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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

July 25, 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  3.19 )
• 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.

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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Data Science in R: A Case Studies Approach to Computational Reasoning and Problem Solving (Chapman & Hall/CRC The R Series) 1st Edition

Effectively Access, Transform, Manipulate, Visualize, and Reason about Data and Computation

Data Science in R: A Case Studies Approach to Computational Reasoning and Problem Solving illustrates the details involved in solving real computational problems encountered in data analysis. It reveals the dynamic and iterative process by which data analysts approach a problem and reason about different ways of implementing solutions.

The book’s collection of projects, comprehensive sample solutions, and follow-up exercises encompass practical topics pertaining to data processing, including:

• Non-standard, complex data formats, such as robot logs and email messages
• Text processing and regular expressions
• Newer technologies, such as Web scraping, Web services, Keyhole Markup Language (KML), and Google Earth
• Statistical methods, such as classification trees, k-nearest neighbors, and naïve Bayes
• Visualization and exploratory data analysis
• Relational databases and Structured Query Language (SQL)
• Algorithm implementation
• Large data and efficiency

Suitable for self-study or as supplementary reading in a statistical computing course, the book enables instructors to incorporate interesting problems into their courses so that students gain valuable experience and data science skills. Students learn how to acquire and work with unstructured or semistructured data as well as how to narrow down and carefully frame the questions of interest about the data.

Blending computational details with statistical and data analysis concepts, this book provides readers with an understanding of how professional data scientists think about daily computational tasks. It will improve readers’ computational reasoning of real-world data analyses.

• ISBN-10 1482234815
• ISBN-13 978-1482234817
• Edition 1st
• Publisher Chapman and Hall/CRC
• Publication date April 21, 2015
• Part of series Chapman & Hall/CRC The R
• Language English
• Dimensions 10 x 7.01 x 1.12 inches
• Print length 539 pages
• See all details

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About the author.

Deborah Nolan holds the Zaffaroni Family Chair in Undergraduate Education at the University of California, Berkeley. She is a fellow of the American Statistical Association and the Institute of Mathematical Statistics. Her research has involved the empirical process, high-dimensional modeling, and, more recently, technology in education and reproducible research.

Duncan Temple Lang is the director of the Data Science Initiative at the University of California, Davis. He has been involved in the development of R and S for 20 years and has developed over 100 R packages. His research focuses on statistical computing, data technologies, meta-computing, reproducibility, and visualization.

Product details

• Publisher ‏ : ‎ Chapman and Hall/CRC; 1st edition (April 21, 2015)
• Language ‏ : ‎ English
• Paperback ‏ : ‎ 539 pages
• ISBN-10 ‏ : ‎ 1482234815
• ISBN-13 ‏ : ‎ 978-1482234817
• Item Weight ‏ : ‎ 2.46 pounds
• Dimensions ‏ : ‎ 10 x 7.01 x 1.12 inches
• #235 in Database Storage & Design
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About the author

Deborah nolan.

Deborah (Deb) Nolan is Professor Emerita of Statistics and Associate Dean for Students in the College of Computing, Data Science, and Society at the University of California, Berkeley, where she held the Zaffaroni Family Chair in Undergraduate Education. Her pedagogical approach connects research with practice through case studies. She has co-authored six books: Learning Data Science, Communicating with Data, Stat Labs, Teaching Statistics, Data Science in R, and XML and Web Technologies for Data Sciences with R.

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1st Edition

Data Science in R A Case Studies Approach to Computational Reasoning and Problem Solving

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Description

Effectively Access, Transform, Manipulate, Visualize, and Reason about Data and Computation Data Science in R: A Case Studies Approach to Computational Reasoning and Problem Solving illustrates the details involved in solving real computational problems encountered in data analysis. It reveals the dynamic and iterative process by which data analysts approach a problem and reason about different ways of implementing solutions. The book’s collection of projects, comprehensive sample solutions, and follow-up exercises encompass practical topics pertaining to data processing, including: Non-standard, complex data formats, such as robot logs and email messages Text processing and regular expressions Newer technologies, such as Web scraping, Web services, Keyhole Markup Language (KML), and Google Earth Statistical methods, such as classification trees, k-nearest neighbors, and naïve Bayes Visualization and exploratory data analysis Relational databases and Structured Query Language (SQL) Simulation Algorithm implementation Large data and efficiency Suitable for self-study or as supplementary reading in a statistical computing course, the book enables instructors to incorporate interesting problems into their courses so that students gain valuable experience and data science skills. Students learn how to acquire and work with unstructured or semistructured data as well as how to narrow down and carefully frame the questions of interest about the data. Blending computational details with statistical and data analysis concepts, this book provides readers with an understanding of how professional data scientists think about daily computational tasks. It will improve readers’ computational reasoning of real-world data analyses.

