitigate losses  

Qantas , one of Australia's largest airlines, leverages data science to reduce losses caused due to flight delays, disruptions, and cancellations. They also use it to provide a better traveling experience for their customers by reducing the number and length of delays caused due to huge air traffic, weather conditions, or difficulties arising in operations. Back in 2016, when heavy storms badly struck Australia's east coast, only 15 out of 436 Qantas flights were cancelled due to their predictive analytics-based system against their competitor Virgin Australia, which witnessed 70 cancelled flights out of 320.  

2. Data Science in Healthcare

The  Healthcare sector  is immensely benefiting from the advancements in AI. Data science, especially in medical imaging, has been helping healthcare professionals come up with better diagnoses and effective treatments for patients. Similarly, several advanced healthcare analytics tools have been developed to generate clinical insights for improving patient care. These tools also assist in defining personalized medications for patients reducing operating costs for clinics and hospitals. Apart from medical imaging or computer vision,  Natural Language Processing (NLP)  is frequently used in the healthcare domain to study the published textual research data.     

A. Pharmaceutical

Driving innovation with NLP: Novo Nordisk.  Novo Nordisk  uses the Linguamatics NLP platform from internal and external data sources for text mining purposes that include scientific abstracts, patents, grants, news, tech transfer offices from universities worldwide, and more. These NLP queries run across sources for the key therapeutic areas of interest to the Novo Nordisk R&D community. Several NLP algorithms have been developed for the topics of safety, efficacy, randomized controlled trials, patient populations, dosing, and devices. Novo Nordisk employs a data pipeline to capitalize the tools' success on real-world data and uses interactive dashboards and cloud services to visualize this standardized structured information from the queries for exploring commercial effectiveness, market situations, potential, and gaps in the product documentation. Through data science, they are able to automate the process of generating insights, save time and provide better insights for evidence-based decision making.  

How AstraZeneca harnesses data for innovation in medicine.  AstraZeneca  is a globally known biotech company that leverages data using AI technology to discover and deliver newer effective medicines faster. Within their R&D teams, they are using AI to decode the big data to understand better diseases like cancer, respiratory disease, and heart, kidney, and metabolic diseases to be effectively treated. Using data science, they can identify new targets for innovative medications. In 2021, they selected the first two AI-generated drug targets collaborating with BenevolentAI in Chronic Kidney Disease and Idiopathic Pulmonary Fibrosis.   

Data science is also helping AstraZeneca redesign better clinical trials, achieve personalized medication strategies, and innovate the process of developing new medicines. Their Center for Genomics Research uses  data science and AI  to analyze around two million genomes by 2026. Apart from this, they are training their AI systems to check these images for disease and biomarkers for effective medicines for imaging purposes. This approach helps them analyze samples accurately and more effortlessly. Moreover, it can cut the analysis time by around 30%.   

AstraZeneca also utilizes AI and machine learning to optimize the process at different stages and minimize the overall time for the clinical trials by analyzing the clinical trial data. Summing up, they use data science to design smarter clinical trials, develop innovative medicines, improve drug development and patient care strategies, and many more.

C. Wearable Technology  

Wearable technology is a multi-billion-dollar industry. With an increasing awareness about fitness and nutrition, more individuals now prefer using fitness wearables to track their routines and lifestyle choices.  

Fitness wearables are convenient to use, assist users in tracking their health, and encourage them to lead a healthier lifestyle. The medical devices in this domain are beneficial since they help monitor the patient's condition and communicate in an emergency situation. The regularly used fitness trackers and smartwatches from renowned companies like Garmin, Apple, FitBit, etc., continuously collect physiological data of the individuals wearing them. These wearable providers offer user-friendly dashboards to their customers for analyzing and tracking progress in their fitness journey.

3. Covid 19 and Data Science

In the past two years of the Pandemic, the power of data science has been more evident than ever. Different  pharmaceutical companies  across the globe could synthesize Covid 19 vaccines by analyzing the data to understand the trends and patterns of the outbreak. Data science made it possible to track the virus in real-time, predict patterns, devise effective strategies to fight the Pandemic, and many more.  

How Johnson and Johnson uses data science to fight the Pandemic   

The  data science team  at  Johnson and Johnson  leverages real-time data to track the spread of the virus. They built a global surveillance dashboard (granulated to county level) that helps them track the Pandemic's progress, predict potential hotspots of the virus, and narrow down the likely place where they should test its investigational COVID-19 vaccine candidate. The team works with in-country experts to determine whether official numbers are accurate and find the most valid information about case numbers, hospitalizations, mortality and testing rates, social compliance, and local policies to populate this dashboard. The team also studies the data to build models that help the company identify groups of individuals at risk of getting affected by the virus and explore effective treatments to improve patient outcomes.

4. Data Science in E-commerce  

In the  e-commerce sector , big data analytics can assist in customer analysis, reduce operational costs, forecast trends for better sales, provide personalized shopping experiences to customers, and many more.  

Amazon uses data science to personalize shopping experiences and improve customer satisfaction.  Amazon  is a globally leading eCommerce platform that offers a wide range of online shopping services. Due to this, Amazon generates a massive amount of data that can be leveraged to understand consumer behavior and generate insights on competitors' strategies. Amazon uses its data to provide recommendations to its users on different products and services. With this approach, Amazon is able to persuade its consumers into buying and making additional sales. This approach works well for Amazon as it earns 35% of the revenue yearly with this technique. Additionally, Amazon collects consumer data for faster order tracking and better deliveries.     

Similarly, Amazon's virtual assistant, Alexa, can converse in different languages; uses speakers and a   camera to interact with the users. Amazon utilizes the audio commands from users to improve Alexa and deliver a better user experience. 

5. Data Science in Supply Chain Management

Predictive analytics and big data are driving innovation in the Supply chain domain. They offer greater visibility into the company operations, reduce costs and overheads, forecasting demands, predictive maintenance, product pricing, minimize supply chain interruptions, route optimization, fleet management , drive better performance, and more.     

Optimizing supply chain with big data analytics: UPS

UPS  is a renowned package delivery and supply chain management company. With thousands of packages being delivered every day, on average, a UPS driver makes about 100 deliveries each business day. On-time and safe package delivery are crucial to UPS's success. Hence, UPS offers an optimized navigation tool ''ORION'' (On-Road Integrated Optimization and Navigation), which uses highly advanced big data processing algorithms. This tool for UPS drivers provides route optimization concerning fuel, distance, and time. UPS utilizes supply chain data analysis in all aspects of its shipping process. Data about packages and deliveries are captured through radars and sensors. The deliveries and routes are optimized using big data systems. Overall, this approach has helped UPS save 1.6 million gallons of gasoline in transportation every year, significantly reducing delivery costs.    

6. Data Science in Meteorology

Weather prediction is an interesting  application of data science . Businesses like aviation, agriculture and farming, construction, consumer goods, sporting events, and many more are dependent on climatic conditions. The success of these businesses is closely tied to the weather, as decisions are made after considering the weather predictions from the meteorological department.   

Besides, weather forecasts are extremely helpful for individuals to manage their allergic conditions. One crucial application of weather forecasting is natural disaster prediction and risk management.  

Weather forecasts begin with a large amount of data collection related to the current environmental conditions (wind speed, temperature, humidity, clouds captured at a specific location and time) using sensors on IoT (Internet of Things) devices and satellite imagery. This gathered data is then analyzed using the understanding of atmospheric processes, and machine learning models are built to make predictions on upcoming weather conditions like rainfall or snow prediction. Although data science cannot help avoid natural calamities like floods, hurricanes, or forest fires. Tracking these natural phenomena well ahead of their arrival is beneficial. Such predictions allow governments sufficient time to take necessary steps and measures to ensure the safety of the population.  

IMD leveraged data science to achieve a record 1.2m evacuation before cyclone ''Fani''   

Most  d ata scientist’s responsibilities  rely on satellite images to make short-term forecasts, decide whether a forecast is correct, and validate models. Machine Learning is also used for pattern matching in this case. It can forecast future weather conditions if it recognizes a past pattern. When employing dependable equipment, sensor data is helpful to produce local forecasts about actual weather models. IMD used satellite pictures to study the low-pressure zones forming off the Odisha coast (India). In April 2019, thirteen days before cyclone ''Fani'' reached the area,  IMD  (India Meteorological Department) warned that a massive storm was underway, and the authorities began preparing for safety measures.  

It was one of the most powerful cyclones to strike India in the recent 20 years, and a record 1.2 million people were evacuated in less than 48 hours, thanks to the power of data science.   

7. Data Science in the Entertainment Industry

Due to the Pandemic, demand for OTT (Over-the-top) media platforms has grown significantly. People prefer watching movies and web series or listening to the music of their choice at leisure in the convenience of their homes. This sudden growth in demand has given rise to stiff competition. Every platform now uses data analytics in different capacities to provide better-personalized recommendations to its subscribers and improve user experience.   

How Netflix uses data science to personalize the content and improve recommendations  

Netflix  is an extremely popular internet television platform with streamable content offered in several languages and caters to various audiences. In 2006, when Netflix entered this media streaming market, they were interested in increasing the efficiency of their existing ''Cinematch'' platform by 10% and hence, offered a prize of $1 million to the winning team. This approach was successful as they found a solution developed by the BellKor team at the end of the competition that increased prediction accuracy by 10.06%. Over 200 work hours and an ensemble of 107 algorithms provided this result. These winning algorithms are now a part of the Netflix recommendation system.  

Netflix also employs Ranking Algorithms to generate personalized recommendations of movies and TV Shows appealing to its users.   

Spotify uses big data to deliver a rich user experience for online music streaming  

Personalized online music streaming is another area where data science is being used.  Spotify  is a well-known on-demand music service provider launched in 2008, which effectively leveraged big data to create personalized experiences for each user. It is a huge platform with more than 24 million subscribers and hosts a database of nearly 20million songs; they use the big data to offer a rich experience to its users. Spotify uses this big data and various algorithms to train machine learning models to provide personalized content. Spotify offers a "Discover Weekly" feature that generates a personalized playlist of fresh unheard songs matching the user's taste every week. Using the Spotify "Wrapped" feature, users get an overview of their most favorite or frequently listened songs during the entire year in December. Spotify also leverages the data to run targeted ads to grow its business. Thus, Spotify utilizes the user data, which is big data and some external data, to deliver a high-quality user experience.  

8. Data Science in Banking and Finance

Data science is extremely valuable in the Banking and  Finance industry . Several high priority aspects of Banking and Finance like credit risk modeling (possibility of repayment of a loan), fraud detection (detection of malicious or irregularities in transactional patterns using machine learning), identifying customer lifetime value (prediction of bank performance based on existing and potential customers), customer segmentation (customer profiling based on behavior and characteristics for personalization of offers and services). Finally, data science is also used in real-time predictive analytics (computational techniques to predict future events).    