Table of Contents

Deborah Nolan holds the Zaffaroni Family Chair in Undergraduate Education at the University of California, Berkeley. She is a fellow of the American Statistical Association and the Institute of Mathematical Statistics. Her research has involved the empirical process, high-dimensional modeling, and, more recently, technology in education and reproducible research. Duncan Temple Lang is the director of the Data Science Initiative at the University of California, Davis. He has been involved in the development of R and S for 20 years and has developed over 100 R packages. His research focuses on statistical computing, data technologies, meta-computing, reproducibility, and visualization.

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Effectively Access, Transform, Manipulate, Visualize, and Reason about Data and ComputationData Science in R: A Case Studies Approach to Computational Reasoning and Problem Solving illustrates the details involved in solving real computational problems encountered in data analysis. It reveals the dynamic and iterative process by which data analysts

TABLE OF CONTENTS

Part | 2  pages, i data manipulation and modeling, chapter 1 | 42  pages, predicting location via indoor positioning systems, chapter 2 | 60  pages, modeling runners’ times in the cherry blossom race, chapter 3 | 66  pages, using statistics to identify spam, chapter 4 | 46  pages, processing robot and sensor log files: seeking a circular target, chapter 5 | 22  pages, strategies for analyzing a 12-gigabyte data set: airline flight delays, ii simulation studies, chapter 6 | 36  pages, pairs trading, chapter 7 | 32  pages, simulation study of a branching process, chapter 8 | 58  pages, a self-organizing dynamic system with a phase transition, chapter 9 | 30  pages, simulating blackjack, iii data and web technologies, chapter 10 | 20  pages, baseball: exploring data in a relational database, chapter 11 | 38  pages, cia factbook mashup, chapter 12 | 50  pages, exploring data science jobs with web scraping and text mining.

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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.

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.

Walmart Sales Forecasting Data Science Project

Downloadable solution code | Explanatory videos | Tech Support

Table of Contents

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.

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.

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.

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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.

Get Closer To Your Dream of Becoming a Data Scientist with 70+ Solved End-to-End ML Projects

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.

New Projects

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.

Get FREE Access to Machine Learning Example Codes for Data Cleaning , Data Munging, and Data Visualization

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.

Explore More  Data Science and Machine Learning Projects for Practice. Fast-Track Your Career Transition with ProjectPro

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.

Access Data Science and Machine Learning Project Code Examples

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 25 Data Science Case Studies [2024]

In an era where data is the new gold, harnessing its power through data science has led to groundbreaking advancements across industries. From personalized marketing to predictive maintenance, the applications of data science are not only diverse but transformative. This compilation of the top 25 data science case studies showcases the profound impact of intelligent data utilization in solving real-world problems. These examples span various sectors, including healthcare, finance, transportation, and manufacturing, illustrating how data-driven decisions shape business operations’ future, enhance efficiency, and optimize user experiences. As we delve into these case studies, we witness the incredible potential of data science to innovate and drive success in today’s data-centric world.

Related: Interesting Data Science Facts

Top 25 Data Science Case Studies [2024]

Case study 1 – personalized marketing (amazon).

Challenge:  Amazon aimed to enhance user engagement by tailoring product recommendations to individual preferences, requiring the real-time processing of vast data volumes.

Solution:  Amazon implemented a sophisticated machine learning algorithm known as collaborative filtering, which analyzes users’ purchase history, cart contents, product ratings, and browsing history, along with the behavior of similar users. This approach enables Amazon to offer highly personalized product suggestions.

Overall Impact:

• Increased Customer Satisfaction:  Tailored recommendations improved the shopping experience.
• Higher Sales Conversions:  Relevant product suggestions boosted sales.

Key Takeaways:

• Personalized Marketing Significantly Enhances User Engagement:  Demonstrating how tailored interactions can deepen user involvement and satisfaction.
• Effective Use of Big Data and Machine Learning Can Transform Customer Experiences:  These technologies redefine the consumer landscape by continuously adapting recommendations to changing user preferences and behaviors.

This strategy has proven pivotal in increasing Amazon’s customer loyalty and sales by making the shopping experience more relevant and engaging.

Case Study 2 – Real-Time Pricing Strategy (Uber)

Challenge:  Uber needed to adjust its pricing dynamically to reflect real-time demand and supply variations across different locations and times, aiming to optimize driver incentives and customer satisfaction without manual intervention.

Solution:  Uber introduced a dynamic pricing model called “surge pricing.” This system uses data science to automatically calculate fares in real time based on current demand and supply data. The model incorporates traffic conditions, weather forecasts, and local events to adjust prices appropriately.

• Optimized Ride Availability:  The model reduced customer wait times by incentivizing more drivers to be available during high-demand periods.
• Increased Driver Earnings:  Drivers benefitted from higher earnings during surge periods, aligning their incentives with customer demand.
• Efficient Balance of Supply and Demand:  Dynamic pricing matches ride availability with customer needs.
• Importance of Real-Time Data Processing:  The real-time processing of data is crucial for responsive and adaptive service delivery.

Uber’s implementation of surge pricing illustrates the power of using real-time data analytics to create a flexible and responsive pricing system that benefits both consumers and service providers, enhancing overall service efficiency and satisfaction.

Case Study 3 – Fraud Detection in Banking (JPMorgan Chase)

Challenge:  JPMorgan Chase faced the critical need to enhance its fraud detection capabilities to safeguard the institution and its customers from financial losses. The primary challenge was detecting fraudulent transactions swiftly and accurately in a vast stream of legitimate banking activities.

Solution:  The bank implemented advanced machine learning models that analyze real-time transaction patterns and customer behaviors. These models are continuously trained on vast amounts of historical fraud data, enabling them to identify and flag transactions that significantly deviate from established patterns, which may indicate potential fraud.

• Substantial Reduction in Fraudulent Transactions:  The advanced detection capabilities led to a marked decrease in fraud occurrences.
• Enhanced Security for Customer Accounts:  Customers experienced greater security and trust in their transactions.
• Effectiveness of Machine Learning in Fraud Detection:  Machine learning models are greatly effective at identifying fraud activities within large datasets.
• Importance of Ongoing Training and Updates:  Continuous training and updating of models are crucial to adapt to evolving fraudulent techniques and maintain detection efficacy.

JPMorgan Chase’s use of machine learning for fraud detection demonstrates how financial institutions can leverage advanced analytics to enhance security measures, protect financial assets, and build customer trust in their banking services.

Case Study 4 – Optimizing Healthcare Outcomes (Mayo Clinic)

Challenge:  The Mayo Clinic aimed to enhance patient outcomes by predicting diseases before they reach critical stages. This involved analyzing large volumes of diverse data, including historical patient records and real-time health metrics from various sources like lab results and patient monitors.

Solution:  The Mayo Clinic employed predictive analytics to integrate and analyze this data to build models that predict patient risk for diseases such as diabetes and heart disease, enabling earlier and more targeted interventions.

• Improved Patient Outcomes:  Early identification of at-risk patients allowed for timely medical intervention.
• Reduction in Healthcare Costs:  Preventing disease progression reduces the need for more extensive and costly treatments later.
• Early Identification of Health Risks:  Predictive models are essential for identifying at-risk patients early, improving the chances of successful interventions.
• Integration of Multiple Data Sources:  Combining historical and real-time data provides a comprehensive view that enhances the accuracy of predictions.

Case Study 5 – Streamlining Operations in Manufacturing (General Electric)

Challenge:  General Electric needed to optimize its manufacturing processes to reduce costs and downtime by predicting when machines would likely require maintenance to prevent breakdowns.