How HDFC utilizes Big Data Analytics to increase revenues and enhance the banking experience    

One of the major private banks in India,  HDFC Bank , was an early adopter of AI. It started with Big Data analytics in 2004, intending to grow its revenue and understand its customers and markets better than its competitors. Back then, they were trendsetters by setting up an enterprise data warehouse in the bank to be able to track the differentiation to be given to customers based on their relationship value with HDFC Bank. Data science and analytics have been crucial in helping HDFC bank segregate its customers and offer customized personal or commercial banking services. The analytics engine and SaaS use have been assisting the HDFC bank in cross-selling relevant offers to its customers. Apart from the regular fraud prevention, it assists in keeping track of customer credit histories and has also been the reason for the speedy loan approvals offered by the bank.  

9. Data Science in Urban Planning and Smart Cities  

Data Science can help the dream of smart cities come true! Everything, from traffic flow to energy usage, can get optimized using data science techniques. You can use the data fetched from multiple sources to understand trends and plan urban living in a sorted manner.  

The significant data science case study is traffic management in Pune city. The city controls and modifies its traffic signals dynamically, tracking the traffic flow. Real-time data gets fetched from the signals through cameras or sensors installed. Based on this information, they do the traffic management. With this proactive approach, the traffic and congestion situation in the city gets managed, and the traffic flow becomes sorted. A similar case study is from Bhubaneswar, where the municipality has platforms for the people to give suggestions and actively participate in decision-making. The government goes through all the inputs provided before making any decisions, making rules or arranging things that their residents actually need.  

10. Data Science in Agricultural Yield Prediction   

Have you ever wondered how helpful it can be if you can predict your agricultural yield? That is exactly what data science is helping farmers with. They can get information about the number of crops they can produce in a given area based on different environmental factors and soil types. Using this information, the farmers can make informed decisions about their yield and benefit the buyers and themselves in multiple ways.  

Data Science in Agricultural Yield Prediction

Farmers across the globe and overseas use various data science techniques to understand multiple aspects of their farms and crops. A famous example of data science in the agricultural industry is the work done by Farmers Edge. It is a company in Canada that takes real-time images of farms across the globe and combines them with related data. The farmers use this data to make decisions relevant to their yield and improve their produce. Similarly, farmers in countries like Ireland use satellite-based information to ditch traditional methods and multiply their yield strategically.  

11. Data Science in the Transportation Industry   

Transportation keeps the world moving around. People and goods commute from one place to another for various purposes, and it is fair to say that the world will come to a standstill without efficient transportation. That is why it is crucial to keep the transportation industry in the most smoothly working pattern, and data science helps a lot in this. In the realm of technological progress, various devices such as traffic sensors, monitoring display systems, mobility management devices, and numerous others have emerged.  

Many cities have already adapted to the multi-modal transportation system. They use GPS trackers, geo-locations and CCTV cameras to monitor and manage their transportation system. Uber is the perfect case study to understand the use of data science in the transportation industry. They optimize their ride-sharing feature and track the delivery routes through data analysis. Their data science approach enabled them to serve more than 100 million users, making transportation easy and convenient. Moreover, they also use the data they fetch from users daily to offer cost-effective and quickly available rides.  

12. Data Science in the Environmental Industry    

Increasing pollution, global warming, climate changes and other poor environmental impacts have forced the world to pay attention to environmental industry. Multiple initiatives are being taken across the globe to preserve the environment and make the world a better place. Though the industry recognition and the efforts are in the initial stages, the impact is significant, and the growth is fast.  

The popular use of data science in the environmental industry is by NASA and other research organizations worldwide. NASA gets data related to the current climate conditions, and this data gets used to create remedial policies that can make a difference. Another way in which data science is actually helping researchers is they can predict natural disasters well before time and save or at least reduce the potential damage considerably. A similar case study is with the World Wildlife Fund. They use data science to track data related to deforestation and help reduce the illegal cutting of trees. Hence, it helps preserve the environment.  

Where to Find Full Data Science Case Studies?  

Data science is a highly evolving domain with many practical applications and a huge open community. Hence, the best way to keep updated with the latest trends in this domain is by reading case studies and technical articles. Usually, companies share their success stories of how data science helped them achieve their goals to showcase their potential and benefit the greater good. Such case studies are available online on the respective company websites and dedicated technology forums like Towards Data Science or Medium.  

Additionally, we can get some practical examples in recently published research papers and textbooks in data science.  

What Are the Skills Required for Data Scientists?  

Data scientists play an important role in the data science process as they are the ones who work on the data end to end. To be able to work on a data science case study, there are several skills required for data scientists like a good grasp of the fundamentals of data science, deep knowledge of statistics, excellent programming skills in Python or R, exposure to data manipulation and data analysis, ability to generate creative and compelling data visualizations, good knowledge of big data, machine learning and deep learning concepts for model building & deployment. Apart from these technical skills, data scientists also need to be good storytellers and should have an analytical mind with strong communication skills.    

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Conclusion  

These were some interesting  data science case studies  across different industries. There are many more domains where data science has exciting applications, like in the Education domain, where data can be utilized to monitor student and instructor performance, develop an innovative curriculum that is in sync with the industry expectations, etc.   

Almost all the companies looking to leverage the power of big data begin with a swot analysis to narrow down the problems they intend to solve with data science. Further, they need to assess their competitors to develop relevant data science tools and strategies to address the challenging issue. This approach allows them to differentiate themselves from their competitors and offer something unique to their customers.  

With data science, the companies have become smarter and more data-driven to bring about tremendous growth. Moreover, data science has made these organizations more sustainable. Thus, the utility of data science in several sectors is clearly visible, a lot is left to be explored, and more is yet to come. Nonetheless, data science will continue to boost the performance of organizations in this age of big data.  

Frequently Asked Questions (FAQs)

A case study in data science requires a systematic and organized approach for solving the problem. Generally, four main steps are needed to tackle every data science case study: 

  • Defining the problem statement and strategy to solve it  
  • Gather and pre-process the data by making relevant assumptions  
  • Select tool and appropriate algorithms to build machine learning /deep learning models 
  • Make predictions, accept the solutions based on evaluation metrics, and improve the model if necessary. 

Getting data for a case study starts with a reasonable understanding of the problem. This gives us clarity about what we expect the dataset to include. Finding relevant data for a case study requires some effort. Although it is possible to collect relevant data using traditional techniques like surveys and questionnaires, we can also find good quality data sets online on different platforms like Kaggle, UCI Machine Learning repository, Azure open data sets, Government open datasets, Google Public Datasets, Data World and so on.  

Data science projects involve multiple steps to process the data and bring valuable insights. A data science project includes different steps - defining the problem statement, gathering relevant data required to solve the problem, data pre-processing, data exploration & data analysis, algorithm selection, model building, model prediction, model optimization, and communicating the results through dashboards and reports.  

Profile

Devashree Madhugiri

Devashree holds an M.Eng degree in Information Technology from Germany and a background in Data Science. She likes working with statistics and discovering hidden insights in varied datasets to create stunning dashboards. She enjoys sharing her knowledge in AI by writing technical articles on various technological platforms. She loves traveling, reading fiction, solving Sudoku puzzles, and participating in coding competitions in her leisure time.

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6 of my favorite case studies in Data Science!

Data scientists are numbers people. They have a deep understanding of statistics and algorithms, programming and hacking, and communication skills. Data science is about applying these three skill sets in a disciplined and systematic manner, with the goal of improving an aspect of the business. That’s the data science process . In order to stay abreast of industry trends, data scientists often turn to case studies. Reviewing these is a helpful way for both aspiring and working data scientists to challenge themselves and learn more about a particular field, a different way of thinking, or ways to better their own company based on similar experiences. If you’re not familiar with case studies , they’ve been described as “an intensive, systematic investigation of a single individual, group, community or some other unit in which the researcher examines in-depth data relating to several variables.” Data science is used by pretty much every industry out there. Insurance claims analysts can use data science to identify fraudulent behavior, e-commerce data scientists can build personalized experiences for their customers, music streaming companies can use it to create different genres of playlists—the possibilities are endless. Allow us to share a few of our favorite data science case studies with you so you can see first hand how companies across a variety of industries leveraged big data to drive productivity, profits, and more.

6 case studies in Data Science

  • How Airbnb characterizes data science
  • How data science is involved in decision-making at Airbnb
  • How Airbnb has scaled its data science efforts across all aspects of the company

Airbnb says that “we’re at a point where our infrastructure is stable, our tools are sophisticated, and our warehouse is clean and reliable. We’re ready to take on exciting new problems.” 3. Spotify’s “This Is” Playlists: The Ultimate Song Analysis For 50 Mainstream Artists If you’re a music lover, you’ve probably used Spotify at least once. If you’re a regular user, you’ve likely taken note of their personalized playlists and been impressed at how well the songs catered to your music preferences. But have you ever thought about how Spotify categorizes their music? You can thank their data science teams for that. The goal of the “This Is” case study is to analyze the music of various Spotify artists, segment the styles, and categorize them into by loudness, danceability, energy, and more. To start, a data scientist looked at Spotify’s API, which collects and provides data from Spotify’s music catalog. Once the data researcher accessed the data from Spotify’s API, he:

  • Processed the data to extract audio features for each artist
  • Visualized the data using D3.js.
  • Applied k-means clustering to separate the artists into different groups
  • Analyzed each feature for all the artists

Want a sneak peek at the results? James Arthur and Post Malone are in the same cluster, Kendrick Lamar is the “fastest” artist, and Marshmello beat Martin Garrix in the energy category. 4. A Leading Online Travel Agency Increases Revenues by 16 Percent with Actionable Analytics One of the largest online travel agencies in the world generated the majority of its revenue through its website and directed most of its resources there, but its clients were still using offline channels such as faxes and phone calls to ask questions. The agency brought in WNS, a travel-focused business process management company, to help it determine how to rethink and redesign its roadmap to capture missed revenue opportunities. WNS determined that the agency lacked an adequate offline strategy, which resulted in a dip in revenue and market share. After a deep dive into customer segments, the performance of offline sales agents, ideal hours for sales agents, and more, WNS was able to help the agency increase offline revenue by 16 percent and increase conversion rates by 21 percent. 5. How Mint.com Grew from Zero to 1 Million Users Mint.com is a free personal finance management service that asks users to input their personal spending data to generate insights about where their money goes. When Noah Kagan joined Mint.com as its marketing director, his goal was to find 100,000 new members in just six months. He didn’t just meet that goal. He destroyed it, generating one million members. How did he do it? Kagan says his success was two-fold. This first part was having a product he believed in. The second he attributes to “reverse engineering marketing.” “The key focal point to this strategy is to work backward,” Kagan explained. “Instead of starting with an intimidating zero playing on your mind, start at the solution and map your plan back from there.” He went on: “Think of it as a road trip. You start with a set destination in mind and then plan your route there. You don’t get in your car and start driving without in the hope that you magically end up where you wanted to be.” 6. Netflix: Using Big Data to Drive Big Engagement One of the best ways to explain the benefits of data science to people who don’t quite grasp the industry is by using Netflix-focused examples. Yes, Netflix is the largest internet-television network in the world. But what most people don’t realize is that, at its core, Netflix is a customer-focused, data-driven business. Founded in 1997 as a mail-order DVD company, it now boasts more than 53 million members in approximately 50 countries. If you watch The Fast and The Furious on Friday night, Netflix will likely serve up a Mark Wahlberg movie among your personalized recommendations for Saturday night. This is due to data science. But did you know that the company also uses its data insights to inform the way it buys, licenses, and creates new content? House of Cards and Orange is the New Black are two examples of how the company leveraged big data to understand its subscribers and cater to their needs. The company’s most-watched shows are generated from recommendations, which in turn foster consumer engagement and loyalty. This is why the company is constantly working on its recommendation engines. The Netflix story is a perfect case study for those who require engaged audiences in order to survive. In summary, data scientists are companies’ secret weapons when it comes to understanding customer behavior and levering it to drive conversion, loyalty, and profits. These six data science case studies show you how a variety of organizations—from a nature conservation group to a finance company to a media company—leveraged their big data to not only survive but to beat out the competition.

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Data Science Solutions: Applications and Use Cases

Data Science is a broad field with many potential applications. It’s not just about analyzing data and modeling algorithms, but it also reinvents the way businesses operate and how different departments interact. Data scientists solve complex problems every day, leveraging a variety of Data Science solutions to tackle issues like processing unstructured data, finding patterns […]

data science solutions

Data Science is a broad field with many potential applications. It’s not just about analyzing data and modeling algorithms, but it also reinvents the way businesses operate and how different departments interact. Data scientists solve complex problems every day, leveraging a variety of Data Science solutions to tackle issues like processing unstructured data, finding patterns in large datasets, and building recommendation engines using advanced statistical methods, artificial intelligence, and machine learning techniques. 

data science case studies with solutions

Data Science helps analyze and extract patterns from corporate data, so these patterns can be organized to guide corporate decisions. Data analysis using Data Science techniques helps companies to figure out which trends are the best fit for businesses during various parts of the year. 

Through data patterns, Data Science professionals can use tools and techniques to forecast future customer needs toward a specific product or service.  Data Science and businesses  can work together closely in understanding consumer preferences across a wide range of items and running better marketing campaigns. 

To enhance the scope of  predictive analytics , Data Science now employs other advanced technologies such as machine learning and deep learning to improve decision-making and create better models for predicting financial risks, customer behaviors, or market trends.

Data Science helps with making  future-proofing decisions,  supply chain predictions, understanding market trends, planning better pricing for products, consideration of automation for various data-driven tasks, and so on.

For example, in sales and marketing, Data Science is mainly used to predict markets, determine new customer segments, optimize pricing structures, and analyze the customer portfolio. Businesses frequently use sentiment analysis and behavior analytics to determine purchase and usage patterns, and to understand how people view products and services. Some businesses like Lowes, Home Depot, or Netflix use “hyper-personalization” techniques to match offers to customers accurately via their recommendation engines. 

E-commerce companies use recommendation engines, pricing algorithms, customer predictive segmentation, personalized product image searching, and artificially intelligent chat bots to offer transformational customer experience. 

In recent times,  deep learning , through its use of “artificial neural networks,” has empowered data scientists to perform unstructured data analytics, such as image recognition, object categorizing, and sound mapping.  

Data Science Solutions by Industry Applications

Now let’s take a look at how Data Science is powering the industry sectors  with its cross-disciplinary platforms and tools:

Data Science Solutions in Banking:  Banking and financial sectors are highly dependent on Data Science solutions powered with big data tools for risk analytics, risk management, KYC, and fraud mitigation. Large banks, hedge funds, stock exchanges, and other financial institutions use advanced Data Science (powered by big data, AI, ML) for trading analytics, pre-trade decision-support analytics, sentiment measurements, predictive analytics, and more. 

Data Science Solutions in Marketing:  Marketing departments often use Data Science to build recommendation systems and to analyze customer behavior. When we talk about Data Science in marketing, we are primarily concerned with what we call “retail marketing.” The retail marketing process involves analyzing customer data to inform business decisions and drive revenue. Common data used in retail marketing include customer data, product data, sales data, and competitor data. Customer transactional data is used extensively in AI-powered  data analytics systems  for increased sales and providing excellent marketing services. Chatbot analytics and sales representative response data are used together to improve sales efficiency. 

The retailer can use this data to build customer-targeted marketing campaigns, optimize prices based on demand, and decide on product assortment. The retail marketing process is rarely automated; it involves making business decisions based on the data. Data scientists working in retail marketing are primarily concerned with deriving insights from the data and applying statistical and machine learning methods to inform these decisions.

Data Science Solutions in Finance and Trading:  Finance departments use Data Science to build trading algorithms, manage risk, and improve compliance. A  data scientist  working in finance will primarily use data about the financial markets. This includes data about the companies whose stocks are traded on the market, the trading activity of the investors, and the stock prices. The financial data is unstructured and messy; it’s collected from different sources using different formats. The data scientist’s first task, therefore, is to process the data and convert it into a structured format. This is necessary for building algorithms and other models. For example, the data scientist might build a trading algorithm that exploits the market inefficiencies and generates profits for the company.

Data Science Solutions in Human Resources:  HR departments use Data Science to hire the best talent, manage employee data, and predict employee performance. The data scientist working in HR will primarily use employee data collected from different sources. This data could be structured or unstructured depending on how it’s collected. The most common source is an HR database such as Workday. The data scientist’s first task is to process the data and clean it. This is necessary for insights from the data. The data scientist might use methods like  machine learning  to predict the employee’s performance. This can be done by training the algorithm on historical employee data and the features it contains. For example, the data scientist might build a model that predicts employee performance using historical data. 

Data Science in Logistics and Warehousing:  Logistics and operations departments  use Data Science to manage supply chains and predict demand. The data scientist working in logistics and warehousing will primarily use data about customer orders, inventory, and product prices. The data scientist will use data from sensors and IoT devices deployed in the supply chain to track the product’s journey. The data scientist might use methods like machine learning to predict demand.  

Data Science Solutions in Customer Service:  Customer service departments use Data Science to answer customer queries, manage tickets, and improve the end-to-end customer experience. The data scientist working in customer service will primarily use data about customer tickets, customers, and the support team. The most common source is the ticket management system. In this case, the data scientist might use methods like machine learning to predict when the customer will stop engaging with the brand. This can be done by training the algorithm on historical customer data. For example, using historical data, the data scientist might build a model that predicts when a customer will stop engaging with the brand.

Big Data with Data Science Solutions Use Cases

While Data Science solutions can be used to get insights into behaviors and processes, big data analytics indicates the convergence of several cutting-edge technologies working together to help enterprise organizations extract better value from the data that they have.

In biomedical research and health, advanced Data Science and big data analytics techniques are used for increasing online revenue, reducing customer complaints, and enhancing customer experience through personalized services. In the hospitality and food services industries, once again big data analytics is used for studying customers’ behavior through shopping data, such as wait times at the checkout. Statistics show that 38% of companies use big data to improve organizational effectiveness. 

In the insurance sector, big data-powered predictive analytics is frequently used for analyzing large volumes of data at high speed during the underwriting stage. Insurance claims analysts now have access to algorithms that help identify fraudulent behaviors. Across all industry sectors, organizations are harnessing the predictive powers of Data Science to enhance their business forecasting capabilities. 

Big data coupled with Data Science  enables enterprise businesses  to leverage their own organization data, rather than relying on market studies or third-party tools. Data Science practitioners work closely with RPA industry professionals to identify data sources for a company, as well as to build dashboards and visuals for searching various forms of data analytics in real-time. Data Science teams can now train deep learning systems to identify contracts and invoices from a stack of documents, as well as perform different types of identification for the information.

Big data analytics has the potential to unlock great insights into data across social media channels and platforms, enabling marketing, customer support, and advertising to improve and be more aligned with corporate goals. Big data analytics make research results better, and helps organizations use research more effectively by allowing them to identify specific test cases and user settings.

Specialized Data Science Use Cases with Examples

Data Science applications can be used for any industry or area of study, but the majority of examples involve data analytics for  business use cases . In this section, some specific use cases are presented with examples to help you better understand its potential in your organization.

Data cleansing:  In Data Science, the first step is data cleansing, which involves identifying and cleaning up any incorrect or incomplete data sets. Data cleansing is critical to identify errors and inconsistencies that can skew your data analysis and lead to poor business decisions. The most important thing about data cleansing is that it’s an ongoing process. Business data is always changing, which means the data you have today might not be correct tomorrow. The best data scientists know that data cleansing isn’t done just once; it’s an ongoing process that starts with the very first data set you collect. 

Prediction and forecasting:  The next step in Data Science is data analysis, prediction, and forecasting. You can do this on an individual level or on a larger scale for your entire customer base. Prediction and forecasting helps you understand how your customers behave and what they may do next. You can use these insights to create better products, marketing campaigns, and customer support. Normally, the techniques used for prediction and forecasting include regression, time series analysis, and artificial neural networks. 

Fraud detection:  Fraud detection is a highly specialized use of Data Science that relies on many techniques to identify inconsistencies. With fraud detection, you’re trying to find any transactions that are incorrect or fraudulent. It’s an important use case because it can significantly reduce the costs of business operations. The best fraud detection systems are wide-ranging. They use many different techniques to identify inconsistencies and unusual data points that suggest fraud. Because fraud detection is such a specialized use case, it’s best to work with a Data Science professional. 

Data Science for business growth:  Every business wants to grow, and this is a natural outcome of doing business. Yet many businesses struggle to keep up with their competitors. Data Science can help you understand your potential customers and improve your services. It can also help you identify new opportunities and explore different areas you can expand into. Use Data Science to identify your target audience and their needs. Then create products and services that serve those needs better than your competitors can. You can also use Data Science to identify new markets, explore new areas for growth, and expand into new industries. 

Data Science is an interdisciplinary field that uses mathematics, engineering, statistics, machine learning, and other fields of study to analyze data and identify patterns. Data Science applications can be used for any industry or area of study, but most examples involve data analytics for  business use cases . Data Science often helps you understand your potential customers and their buying needs. 

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Six Best Data Science Case Studies For Data Science Aspirants

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Data science in hospitality, data science in the pharmaceutical industry, data science in the e-commerce industry, data science in the entertainment industry, data science in finance, data science in public sector, why studying case studies for data science is crucial.

The use of Data science is not new and if you are working in technology and you know the hype around data science. Data science typically involves working with large and complex data sets that may be structured, semi-structured, or unstructured. The goal of data science is to identify patterns, relationships, and trends in data that can be used to inform decision-making, drive business value, and solve complex problems.

Data scientists use a variety of tools and programming languages such as Python, R, SQL, and Hadoop to collect, clean, process, and analyze data. They work closely with subject matter experts, stakeholders, and other data professionals to ensure that the analysis is relevant and actionable.

Learning about data science is an exciting journey and if you are looking forward to having a career in this field then going through some data science case studies can be very useful. We are going to discuss some of the case studies focussing on data science and its use with some examples to give you an idea. Also, with Data Science Certification Course, you will be able to get a comprehensive knowledge of this domain and learn more about it.

Data Science is a fast-expanding discipline that has applications in several industries, such as the hospitality industry. From consumer preferences and behaviour to operational measures such as revenue and inventory, the hotel sector handles a large quantity of data.