Solution:  GE leveraged data from sensors embedded in machinery to monitor their condition continuously. Data science algorithms analyze this sensor data to predict when a machine is likely to disappoint, facilitating preemptive maintenance and scheduling.

• Reduction in Unplanned Machine Downtime:  Predictive maintenance helped avoid unexpected breakdowns.
• Lower Maintenance Costs and Improved Machine Lifespan:  Regular maintenance based on predictive data reduced overall costs and extended the life of machinery.
• Predictive Maintenance Enhances Operational Efficiency:  Using data-driven predictions for maintenance can significantly reduce downtime and operational costs.
• Value of Sensor Data:  Continuous monitoring and data analysis are crucial for forecasting equipment health and preventing failures.

Related: Data Engineering vs. Data Science

Case Study 6 – Enhancing Supply Chain Management (DHL)

Challenge:  DHL sought to optimize its global logistics and supply chain operations to decreases expenses and enhance delivery efficiency. It required handling complex data from various sources for better route planning and inventory management.

Solution:  DHL implemented advanced analytics to process and analyze data from its extensive logistics network. This included real-time tracking of shipments, analysis of weather conditions, traffic patterns, and inventory levels to optimize route planning and warehouse operations.

• Enhanced Efficiency in Logistics Operations:  More precise route planning and inventory management improved delivery times and reduced resource wastage.
• Reduced Operational Costs:  Streamlined operations led to significant cost savings across the supply chain.
• Critical Role of Comprehensive Data Analysis:  Effective supply chain management depends on integrating and analyzing data from multiple sources.
• Benefits of Real-Time Data Integration:  Real-time data enhances logistical decision-making, leading to more efficient and cost-effective operations.

Case Study 7 – Predictive Maintenance in Aerospace (Airbus)

Challenge:  Airbus faced the challenge of predicting potential failures in aircraft components to enhance safety and reduce maintenance costs. The key was to accurately forecast the lifespan of parts under varying conditions and usage patterns, which is critical in the aerospace industry where safety is paramount.

Solution:  Airbus tackled this challenge by developing predictive models that utilize data collected from sensors installed on aircraft. These sensors continuously monitor the condition of various components, providing real-time data that the models analyze. The predictive algorithms assess the likelihood of component failure, enabling maintenance teams to schedule repairs or replacements proactively before actual failures occur.

• Increased Safety:  The ability to predict and prevent potential in-flight failures has significantly improved the safety of Airbus aircraft.
• Reduced Costs:  By optimizing maintenance schedules and minimizing unnecessary checks, Airbus has been able to cut down on maintenance expenses and reduce aircraft downtime.
• Enhanced Safety through Predictive Analytics:  The use of predictive analytics in monitoring aircraft components plays a crucial role in preventing failures, thereby enhancing the overall safety of aviation operations.
• Valuable Insights from Sensor Data:  Real-time data from operational use is critical for developing effective predictive maintenance strategies. This data provides insights for understanding component behavior under various conditions, allowing for more accurate predictions.

This case study demonstrates how Airbus leverages advanced data science techniques in predictive maintenance to ensure higher safety standards and more efficient operations, setting an industry benchmark in the aerospace sector.

Case Study 8 – Enhancing Film Recommendations (Netflix)

Challenge:  Netflix aimed to improve customer retention and engagement by enhancing the accuracy of its recommendation system. This task involved processing and analyzing vast amounts of data to understand diverse user preferences and viewing habits.

Solution:  Netflix employed collaborative filtering techniques, analyzing user behaviors (like watching, liking, or disliking content) and similarities between content items. This data-driven approach allows Netflix to refine and personalize recommendations continuously based on real-time user interactions.

• Increased Viewer Engagement:  Personalized recommendations led to longer viewing sessions.
• Higher Customer Satisfaction and Retention Rates:  Tailored viewing experiences improved overall customer satisfaction, enhancing loyalty.
• Tailoring User Experiences:  Machine learning is pivotal in personalizing media content, significantly impacting viewer engagement and satisfaction.
• Importance of Continuous Updates:  Regularly updating recommendation algorithms is essential to maintain relevance and effectiveness in user engagement.

Case Study 9 – Traffic Flow Optimization (Google)

Challenge:  Google needed to optimize traffic flow within its Google Maps service to reduce congestion and improve routing decisions. This required real-time analysis of extensive traffic data to predict and manage traffic conditions accurately.

Solution:  Google Maps integrates data from multiple sources, including satellite imagery, sensor data, and real-time user location data. These data points are used to model traffic patterns and predict future conditions dynamically, which informs updated routing advice.

• Reduced Traffic Congestion:  More efficient routing reduced overall traffic buildup.
• Enhanced Accuracy of Traffic Predictions and Routing:  Improved predictions led to better user navigation experiences.
• Integration of Multiple Data Sources:  Combining various data streams enhances the accuracy of traffic management systems.
• Advanced Modeling Techniques:  Sophisticated models are crucial for accurately predicting traffic patterns and optimizing routes.

Case Study 10 – Risk Assessment in Insurance (Allstate)

Challenge:  Allstate sought to refine its risk assessment processes to offer more accurately priced insurance products, challenging the limitations of traditional actuarial models through more nuanced data interpretations.

Solution:  Allstate enhanced its risk assessment framework by integrating machine learning, allowing for granular risk factor analysis. This approach utilizes individual customer data such as driving records, home location specifics, and historical claim data to tailor insurance offerings more accurately.

• More Precise Risk Assessment:  Improved risk evaluation led to more tailored insurance offerings.
• Increased Market Competitiveness:  Enhanced pricing accuracy boosted Allstate’s competitive edge in the insurance market.
• Nuanced Understanding of Risk:  Machine learning provides a deeper, more nuanced understanding of risk than traditional models, leading to better risk pricing.
• Personalized Pricing Strategies:  Leveraging detailed customer data in pricing strategies enhances customer satisfaction and business performance.

Related: Can you move from Cybersecurity to Data Science?

Case Study 11 – Energy Consumption Reduction (Google DeepMind)

Challenge:  Google DeepMind aimed to significantly reduce the high energy consumption required for cooling Google’s data centers, which are crucial for maintaining server performance but also represent a major operational cost.

Solution:  DeepMind implemented advanced AI algorithms to optimize the data center cooling systems. These algorithms predict temperature fluctuations and adjust cooling processes accordingly, saving energy and reducing equipment wear and tear.

• Reduction in Energy Consumption:  Achieved a 40% reduction in energy used for cooling.
• Decrease in Operational Costs and Environmental Impact:  Lower energy usage resulted in cost savings and reduced environmental footprint.
• AI-Driven Optimization:  AI can significantly decrease energy usage in large-scale infrastructure.
• Operational Efficiency Gains:  Efficiency improvements in operational processes lead to cost savings and environmental benefits.

Case Study 12 – Improving Public Safety (New York City Police Department)

Challenge:  The NYPD needed to enhance its crime prevention strategies by better predicting where and when crimes were most likely to occur, requiring sophisticated analysis of historical crime data and environmental factors.

Solution:  The NYPD implemented a predictive policing system that utilizes data analytics to identify potential crime hotspots based on trends and patterns in past crime data. Officers are preemptively dispatched to these areas to deter criminal activities.

• Reduction in Crime Rates:  There is a notable decrease in crime in areas targeted by predictive policing.
• More Efficient Use of Police Resources:  Enhanced allocation of resources where needed.
• Effectiveness of Data-Driven Crime Prevention:  Targeting resources based on data analytics can significantly reduce crime.
• Proactive Law Enforcement:  Predictive analytics enable a shift from reactive to proactive law enforcement strategies.

Case Study 13 – Enhancing Agricultural Yields (John Deere)

Challenge:  John Deere aimed to help farmers increase agricultural productivity and sustainability by optimizing various farming operations from planting to harvesting.

Solution:  Utilizing data from sensors on equipment and satellite imagery, John Deere developed algorithms that provide actionable insights for farmers on optimal planting times, water usage, and harvest schedules.

• Increased Crop Yields:  More efficient farming methods led to higher yields.
• Enhanced Sustainability of Farming Practices:  Improved resource management contributed to more sustainable agriculture.
• Precision Agriculture:  Significantly improves productivity and resource efficiency.
• Data-Driven Decision-Making:  Enables better farming decisions through timely and accurate data.