Airbnb is a popular online market where people can rent out their homes to travellers. The platform collects a lot of information about its users, such as their search and booking histories, preferences, and reviews. Data science is an important part of Airbnb's business model because it helps the company improve the user experience and optimize its operations in a number of ways, such as:

Airbnb uses Data Science to analyze user behaviour and preferences so that it can make personalized suggestions for properties and experiences that match the user's interests. The platform also uses machine learning algorithms to improve search results and rankings based on factors like location, price, and user reviews. It uses Data Science to help hosts set prices for their properties by looking at market demand and other factors that affect prices. The platform also uses dynamic pricing algorithms to change prices in real-time, based on changes in supply and demand.

Airbnb uses Data Science to find and stop fraud on its platform by looking at user behaviour and patterns that could be signs of fraud. Furthermore, it uses Data Science to improve the way it runs by looking at data about user behaviour, bookings, and how its inventory is managed.

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The pharmaceutical sector creates a vast quantity of data from a variety of sources, including clinical trials, electronic health records, genetic data, and other sorts of medical data, which has increased the significance of data science. This data may be utilized by Data Science to improve medication research, clinical trials, and patient outcomes for pharmaceutical corporations.

AstraZeneca is a global biopharmaceutical company that has been using Data Science to improve drug discovery, clinical trials, and patient outcomes.

AstraZeneca finds appropriate treatment options and creates new medications using big genetic and molecular databases using data science. AstraZeneca and the London Institute of Cancer Research collaborated in 2016 to employ artificial intelligence and machine learning algorithms to examine genetic data and find cancer medication targets. AstraZeneca can produce more effective pharmaceuticals faster by utilizing Data Science to find correlations in biological data that human researchers may miss.

AstraZeneca uses Data Science to identify patient subpopulations who may respond to a medicine and forecast side effects to optimize clinical trial design and analysis.

AstraZeneca uses Data Science to personalize patient treatment strategies based on genetic and other health data. AstraZeneca partnered with Human Longevity, Inc., a genomics and machine learning firm, in 2018 to employ machine learning algorithms to evaluate genomic data from cancer patients and produce individualized treatment plans.

Data Science has become a vital aspect of the e-commerce business, which generates huge quantities of data from a variety of sources, such as consumer transactions, website traffic, and social media. In the e-commerce industry, Data Science is utilized to assist businesses in better understanding their customers, optimizing their operations, and boosting their revenues. Let us understand with a case study in data science.

Data Science has helped Amazon enhance operations, customer experience, and profitability. Amazon's recommendation algorithm is famous for using Data Science. Amazon analyses user data and suggests products based on collaborative filtering, content-based filtering, and other machine-learning techniques. By personalizing shopping and making it easier to discover new things, Amazon has increased sales and consumer loyalty.

Amazon optimizes their supply chain using Data Science to analyze enormous databases of inventory, sales, and delivery data. This lets Amazon make data-driven decisions regarding inventory management, delivery routes, and warehouse locations, reducing costs and improving efficiency. Amazon's fraud detection system detects suspicious behaviour using rule-based systems and machine learning techniques. This has protected Amazon and its customers from fraudulent transactions, decreasing financial losses and increasing confidence.

Amazon predicts customer behaviour and improves operations via predictive analytics. Amazon employs machine learning algorithms to forecast consumer returns, optimize inventory management and reduce return expenses. Amazon's use of Data Science has enabled it to become one of the most successful e-commerce companies in the world by allowing it to make data-driven decisions that improve customer experience, increase profitability, and optimize operations. Amazon's continuous investment in Data Science is likely to spur innovation and economic expansion in the coming years.

The entertainment sector creates vast quantities of data from many sources, including as social media, streaming platforms, box office sales, and user engagement, which has increased the importance of data science. The entertainment business is utilizing data science to better understand its audiences, optimize its operations, and generate more engaging content.

Netflix's recommendation system is famous for using Data Science. Netflix analyses consumer data and recommends relevant content using machine learning algorithms. Netflix's tailored suggestions and easy content discovery have increased customer engagement and loyalty.

Netflix leverages data science to create appealing original content. Netflix leverages user behaviour and preferences to discover content gaps and develop popular content. This has helped Netflix stand out and build a great brand. Netflix acquires third-party content using data science. Netflix leverages viewer behaviour and preferences to determine popular content and make smart content acquisition decisions. This has helped Netflix grow an audience-pleasing collection while minimizing costs.

Netflix enhances streaming quality using data science. Netflix employs machine learning algorithms to find the best streaming bitrate for each user based on network congestion, device performance, and user behaviour. Netflix members now have a better experience and lower data prices. Netflix increases its marketing with data science. Netflix uses viewer behaviour and preferences to generate successful marketing campaigns. This has improved Netflix's marketing campaigns.

Data Science has become increasingly important in the finance industry, where it is being used to help companies better understand their customers, optimize their operations, and reduce risk.

Data Science is utilized by JP Morgan to analyze and detect credit card fraud. JP Morgan uses machine learning algorithms to evaluate vast volumes of transaction data in real-time in order to identify fraudulent transactions. The algorithms are trained on a vast array of data, including transaction amounts, merchant locations, and customer behaviour patterns, in order to discover anomalies and trends indicative of fraudulent activity.

JP Morgan's machine learning algorithms may learn and adapt over time, enhancing their ability to identify fraud and decreasing false positives. This has considerably enhanced JP Morgan's ability to detect fraud, minimize losses and protect clients.

Goldman Sachs' use of Data Science to optimize their trading tactics is another such. Goldman Sachs employs machine learning algorithms to identify trading opportunities and optimize its trading methods by analyzing market data, news, and other information. The ability of the algorithms to process vast amounts of data in real-time enables Goldman Sachs to execute deals more quickly and effectively than its competitors. By utilizing Data Science to optimize its trading tactics, Goldman Sachs has been able to increase its profits and obtain a competitive edge in the extremely competitive financial markets.

Also read , Data Science in Fintech Industry

Data Science is also being increasingly used in the public sector to help governments better understand and serve their citizens. Below is a case study in data science:

Chicago, Illinois, has been utilizing Data Science to analyze traffic data and enhance traffic signal timing. The initial timing of the city's traffic signals was based on a fixed schedule, which frequently led to long waits at junctions and worsened traffic congestion.

To address this issue, the city created the Adaptive Traffic Control System (ATCS), which employs Data Science to adjust the timing of traffic signals based on real-time traffic data. The system takes data from multiple sources, such as traffic sensors, weather sensors, and public transportation data, analyses the data with machine learning algorithms, and optimizes traffic signal timing.

The ATCS technology has been remarkably effective at reducing traffic congestion and enhancing traffic flow. The city of Chicago claimed a 16% decrease in total travel time and a 22% decrease in the number of intersection stops. In addition, the system has decreased pollutants and enhanced air quality by decreasing the length of time vehicles idle at crossings.

The effectiveness of the ATCS system in Chicago has prompted other cities to adopt similar systems. This is just one example of how the public sector is utilizing Data Science to improve the lives of inhabitants and make cities more efficient and sustainable.

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  • Case studies provide the ability to comprehend how Data Science is being utilized in real-world settings. Through researching data science case studies, we can observe how firms are adopting Data Science to solve challenging challenges, develop new products and services, and improve decision-making.
  • Case studies can aid in the acquisition of best practices in Data Science. We can examine how firms are tackling Data Science initiatives, what approaches and technologies they are utilizing, and what issues they are having. This input will assist us in enhancing our Data Science procedures and avoiding common errors.
  • Case studies can provide insights into certain sectors, issues, and solutions. Through analyzing case studies, we can obtain a greater comprehension of specific industries, such as healthcare and banking, and the issues they face. We can also acquire insights into certain Data Science approaches, such as machine learning or data visualization.
  • Studying Data Science case studies can assist develop critical thinking skills. Through analyzing and assessing case studies, we can learn to recognize issues, formulate theories, and assess the evidence. This is a valuable talent in any job, but particularly in Data Science, where critical thinking is essential.

Overall, the study of Data Science case studies is an essential component of Data Science training and skill development. By learning how Data Science certification course is being implemented in the real world, we may obtain useful insights, enhance our skills, and make a good effect in our businesses and communities. So, if you are looking to have a great career in this field, then going for the best course for data science is a great option for you. With StarAgile, you can make sure that you are aware of what is going on in the world and that you are in touch with the latest developments in this sector. So, choose the best data science course and give your career some wings.

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Challenging Data Science Case Studies You Should Try

Aman Kharwal

  • February 14, 2024
  • Machine Learning

One of the best ways to improve your Data Science skills is to work on real-world problems. Working on real-world problems helps you understand how to solve a Data Science problem from a particular domain or a particular type of data. So, if you are looking for some challenges to improve your Data Science skills, this article is for you. In this article, I’ll take you through 5 challenging Data Science case studies based on real-time business problems you should try.

5 Challenging Data Science Case Studies You Should Try

Below are 5 challenging Data Science case studies you should try to improve your skills in working with data.

  • Optimizing Cost and Profitability

Challenging Data Science Case Studies You Should Try: Optimizing Cost and Profitability

This Data Science Case Study is based on a food delivery service’s operations, focusing on understanding its cost structure and profitability through a dataset of 1,000 food orders. It challenges you to dissect major cost components, evaluate individual and overall profitability, and propose strategic recommendations for cost reduction, pricing adjustments, and optimization of commission fees and discount strategies.

By analyzing data points such as order values, delivery fees, discounts, and commission fees, you need to find a profitable balance for the service. It also includes the challenge of simulating the financial impact of your strategies to forecast potential improvements in profitability to provide actionable insights for the service to transform losses into profits.

You can find this case study and the dataset here .

  • Fashion Recommendations Using Image Features

Fashion Recommendations Using Image Features: case study

This case study is based on developing a fashion recommendation system that leverages image feature extraction to analyze and recommend similar or complementary fashion items, such as clothing and accessories, to users. Using a dataset of women’s fashion item images, categorized by type, style, colour, and pattern, your challenge here will be to employ a pre-trained Convolutional Neural Network (CNN) model (e.g., VGG16, ResNet) to extract detailed features from each image.

Then, these features, capturing texture, colour, and shape, need to be compared using a similarity measure (e.g., cosine similarity) to find and recommend items from the dataset that visually resemble the user’s input item. The goal is to enhance the shopping experience by providing personalized fashion recommendations based on visual similarity.

  • Quantitative Analysis

Challenging Data Science Case Studies You Should Try: Quantitative Analysis

This case study involves conducting a quantitative analysis using a dataset that contains detailed information on stock market transactions, including ticker symbols, trading dates, and price points (open, high, low, close, adjusted close) along with trading volume. The objective is to delve into stock market dynamics to enhance investment strategies by identifying long-term price trends, assessing stock volatility, exploring correlations among different stocks for diversification opportunities, and analyzing the risk-return trade-off for various stocks.

This comprehensive approach challenges you to provide a deeper understanding of market movements and inform more strategic investment decisions, focusing on optimizing portfolio management based on empirical data analysis.

  • Light Theme Vs Dark Theme

Light Theme Vs Dark Theme: case study

This case study revolves around an A/B testing experiment conducted by an online bookstore to optimize its website design by comparing two themes: “Light Theme” and “Dark Theme”, aiming to enhance user engagement and increase book purchases. The experiment involves analyzing a dataset containing user interactions and engagement metrics such as click-through rates, conversion rates, bounce rates, and scroll depth, alongside demographic information like age and location, session duration, book purchases, and cart additions.