Case Study 14 – Streamlining Drug Discovery (Pfizer)

Challenge:  Pfizer faced the need to accelerate the process of discoverying drug and improve the success rates of clinical trials.

Solution:  Pfizer employed data science to simulate and predict outcomes of drug trials using historical data and predictive models, optimizing trial parameters and improving the selection of drug candidates.

• Accelerated Drug Development:  Reduced time to market for new drugs.
• Increased Efficiency and Efficacy in Clinical Trials:  More targeted trials led to better outcomes.
• Reduction in Drug Development Time and Costs:  Data science streamlines the R&D process.
• Improved Clinical Trial Success Rates:  Predictive modeling enhances the accuracy of trial outcomes.

Case Study 15 – Media Buying Optimization (Procter & Gamble)

Challenge:  Procter & Gamble aimed to maximize the ROI of their extensive advertising budget by optimizing their media buying strategy across various channels.

Solution:  P&G analyzed extensive data on consumer behavior and media consumption to identify the most effective times and channels for advertising, allowing for highly targeted ads that reach the intended audience at optimal times.

• Improved Effectiveness of Advertising Campaigns:  More effective ads increased campaign impact.
• Increased Sales and Better Budget Allocation:  Enhanced ROI from more strategic media spending.
• Enhanced Media Buying Strategies:  Data analytics significantly improves media buying effectiveness.
• Insights into Consumer Behavior:  Understanding consumer behavior is crucial for optimizing advertising ROI.

Related: Is Data Science Certificate beneficial for your career?

Case Study 16 – Reducing Patient Readmission Rates with Predictive Analytics (Mount Sinai Health System)

Challenge:  Mount Sinai Health System sought to reduce patient readmission rates, a significant indicator of healthcare quality and a major cost factor. The challenge involved identifying patients at high risk of being readmitted within 30 days of discharge.

Solution:  The health system implemented a predictive analytics platform that analyzes real-time patient data and historical health records. The system detects patterns and risk factors contributing to high readmission rates by utilizing machine learning algorithms. Factors such as past medical history, discharge conditions, and post-discharge care plans were integrated into the predictive model.

• Reduced Readmission Rates:  Early identification of at-risk patients allowed for targeted post-discharge interventions, significantly reducing readmission rates.
• Enhanced Patient Outcomes: Patients received better follow-up care tailored to their health risks.
• Predictive Analytics in Healthcare:  Effective for managing patient care post-discharge.
• Holistic Patient Data Utilization: Integrating various data points provides a more accurate prediction and better healthcare outcomes.

Case Study 17 – Enhancing E-commerce Customer Experience with AI (Zalando)

Challenge:  Zalando aimed to enhance the online shopping experience by improving the accuracy of size recommendations, a common issue that leads to high return rates in online apparel shopping.

Solution:  Zalando developed an AI-driven size recommendation engine that analyzes past purchase and return data in combination with customer feedback and preferences. This system utilizes machine learning to predict the best-fit size for customers based on their unique body measurements and purchase history.

• Reduced Return Rates:  More accurate size recommendations decreased the returns due to poor fit.
• Improved Customer Satisfaction: Customers experienced a more personalized shopping journey, enhancing overall satisfaction.
• Customization Through AI:  Personalizing customer experience can significantly impact satisfaction and business metrics.
• Data-Driven Decision-Making: Utilizing customer data effectively can improve business outcomes by reducing costs and enhancing the user experience.

Case Study 18 – Optimizing Energy Grid Performance with Machine Learning (Enel Group)

Challenge:  Enel Group, one of the largest power companies, faced challenges in managing and optimizing the performance of its vast energy grids. The primary goal was to increase the efficiency of energy distribution and reduce operational costs while maintaining reliability in the face of fluctuating supply and demand.

Solution:  Enel Group implemented a machine learning-based system that analyzes real-time data from smart meters, weather stations, and IoT devices across the grid. This system is designed to predict peak demand times, potential outages, and equipment failures before they occur. By integrating these predictions with automated grid management tools, Enel can dynamically adjust energy flows, allocate resources more efficiently, and schedule maintenance proactively.