The primary objective is to ascertain whether there is a statistically significant difference in these key metrics between the two themes to identify which theme promotes better user engagement, higher conversion rates, and increased purchases, thereby guiding the bookstore in making an informed decision on the optimal website theme for boosting performance.

  • B2B E-commerce Fraud

Challenging Data Science Case Studies You Should Try: B2B E-commerce Fraud

This case study outlines ABC Company’s initiative to verify the accuracy of courier fees charged for delivering orders from its e-commerce platform across India. ABC collaborates with several courier companies, which calculate fees based on product weight and the distance from the warehouse to the customer’s address. To ensure the correctness of these charges, ABC utilizes data from three internal reports (Website Order Report, Master SKU, and Warehouse PIN for all India Pincode mappings) and compares it against the invoices received from the courier companies.

The process involves matching the total order weight, calculated using the SKU master for product weights and rounded to the nearest 0.5 kg, against the shipment weight reported by the couriers. Additionally, ABC verifies the delivery area using the warehouse PIN to Pincode mappings and calculates the total courier charges based on a rate card that specifies fixed fees and additional charges for weight slabs and delivery areas. The goal is to reconcile the internally calculated charges with those billed by the courier companies, ensuring billing accuracy for each order shipment.

So below are 5 challenging Data Science case studies you should try to improve your skills in working with data:

I hope you liked this article on 5 challenging Data Science case studies that you should try to improve your skills in working with data. Feel free to ask valuable questions in the comments section below. You can follow me on  Instagram  for many more resources.

Aman Kharwal

Aman Kharwal

I'm a writer and data scientist on a mission to educate others about the incredible power of data📈.

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Data Science Case Study Interview: Your Guide to Success

by Enterprise DNA Experts | 10:29 pm EST | November 28, 2023 | Careers

Data Science Case Study Interview: Your Guide to Success

Ready to crush your next data science interview? Well, you’re in the right place.

This type of interview is designed to assess your problem-solving skills, technical knowledge, and ability to apply data-driven solutions to real-world challenges.

So, how can you master these interviews and secure your next job?

To master your data science case study interview:

Practice Case Studies: Engage in mock scenarios to sharpen problem-solving skills.

Review Core Concepts: Brush up on algorithms, statistical analysis, and key programming languages.

Contextualize Solutions: Connect findings to business objectives for meaningful insights.

Clear Communication: Present results logically and effectively using visuals and simple language.

Adaptability and Clarity: Stay flexible and articulate your thought process during problem-solving.

This article will delve into each of these points and give you additional tips and practice questions to get you ready to crush your upcoming interview!

After you’ve read this article, you can enter the interview ready to showcase your expertise and win your dream role.

Let’s dive in!

Data Science Case Study Interview

Table of Contents

What to Expect in the Interview?

Data science case study interviews are an essential part of the hiring process. They give interviewers a glimpse of how you, approach real-world business problems and demonstrate your analytical thinking, problem-solving, and technical skills.

Furthermore, case study interviews are typically open-ended , which means you’ll be presented with a problem that doesn’t have a right or wrong answer.

Instead, you are expected to demonstrate your ability to:

Break down complex problems

Make assumptions

Gather context

Provide data points and analysis

This type of interview allows your potential employer to evaluate your creativity, technical knowledge, and attention to detail.

But what topics will the interview touch on?

Topics Covered in Data Science Case Study Interviews

Topics Covered in Data Science Case Study Interviews

In a case study interview , you can expect inquiries that cover a spectrum of topics crucial to evaluating your skill set:

Topic 1: Problem-Solving Scenarios

In these interviews, your ability to resolve genuine business dilemmas using data-driven methods is essential.

These scenarios reflect authentic challenges, demanding analytical insight, decision-making, and problem-solving skills.

Real-world Challenges: Expect scenarios like optimizing marketing strategies, predicting customer behavior, or enhancing operational efficiency through data-driven solutions.

Analytical Thinking: Demonstrate your capacity to break down complex problems systematically, extracting actionable insights from intricate issues.

Decision-making Skills: Showcase your ability to make informed decisions, emphasizing instances where your data-driven choices optimized processes or led to strategic recommendations.

Your adeptness at leveraging data for insights, analytical thinking, and informed decision-making defines your capability to provide practical solutions in real-world business contexts.

Problem-Solving Scenarios in Data Science Interview

Topic 2: Data Handling and Analysis

Data science case studies assess your proficiency in data preprocessing, cleaning, and deriving insights from raw data.

Data Collection and Manipulation: Prepare for data engineering questions involving data collection, handling missing values, cleaning inaccuracies, and transforming data for analysis.

Handling Missing Values and Cleaning Data: Showcase your skills in managing missing values and ensuring data quality through cleaning techniques.

Data Transformation and Feature Engineering: Highlight your expertise in transforming raw data into usable formats and creating meaningful features for analysis.

Mastering data preprocessing—managing, cleaning, and transforming raw data—is fundamental. Your proficiency in these techniques showcases your ability to derive valuable insights essential for data-driven solutions.

Topic 3: Modeling and Feature Selection

Data science case interviews prioritize your understanding of modeling and feature selection strategies.

Model Selection and Application: Highlight your prowess in choosing appropriate models, explaining your rationale, and showcasing implementation skills.

Feature Selection Techniques: Understand the importance of selecting relevant variables and methods, such as correlation coefficients, to enhance model accuracy.

Ensuring Robustness through Random Sampling: Consider techniques like random sampling to bolster model robustness and generalization abilities.

Excel in modeling and feature selection by understanding contexts, optimizing model performance, and employing robust evaluation strategies.

Become a master at data modeling using these best practices:

Topic 4: Statistical and Machine Learning Approach

These interviews require proficiency in statistical and machine learning methods for diverse problem-solving. This topic is significant for anyone applying for a machine learning engineer position.

Using Statistical Models: Utilize logistic and linear regression models for effective classification and prediction tasks.

Leveraging Machine Learning Algorithms: Employ models such as support vector machines (SVM), k-nearest neighbors (k-NN), and decision trees for complex pattern recognition and classification.

Exploring Deep Learning Techniques: Consider neural networks, convolutional neural networks (CNN), and recurrent neural networks (RNN) for intricate data patterns.

Experimentation and Model Selection: Experiment with various algorithms to identify the most suitable approach for specific contexts.

Combining statistical and machine learning expertise equips you to systematically tackle varied data challenges, ensuring readiness for case studies and beyond.

Topic 5: Evaluation Metrics and Validation

In data science interviews, understanding evaluation metrics and validation techniques is critical to measuring how well machine learning models perform.

Choosing the Right Metrics: Select metrics like precision, recall (for classification), or R² (for regression) based on the problem type. Picking the right metric defines how you interpret your model’s performance.

Validating Model Accuracy: Use methods like cross-validation and holdout validation to test your model across different data portions. These methods prevent errors from overfitting and provide a more accurate performance measure.

Importance of Statistical Significance: Evaluate if your model’s performance is due to actual prediction or random chance. Techniques like hypothesis testing and confidence intervals help determine this probability accurately.

Interpreting Results: Be ready to explain model outcomes, spot patterns, and suggest actions based on your analysis. Translating data insights into actionable strategies showcases your skill.

Finally, focusing on suitable metrics, using validation methods, understanding statistical significance, and deriving actionable insights from data underline your ability to evaluate model performance.

Evaluation Metrics and Validation for case study interview

Also, being well-versed in these topics and having hands-on experience through practice scenarios can significantly enhance your performance in these case study interviews.

Prepare to demonstrate technical expertise and adaptability, problem-solving, and communication skills to excel in these assessments.

Now, let’s talk about how to navigate the interview.

Here is a step-by-step guide to get you through the process.

Steps by Step Guide Through the Interview

Steps by Step Guide Through the Interview

This section’ll discuss what you can expect during the interview process and how to approach case study questions.

Step 1: Problem Statement: You’ll be presented with a problem or scenario—either a hypothetical situation or a real-world challenge—emphasizing the need for data-driven solutions within data science.

Step 2: Clarification and Context: Seek more profound clarity by actively engaging with the interviewer. Ask pertinent questions to thoroughly understand the objectives, constraints, and nuanced aspects of the problem statement.

Step 3: State your Assumptions: When crucial information is lacking, make reasonable assumptions to proceed with your final solution. Explain these assumptions to your interviewer to ensure transparency in your decision-making process.

Step 4: Gather Context: Consider the broader business landscape surrounding the problem. Factor in external influences such as market trends, customer behaviors, or competitor actions that might impact your solution.

Step 5: Data Exploration: Delve into the provided datasets meticulously. Cleanse, visualize, and analyze the data to derive meaningful and actionable insights crucial for problem-solving.

Step 6: Modeling and Analysis: Leverage statistical or machine learning techniques to address the problem effectively. Implement suitable models to derive insights and solutions aligning with the identified objectives.

Step 7: Results Interpretation: Interpret your findings thoughtfully. Identify patterns, trends, or correlations within the data and present clear, data-backed recommendations relevant to the problem statement.

Step 8: Results Presentation: Effectively articulate your approach, methodologies, and choices coherently. This step is vital, especially when conveying complex technical concepts to non-technical stakeholders.

Remember to remain adaptable and flexible throughout the process and be prepared to adapt your approach to each situation.

Now that you have a guide on navigating the interview, let us give you some tips to help you stand out from the crowd.

Top 3 Tips to Master Your Data Science Case Study Interview

Tips to Master Data Science Case Study Interviews

Approaching case study interviews in data science requires a blend of technical proficiency and a holistic understanding of business implications.

Here are practical strategies and structured approaches to prepare effectively for these interviews:

1. Comprehensive Preparation Tips

To excel in case study interviews, a blend of technical competence and strategic preparation is key.

Here are concise yet powerful tips to equip yourself for success:

Practice with Mock Case Studies : Familiarize yourself with the process through practice. Online resources offer example questions and solutions, enhancing familiarity and boosting confidence.

Review Your Data Science Toolbox: Ensure a strong foundation in fundamentals like data wrangling, visualization, and machine learning algorithms. Comfort with relevant programming languages is essential.

Simplicity in Problem-solving: Opt for clear and straightforward problem-solving approaches. While advanced techniques can be impressive, interviewers value efficiency and clarity.

Interviewers also highly value someone with great communication skills. Here are some tips to highlight your skills in this area.

2. Communication and Presentation of Results

Communication and Presentation of Results in interview

In case study interviews, communication is vital. Present your findings in a clear, engaging way that connects with the business context. Tips include:

Contextualize results: Relate findings to the initial problem, highlighting key insights for business strategy.

Use visuals: Charts, graphs, or diagrams help convey findings more effectively.

Logical sequence: Structure your presentation for easy understanding, starting with an overview and progressing to specifics.

Simplify ideas: Break down complex concepts into simpler segments using examples or analogies.

Mastering these techniques helps you communicate insights clearly and confidently, setting you apart in interviews.

Lastly here are some preparation strategies to employ before you walk into the interview room.

3. Structured Preparation Strategy

Prepare meticulously for data science case study interviews by following a structured strategy.

Here’s how:

Practice Regularly: Engage in mock interviews and case studies to enhance critical thinking and familiarity with the interview process. This builds confidence and sharpens problem-solving skills under pressure.

Thorough Review of Concepts: Revisit essential data science concepts and tools, focusing on machine learning algorithms, statistical analysis, and relevant programming languages (Python, R, SQL) for confident handling of technical questions.

Strategic Planning: Develop a structured framework for approaching case study problems. Outline the steps and tools/techniques to deploy, ensuring an organized and systematic interview approach.

Understanding the Context: Analyze business scenarios to identify objectives, variables, and data sources essential for insightful analysis.

Ask for Clarification: Engage with interviewers to clarify any unclear aspects of the case study questions. For example, you may ask ‘What is the business objective?’ This exhibits thoughtfulness and aids in better understanding the problem.

Transparent Problem-solving: Clearly communicate your thought process and reasoning during problem-solving. This showcases analytical skills and approaches to data-driven solutions.

Blend technical skills with business context, communicate clearly, and prepare to systematically ace your case study interviews.

Now, let’s really make this specific.

Each company is different and may need slightly different skills and specializations from data scientists.

However, here is some of what you can expect in a case study interview with some industry giants.

Case Interviews at Top Tech Companies

Case Interviews at Top Tech Companies

As you prepare for data science interviews, it’s essential to be aware of the case study interview format utilized by top tech companies.

In this section, we’ll explore case interviews at Facebook, Twitter, and Amazon, and provide insight into what they expect from their data scientists.

Facebook predominantly looks for candidates with strong analytical and problem-solving skills. The case study interviews here usually revolve around assessing the impact of a new feature, analyzing monthly active users, or measuring the effectiveness of a product change.

To excel during a Facebook case interview, you should break down complex problems, formulate a structured approach, and communicate your thought process clearly.

Twitter , similar to Facebook, evaluates your ability to analyze and interpret large datasets to solve business problems. During a Twitter case study interview, you might be asked to analyze user engagement, develop recommendations for increasing ad revenue, or identify trends in user growth.

Be prepared to work with different analytics tools and showcase your knowledge of relevant statistical concepts.

Amazon is known for its customer-centric approach and data-driven decision-making. In Amazon’s case interviews, you may be tasked with optimizing customer experience, analyzing sales trends, or improving the efficiency of a certain process.

Keep in mind Amazon’s leadership principles, especially “Customer Obsession” and “Dive Deep,” as you navigate through the case study.

Remember, practice is key. Familiarize yourself with various case study scenarios and hone your data science skills.

With all this knowledge, it’s time to practice with the following practice questions.

Mockup Case Studies and Practice Questions

Mockup Case Studies and Practice Questions

To better prepare for your data science case study interviews, it’s important to practice with some mockup case studies and questions.

One way to practice is by finding typical case study questions.

Here are a few examples to help you get started:

Customer Segmentation: You have access to a dataset containing customer information, such as demographics and purchase behavior. Your task is to segment the customers into groups that share similar characteristics. How would you approach this problem, and what machine-learning techniques would you consider?

Fraud Detection: Imagine your company processes online transactions. You are asked to develop a model that can identify potentially fraudulent activities. How would you approach the problem and which features would you consider using to build your model? What are the trade-offs between false positives and false negatives?

Demand Forecasting: Your company needs to predict future demand for a particular product. What factors should be taken into account, and how would you build a model to forecast demand? How can you ensure that your model remains up-to-date and accurate as new data becomes available?

By practicing case study interview questions , you can sharpen problem-solving skills, and walk into future data science interviews more confidently.

Remember to practice consistently and stay up-to-date with relevant industry trends and techniques.

Final Thoughts

Data science case study interviews are more than just technical assessments; they’re opportunities to showcase your problem-solving skills and practical knowledge.

Furthermore, these interviews demand a blend of technical expertise, clear communication, and adaptability.

Remember, understanding the problem, exploring insights, and presenting coherent potential solutions are key.

By honing these skills, you can demonstrate your capability to solve real-world challenges using data-driven approaches. Good luck on your data science journey!

Frequently Asked Questions

How would you approach identifying and solving a specific business problem using data.

To identify and solve a business problem using data, you should start by clearly defining the problem and identifying the key metrics that will be used to evaluate success.

Next, gather relevant data from various sources and clean, preprocess, and transform it for analysis. Explore the data using descriptive statistics, visualizations, and exploratory data analysis.

Based on your understanding, build appropriate models or algorithms to address the problem, and then evaluate their performance using appropriate metrics. Iterate and refine your models as necessary, and finally, communicate your findings effectively to stakeholders.

Can you describe a time when you used data to make recommendations for optimization or improvement?

Recall a specific data-driven project you have worked on that led to optimization or improvement recommendations. Explain the problem you were trying to solve, the data you used for analysis, the methods and techniques you employed, and the conclusions you drew.

Share the results and how your recommendations were implemented, describing the impact it had on the targeted area of the business.

How would you deal with missing or inconsistent data during a case study?

When dealing with missing or inconsistent data, start by assessing the extent and nature of the problem. Consider applying imputation methods, such as mean, median, or mode imputation, or more advanced techniques like k-NN imputation or regression-based imputation, depending on the type of data and the pattern of missingness.

For inconsistent data, diagnose the issues by checking for typos, duplicates, or erroneous entries, and take appropriate corrective measures. Document your handling process so that stakeholders can understand your approach and the limitations it might impose on the analysis.

What techniques would you use to validate the results and accuracy of your analysis?

To validate the results and accuracy of your analysis, use techniques like cross-validation or bootstrapping, which can help gauge model performance on unseen data. Employ metrics relevant to your specific problem, such as accuracy, precision, recall, F1-score, or RMSE, to measure performance.

Additionally, validate your findings by conducting sensitivity analyses, sanity checks, and comparing results with existing benchmarks or domain knowledge.

How would you communicate your findings to both technical and non-technical stakeholders?

To effectively communicate your findings to technical stakeholders, focus on the methodology, algorithms, performance metrics, and potential improvements. For non-technical stakeholders, simplify complex concepts and explain the relevance of your findings, the impact on the business, and actionable insights in plain language.

Use visual aids, like charts and graphs, to illustrate your results and highlight key takeaways. Tailor your communication style to the audience, and be prepared to answer questions and address concerns that may arise.

How do you choose between different machine learning models to solve a particular problem?

When choosing between different machine learning models, first assess the nature of the problem and the data available to identify suitable candidate models. Evaluate models based on their performance, interpretability, complexity, and scalability, using relevant metrics and techniques such as cross-validation, AIC, BIC, or learning curves.

Consider the trade-offs between model accuracy, interpretability, and computation time, and choose a model that best aligns with the problem requirements, project constraints, and stakeholders’ expectations.

Keep in mind that it’s often beneficial to try several models and ensemble methods to see which one performs best for the specific problem at hand.

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Modern Data Science with R

3rd edition (light edits and updates)

Benjamin S. Baumer, Daniel T. Kaplan, and Nicholas J. Horton

February 14, 2024

3rd edition

This is the work-in-progress of the 3rd edition. At present, there are relatively modest changes from the second edition beyond those necessitated by changes in the R ecosystem.

Key changes include:

  • Transition to Quarto from RMarkdown
  • Transition from magrittr pipe ( %>% ) to base R pipe ( |> )
  • Minor updates to specific examples (e.g., updating tables scraped from Wikipedia) and code (e.g., new group options within the dplyr package).

At the main website for the book , you will find other reviews, instructor resources, errata, and other information.

Do you see issues or have suggestions? To submit corrections, please visit our website’s public GitHub repository and file an issue.

Known issues with the 3rd edition

This is a work in progress. At present there are a number of known issues:

  • nuclear reactors example ( 6.4.4 Example: Japanese nuclear reactors ) needs to be updated to account for Wikipedia changes
  • Python code not yet implemented ( Chapter  21  Epilogue: Towards “big data” )
  • Spark code not yet implemented ( Chapter  21  Epilogue: Towards “big data” )
  • SQL output captions not working ( Chapter  15  Database querying using SQL )
  • Open street map geocoding not yet implemented ( Chapter  18  Geospatial computations )
  • ggmosaic() warnings ( Figure  Mosaic plot (eikosogram) of diabetes by age and weight status (BMI). )
  • RMarkdown introduction ( Appendix  Appendix D — Reproducible analysis and workflow ) not yet converted to Quarto examples
  • issues with references in Appendix  Appendix A — Packages used in the book
  • Exercises not yet available (throughout)
  • Links have not all been verified (help welcomed here!)

2nd edition

The online version of the 2nd edition of Modern Data Science with R is available. You can purchase the book from CRC Press or from Amazon .

The main website for the book includes more information, including reviews, instructor resources, and errata.

To submit corrections, please visit our website’s public GitHub repository and file an issue.

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1st edition

The 1st edition may still be available for purchase. Although much of the material has been updated and improved, the general framework is the same ( reviews ).

© 2021 by Taylor & Francis Group, LLC . Except as permitted under U.S. copyright law, no part of this book may be reprinted, reproduced, transmitted, or utilized in any form by an electronic, mechanical, or other means, now known or hereafter invented, including photocopying, microfilming, and recording, or in any information storage or retrieval system, without written permission from the publishers.

Background and motivation

The increasing volume and sophistication of data poses new challenges for analysts, who need to be able to transform complex data sets to answer important statistical questions. A consensus report on data science for undergraduates ( National Academies of Science, Engineering, and Medicine 2018 ) noted that data science is revolutionizing science and the workplace. They defined a data scientist as “a knowledge worker who is principally occupied with analyzing complex and massive data resources.”

Michael I. Jordan has described data science as the marriage of computational thinking and inferential (statistical) thinking. Without the skills to be able to “wrangle” or “marshal” the increasingly rich and complex data that surround us, analysts will not be able to use these data to make better decisions.

Demand is strong for graduates with these skills. According to the company ratings site Glassdoor , “data scientist” was the best job in America every year from 2016–2019 ( Columbus 2019 ) .

New data technologies make it possible to extract data from more sources than ever before. Streamlined data processing libraries enable data scientists to express how to restructure those data into a form suitable for analysis. Database systems facilitate the storage and retrieval of ever-larger collections of data. State-of-the-art workflow tools foster well-documented and reproducible analysis. Modern statistical and machine learning methods allow the analyst to fit and assess models as well as to undertake supervised or unsupervised learning to glean information about the underlying real-world phenomena. Contemporary data science requires tight integration of these statistical, computing, data-related, and communication skills.

Intended audience

This book is intended for readers who want to develop the appropriate skills to tackle complex data science projects and “think with data” (as coined by Diane Lambert of Google). The desire to solve problems using data is at the heart of our approach.

We acknowledge that it is impossible to cover all these topics in any level of detail within a single book: Many of the chapters could productively form the basis for a course or series of courses. Instead, our goal is to lay a foundation for analysis of real-world data and to ensure that analysts see the power of statistics and data analysis. After reading this book, readers will have greatly expanded their skill set for working with these data, and should have a newfound confidence about their ability to learn new technologies on-the-fly.

This book was originally conceived to support a one-semester, 13-week undergraduate course in data science. We have found that the book will be useful for more advanced students in related disciplines, or analysts who want to bolster their data science skills. At the same time, Part I of the book is accessible to a general audience with no programming or statistics experience.

Key features of this book

Focus on case studies and extended examples.

We feature a series of complex, real-world extended case studies and examples from a broad range of application areas, including politics, transportation, sports, environmental science, public health, social media, and entertainment. These rich data sets require the use of sophisticated data extraction techniques, modern data visualization approaches, and refined computational approaches.

Context is king for such questions, and we have structured the book to foster the parallel developments of statistical thinking, data-related skills, and communication. Each chapter focuses on a different extended example with diverse applications, while exercises allow for the development and refinement of the skills learned in that chapter.

The book has three main sections plus supplementary appendices. Part I provides an introduction to data science, which includes an introduction to data visualization, a foundation for data management (or “wrangling”), and ethics. Part II extends key modeling notions from introductory statistics, including regression modeling, classification and prediction, statistical foundations, and simulation. Part III introduces more advanced topics, including interactive data visualization, SQL and relational databases, geospatial data, text mining, and network science.

We conclude with appendices that introduce the book’s R package, R and RStudio , key aspects of algorithmic thinking, reproducible analysis, a review of regression, and how to set up a local SQL database.

The book features extensive cross-referencing (given the inherent connections between topics and approaches).

Supporting materials

In addition to many examples and extended case studies, the book incorporates exercises at the end of each chapter along with supplementary exercises available online. Many of the exercises are quite open-ended, and are designed to allow students to explore their creativity in tackling data science questions. (A solutions manual for instructors is available from the publisher.)

The book website at https://mdsr-book.github.io/mdsr3e includes the table of contents, the full text of each chapter, and bibliography. The instructor’s website at https://mdsr-book.github.io/ contains code samples, supplementary exercises, additional activities, and a list of errata.

Changes in the second edition

Data science moves quickly. A lot has changed since we wrote the first edition. We have updated all chapters to account for many of these changes and to take advantage of state-of-the-art R packages.

First, the chapter on working with geospatial data has been expanded and split into two chapters. The first focuses on working with geospatial data, and the second focuses on geospatial computations. Both chapters now use the sf package and the new geom_sf() function in ggplot2 . These changes allow students to penetrate deeper into the world of geospatial data analysis.

Second, the chapter on tidy data has undergone significant revisions. A new section on list-columns has been added, and the section on iteration has been expanded into a full chapter. This new chapter makes consistent use of the functional programming style provided by the purrr package. These changes help students develop a habit of mind around scalability: if you are copying-and-pasting code more than twice, there is probably a more efficient way to do it.

Third, the chapter on supervised learning has been split into two chapters and updated to use the tidymodels suite of packages. The first chapter now covers model evaluation in generality, while the second introduces several models. The tidymodels ecosystem provides a consistent syntax for fitting, interpreting, and evaluating a wide variety of machine learning models, all in a manner that is consistent with the tidyverse . These changes significantly reduce the cognitive overhead of the code in this chapter.

The content of several other chapters has undergone more minor—but nonetheless substantive—revisions. All of the code in the book has been revised to adhere more closely to the tidyverse syntax and style. Exercises and solutions from the first edition have been revised, and new exercises have been added. The code from each chapter is now available on the book website. The book has been ported to bookdown , so that a full version can be found online at https://mdsr-book.github.io/mdsr2e .

Key role of technology

While many tools can be used effectively to undertake data science, and the technologies to undertake analyses are quickly changing, R and Python have emerged as two powerful and extensible environments. While it is important for data scientists to be able to use multiple technologies for their analyses, we have chosen to focus on the use of R and RStudio (an open source integrated development environment created by Posit) to avoid cognitive overload. We describe a powerful and coherent set of tools that can be introduced within the confines of a single semester and that provide a foundation for data wrangling and exploration.

We take full advantage of the ( RStudio ) environment. This powerful and easy-to-use front end adds innumerable features to R including package support, code-completion, integrated help, a debugger, and other coding tools. In our experience, the use of ( RStudio ) dramatically increases the productivity of R users, and by tightly integrating reproducible analysis tools, helps avoid error-prone “cut-and-paste” workflows. Our students and colleagues find ( RStudio ) to be an accessible interface. No prior knowledge or experience with R or ( RStudio ) is required: we include an introduction within the Appendix.

As noted earlier, we have comprehensively integrated many substantial improvements in the tidyverse , an opinionated set of packages that provide a more consistent interface to R ( Wickham 2023 ) . Many of the design decisions embedded in the tidyverse packages address issues that have traditionally complicated the use of R for data analysis. These decisions allow novice users to make headway more quickly and develop good habits.

We used a reproducible analysis system ( knitr ) to generate the example code and output in this book. Code extracted from these files is provided on the book’s website. We provide a detailed discussion of the philosophy and use of these systems. In particular, we feel that the knitr and rmarkdown packages for R , which are tightly integrated with Posit’s ( RStudio ) IDE, should become a part of every R user’s toolbox. We can’t imagine working on a project without them (and we’ve incorporated reproducibility into all of our courses).

Modern data science is a team sport. To be able to fully engage, analysts must be able to pose a question, seek out data to address it, ingest this into a computing environment, model and explore, then communicate results. This is an iterative process that requires a blend of statistics and computing skills.

How to use this book

The material from this book has supported several courses to date at Amherst, Smith, and Macalester Colleges, as well as many others around the world. From our personal experience, this includes an intermediate course in data science (in 2013 and 2014 at Smith College and since 2017 at Amherst College), an introductory course in data science (since 2016 at Smith), and a capstone course in advanced data analysis (multiple years at Amherst).

The introductory data science course at Smith has no prerequisites and includes the following subset of material:

  • Data Visualization: three weeks, covering Chapters  1  Prologue: Why data science? – 3  A grammar for graphics
  • Data Wrangling: five weeks, covering Chapters  4  Data wrangling on one table – 7  Iteration
  • Ethics: one week, covering Chapter  8  Data science ethics
  • Database Querying: two weeks, covering Chapter  15  Database querying using SQL
  • Geospatial Data: two weeks, covering Chapter  17  Working with geospatial data and part of Chapter  18  Geospatial computations

A intermediate course at Amherst followed the approach of Baumer ( 2015 ) with a pre-requisite of some statistics and some computer science and an integrated final project. The course generally covers the following chapters:

  • Data Visualization: two weeks, covering Chapters  1  Prologue: Why data science? – 3  A grammar for graphics and 14  Dynamic and customized data graphics
  • Data Wrangling: four weeks, covering Chapters  4  Data wrangling on one table – 7  Iteration
  • Unsupervised Learning: one week, covering Chapter  12  Unsupervised learning
  • Database Querying: one week, covering Chapter  15  Database querying using SQL
  • Geospatial Data: one week, covering Chapter  17  Working with geospatial data and some of Chapter  18  Geospatial computations
  • Text Mining: one week, covering Chapter  19  Text as data
  • Network Science: one week, covering Chapter  20  Network science

The capstone course at Amherst reviewed much of that material in more depth:

  • Data Visualization: three weeks, covering Chapters  1  Prologue: Why data science? – 3  A grammar for graphics and Chapter  14  Dynamic and customized data graphics
  • Data Wrangling: two weeks, covering Chapters  4  Data wrangling on one table – 7  Iteration
  • Simulation: one week, covering Chapter  13  Simulation
  • Statistical Learning: two weeks, covering Chapters  10  Predictive modeling – 12  Unsupervised learning
  • Databases: one week, covering Chapter  15  Database querying using SQL and Appendix  Appendix F — Setting up a database server
  • Spatial Data: one week, covering Chapter  17  Working with geospatial data
  • Big Data: one week, covering Chapter  21  Epilogue: Towards “big data”

We anticipate that this book could serve as the primary text for a variety of other courses, such as a Data Science 2 course, with or without additional supplementary material.

The content in Part I—particularly the ggplot2 visualization concepts presented in Chapter  3  A grammar for graphics and the dplyr data wrangling operations presented in Chapter  4  Data wrangling on one table —is fundamental and is assumed in Parts II and III. Each of the topics in Part III are independent of each other and the material in Part II. Thus, while most instructors will want to cover most (if not all) of Part I in any course, the material in Parts II and III can be added with almost total freedom.

The material in Part II is designed to expose students with a beginner’s understanding of statistics (i.e., basic inference and linear regression) to a richer world of statistical modeling and statistical inference.

Acknowledgments

We would like to thank John Kimmel at Informa CRC/Chapman and Hall for his support and guidance. We also thank Jim Albert, Nancy Boynton, Jon Caris, Mine Çetinkaya-Rundel, Jonathan Che, Patrick Frenett, Scott Gilman, Maria-Cristiana Gîrjău, Johanna Hardin, Alana Horton, John Horton, Kinari Horton, Azka Javaid, Andrew Kim, Eunice Kim, Caroline Kusiak, Ken Kleinman, Priscilla (Wencong) Li, Amelia McNamara, Melody Owen, Randall Pruim, Tanya Riseman, Gabriel Sosa, Katie St. Clair, Amy Wagaman, Susan (Xiaofei) Wang, Hadley Wickham, J. J. Allaire and the Posit (formerly RStudio) developers, the anonymous reviewers, multiple classes at Smith and Amherst Colleges, and many others for contributions to the R and ( RStudio ) environment, comments, guidance, and/or helpful suggestions on drafts of the manuscript. Rose Porta was instrumental in proofreading and easing the transition from Sweave to R Markdown. Jessica Yu converted and tagged most of the exercises from the first edition to the new format based on etude .

Above all we greatly appreciate Cory, Maya, and Julia for their patience and support.

Northampton, MA and St. Paul, MN August, 2023 (third edition [light edits and updates])

Northampton, MA and St. Paul, MN December, 2020 (second edition)

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Practicing data science comes with challenges. It comes with fragmented data, a short supply of data science skills, and various tools, practices, and frameworks to choose from run with rigid IT standards for training and deployment. It's also challenging to operationalize ML models with unclear accuracy and difficult-to-audit predictions.

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Some virtual care companies putting patient data at risk, new study finds

Canadian researchers have patient privacy concerns as industry grows post-covid.

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Social Sharing

This story is part of CBC Health's Second Opinion, a weekly analysis of health and medical science news emailed to subscribers on Saturday mornings. If you haven't subscribed yet, you can do that by  clicking here .

If you visit a doctor virtually through a commercial app, the information you submit in the app could be used to promote a particular drug or service, says the leader of a new Canadian study involving industry insiders.

The industry insiders "were concerned that care might not be designed to be the best care for patients, but rather might be designed to increase uptake of the drug or vaccine to meet the pharmaceutical company objectives," said Dr. Sheryl Spithoff, a physician and scientist at Women's College Hospital in Toronto.

Virtual care took off as a convenient way to access health care during the COVID-19 pandemic, allowing patients to consult with a doctor by videoconference, phone call or text.

It's estimated that more than one in five adults in Canada —  or 6.5 million people — don't have a family physician or nurse practitioner they can see regularly, and virtual care is helping to fill the void.

But the study's researchers and others who work in the medical field have raised concerns that some virtual care companies aren't adequately protecting patients' private health information from being used by drug companies and shared with third parties that want to market products and services.

A female doctor with long, brown hair standing in a medical office.

Spithoff co-authored the study in this week's BMJ Open , based on interviews with 18 individuals employed or affiliated with the Canadian virtual care industry between October 2021 and January 2022. The researchers also analyzed 31 privacy documents from the websites of more than a dozen companies.

The for-profit virtual care industry valued patient data and "appears to view data as a revenue stream," the researchers found.

One employee with a virtual care platform told the researchers that the platform, "at the behest of the pharmaceutical company, would conduct 'A/B testing' by putting out a new version of software to a percentage of patients to see if the new version improved uptake of the drug."

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Many virtual care apps pushing products, selling personal data, research finds

Concerns about how data might be shared.

Matthew Herder, director of the Health Law Institute at Dalhousie University in Halifax, said he hopes the study draws the public's attention to what's behind some of these platforms.

"All of this is happening because of a business model that sees value in collecting that data and using it in a variety of ways that have little to do with patient care and more to do in building up the assets of that company," Herder said.

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Other industry insiders were concerned about how data, such as browsing information, might be shared with third parties such as Google and Meta, the owner of Facebook, for marketing purposes, Spithoff said.

The study's authors said companies placed data in three categories:

  • Registration data, such as name, email address and date of birth.
  • User data, such as how, when and where you use the website, on what device and your internet protocol or IP address.
  • De-identified personal health information, such as removing the name and date of birth and modifying the postal code.

Some companies considered the first two categories as assets that could be monetized, employees told the researchers.

  • Many Canadians welcomed virtual health care. Where does it fit in the system now?
  • Virtual urgent care didn't divert Ontario patients from ER visits during pandemic, study suggests

Not all of the companies treated the third category the same way. Some used personal health information only for the primary purpose of a patient's virtual exchange with a physician, while others used it for commercial reasons, sharing analytics or de-identified information with third parties.

The study's authors said while each individual data point may not provide much information, advertisers and data analytic companies amalgamate data from browsing history and social media accounts to provide insights into an individual's mental health status, for example.

One study participant described how a partnership for targeted ads might work: "If an individual is coming through our service looking for mental health resources, how can we lean them into some of our partnerships with corporate counselling services?"

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Nurses’ union says virtual care is a move toward privatization of health care

Conflict-of-interest questions.

Lorian Hardcastle, an associate professor of law and medicine at the University of Calgary, studied  uptake of virtual care in 2020. She highlighted issues of continuity of care, privacy legislation and consent policies.

Since then, she said, uptake in virtual care accelerated during the COVID-19 pandemic.

"I think that the commercialization of the health-care system raises concerns around conflicts of interest between what is best for patients on the one hand and then on the other hand, what has the best return for shareholders," said Hardcastle, who was not involved in the BMJ Open study.

A woman with long brown hair wearing a blouse and jacket.

Hardcastle said it is helpful to have industry insiders acknowledge problems that health professionals and academics have expressed about commercialization.

The Office of the Privacy Commissioner of Canada, which funded the study, said in an email that privately funded health professionals are generally considered to be conducting commercial activities.

Hospitals, long-term care facilities and home care services that are publicly funded are not considered to be engaged in commercial activities and are covered by provincial privacy legislation, the office said. Health information falls into many categories and may be subject to different privacy laws across various jurisdictions.

Hardcastle also suggested that self-regulatory bodies, such as provincial colleges of physicians and surgeons, may need to revisit policies around relationships between health providers and industry.

Virtual care industry responds

CBC News heard from some Canadian virtual care companies that said they take the privacy of individuals seriously.

"Patient data is only used with patients' explicit consent and only when it's required for health-care interactions between a patient and a doctor," a spokesperson for virtual care platform Maple said. "We do not exploit patient data for marketing or commercial gain."

  • Is virtual care a cure for Canada's battered health-care system?

In a statement, Rocket Doctor said it is important to note that the company "does not do any of the things listed by the researchers as common in the telehealth industry."

Telus said that all of the data collected from its virtual care service is treated as personal health information.

"Telus Health doesn't receive any funds from pharmaceutical companies for our virtual care service and we do not sell any patient data collected," said Pamela Snively, the company's chief data and trust officer.

Source of information hard to pin down

Hardcastle said it may be difficult for some people to distinguish between receiving reliable and accurate information from a health-care provider on an app and getting services marketed to them that the health provider may or may not find useful.

"Your family doctor isn't trying to collect superfluous information in order to market services to you," she said.

Some provinces and territories pay for the virtual services. In other cases, patients pay themselves or are covered by employer or private insurance.

  • Patients tapping into alternative care options, but N.S. emergency departments still face challenges

Nova Scotia's government, for example, has a contract with Maple to provide residents without a primary care provider with unlimited virtual visits. Those who do have a regular provider can have two visits per year paid for by the province.

Tara Sampalli, senior scientific director at Nova Scotia Health Innovation Hub, said the province's contract with Maple means residents' data can't be used in other ways, such as by third-party providers.

The province doesn't have that level of control over other providers of virtual care, said Sampalli, who holds a PhD in health informatics.

Calls for an opt-out choice

Herder, of Dalhousie University, said users should be able to easily opt out of having their data used for commercial purposes. He also said that if the data doesn't represent the full diversity of Canada, algorithms shaping clinical decision-making could be racially biased.

Spithoff said while patient awareness is important, patients aren't in a position to fix this problem.

  • 140,000 Nova Scotians are waiting for a family doctor. Can virtual care help?

"We need better legislation, regulation, and we need better funding for primary care," she said. "Or people can get virtual care integrated into their offline care."

Spithoff and her co-authors said self-regulation by the industry is unlikely to lead to change. 

The researchers acknowledged they were limited to publicly available documents and that they did not interview those affiliated with the third-party advertisers.

data science case studies with solutions

Canadian Medical Association calls for health-care system overhaul

Corrections.

  • An earlier version of this story suggested that all health professionals conduct commercial activities under federal legislation. In fact, some publicly funded health services are not commercial and are covered by various other legislation. Feb 12, 2024 6:11 PM ET

ABOUT THE AUTHOR

data science case studies with solutions

Amina Zafar covers medical sciences and health topics, including infectious diseases, for CBC News. She holds an undergraduate degree in environmental science and a master's in journalism.

With files from CBC's Christine Birak

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HP Managed Print Services case studies

Customers across industries share how hp managed print services helped transform the way they work., csx transforms print culture.

Rail transportation leader looks to HP MPS for excellent service and robust security.

iA Financial Group optimizes print environment

Infolaser improves user experience, security, and carbon reduction strategy with HP MPS.

FMOLHS gains flexibility for business growth

With HP MPS, Franciscan Missionaries of Our Lady Health System consolidates print, reduces costs, improves security, and integrates seamlessly with EMR.

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HP Managed Print Services (MPS) is a suite of scalable and flexible solutions for office and production printing environments that help organizations productively and profitably manage paper and digital document workflows. HP MPS is a combination of hardware, supplies, solutions, and services all under a multi-year contract. HP MPS helps transform unmanaged data into intelligent information that can be captured, connected, and communicated while advancing your organization’s environmental, security, and mobility goals.

What is the process you use?

HP’s comprehensive approach to MPS is delivered through flexible, modular service offerings that are organized into three stages: Design, Transition, and Manage. We allow you to select the level of involvement that’s right for you—you can manage key components of MPS in-house, outsource some areas completely to HP, and take a co-management approach in other areas—whatever works best with your budget and resources.

What are the benefits of MPS?

Managed Print Services (MPS) is a business enabler—it enables you to harness and optimally manage the power of your IT print infrastructure. It allows you to lower your total cost of printing, improve IT efficiency, and invest in areas that can increase productivity, competitiveness, and profitability.*

Does HP MPS understand my industry-specific needs?

Yes, we have deep industry expertise across a number of industries including Healthcare, Education, Manufacturing, Finance and Legal, Retail, and Media Entertainment. We offer industry-specific solutions to digitize and streamline critical processes, transform paper-based workflows to reduce costs, drive productivity, and help improve your customers’ experience.

Why should I choose HP as my MPS partner?

HP is a trusted global technology and services leader with service coverage in 170 countries that are tuned locally to address unique region and country needs. HP has the strongest, most comprehensive print security in the industry,** a global bench of credentialed security advisors, and the World’s Most Secure Printers.** As an innovative leader and partner in advancing sustainability and corporate social responsibility goals, HP was recognized as the #1 America’s most responsible companies*** by Newsweek in 2020.

*Source: ALL Associates Group, February 2018.  For largest 5,000 Global Companies.  

**Includes device, data, and document security capabilities by leading managed print service providers. Based on HP review of 2019 publicly available information on service-level agreement offers, security services, security and management software, and device embedded security features of their competitive in-class printers. For more information, visit hp.com/go/MPSsecurityclaims or hp.com/go/securemps .

***Source: Newsweek, 2020

HP Secure Managed Print Services

The strongest, most comprehensive print security in the industry* helps you deploy print security, manage your fleet over time, and keep it up to date with the latest protections.

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*Includes device, data, and document security capabilities by leading managed print service providers. Based on HP review of 2019 publicly available information on service-level agreement offers, security services, security and management software, and device embedded security features of their competitive in-class printers. For more information, visit  hp.com/go/MPSsecurityclaims  or  hp.com/go/securemps .

eGuide: A blueprint for print transformation

Download eGuide

A definitive guide with key insights and considerations to help your organization build a successful print transformation strategy for hybrid work.

This eGuide offers insights to:

  • Examine how workflow needs have changed across departments and processes
  • Automate paper-based workflows and integrate them with the cloud
  • Help ensure the right security and sustainability measures are in place
  • How HP Managed Print Services can help solve hybrid working challenges

Disclaimers

  • Includes device, data, and document security capabilities by leading managed print service providers. Based on HP review of 2019 publicly available information on service-level agreement offers, security services, security and management software, and device embedded security features of their competitive in-class printers. For more information, visit  hp.com/go/MPSsecurityclaims  or  hp.com/go/securemps.
  • Based on results of third-party (WSP) research for HP of OEM MPS providers with carbon neutral offers as of June 2020. “Comprehensive” means the planet’s only globally certified carbon neutral MPS service that covers lifecycle emissions due to raw material extraction, manufacturing, transportation, use of HP printers, Original HP supplies, and paper, and end of service.
  • HP’s most advanced embedded security features are available on HP Managed and Enterprise devices with HP FutureSmart firmware 4.5 or above. Claim based on HP review of published features as of February 2023 of competitive in-class printers. Only HP offers a combination of security features to automatically detect, stop, and recover from attacks with a self-healing reboot, in alignment with NIST SP 800-193 guidelines for device cyber resiliency. For a list of compatible products, visit  hp.com/go/PrintersThatProtect . For more information, visit  hp.com/go/PrinterSecurityClaims .
  • The HP Carbon Neutral Service is verified in accordance with The CarbonNeutral Protocol.

HP Security is now HP Wolf Security. Security features vary by platform, please see product data sheet for details.

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