• Enhanced Grid Efficiency:  Improved distribution management, reduced energy wastage, and optimized resource allocation.
• Reduced Operational Costs: Predictive maintenance and better grid management decreased the frequency and cost of repairs and outages.
• Predictive Maintenance in Utility Networks:  Advanced analytics can preemptively identify issues, saving costs and enhancing service reliability.
• Real-Time Data Integration: Leveraging data from various sources in real-time enables more agile and informed decision-making in energy management.

Case Study 19 – Personalizing Movie Streaming Experience (WarnerMedia)

Challenge:  WarnerMedia sought to enhance viewer engagement and subscription retention rates on its streaming platforms by providing more personalized content recommendations.

Solution:  WarnerMedia deployed a sophisticated data science strategy, utilizing deep learning algorithms to analyze viewer behaviors, including viewing history, ratings given to shows and movies, search patterns, and demographic data. This analysis helped create highly personalized viewer profiles, which were then used to tailor content recommendations, homepage layouts, and promotional offers specifically to individual preferences.

• Increased Viewer Engagement:  Personalized recommendations resulted in extended viewing times and increased interactions with the platform.
• Higher Subscription Retention: Tailored user experiences improved overall satisfaction, leading to lower churn rates.
• Deep Learning Enhances Personalization:  Deep learning algorithms allow a more nuanced knowledge of consumer preferences and behavior.
• Data-Driven Customization is Key to User Retention: Providing a customized experience based on data analytics is critical for maintaining and growing a subscriber base in the competitive streaming market.

Case Study 20 – Improving Online Retail Sales through Customer Sentiment Analysis (Zappos)

Challenge:  Zappos, an online shoe and clothing retailer, aimed to enhance customer satisfaction and boost sales by better understanding customer sentiments and preferences across various platforms.

Solution:  Zappos implemented a comprehensive sentiment analysis program that utilized natural language processing (NLP) techniques to gather and analyze customer feedback from social media, product reviews, and customer support interactions. This data was used to identify emerging trends, customer pain points, and overall sentiment towards products and services. The insights derived from this analysis were subsequently used to customize marketing strategies, enhance product offerings, and improve customer service practices.

• Enhanced Product Selection and Marketing:  Insight-driven adjustments to inventory and marketing strategies increased relevancy and customer satisfaction.
• Improved Customer Experience: By addressing customer concerns and preferences identified through sentiment analysis, Zappos enhanced its overall customer service, increasing loyalty and repeat business.
• Power of Sentiment Analysis in Retail:  Understanding and reacting to customer emotions and opinions can significantly impact sales and customer satisfaction.
• Strategic Use of Customer Feedback: Leveraging customer feedback to drive business decisions helps align product offerings and services with customer expectations, fostering a positive brand image.

Related: Data Science Industry in the US

Case Study 21 – Streamlining Airline Operations with Predictive Analytics (Delta Airlines)

Challenge:  Delta Airlines faced operational challenges, including flight delays, maintenance scheduling inefficiencies, and customer service issues, which impacted passenger satisfaction and operational costs.

Solution:  Delta implemented a predictive analytics system that integrates data from flight operations, weather reports, aircraft sensor data, and historical maintenance records. The system predicts potential delays using machine learning models and suggests optimal maintenance scheduling. Additionally, it forecasts passenger load to optimize staffing and resource allocation at airports.

• Reduced Flight Delays:  Predictive insights allowed for better planning and reduced unexpected delays.
• Enhanced Maintenance Efficiency:  Maintenance could be scheduled proactively, decreasing the time planes spend out of service.
• Improved Passenger Experience: With better resource management, passenger handling became more efficient, enhancing overall customer satisfaction.
• Operational Efficiency Through Predictive Analytics:  Leveraging data for predictive purposes significantly improves operational decision-making.
• Data Integration Across Departments: Coordinating data from different sources provides a holistic view crucial for effective airline management.

Case Study 22 – Enhancing Financial Advisory Services with AI (Morgan Stanley)

Challenge:  Morgan Stanley sought to offer clients more personalized and effective financial guidance. The challenge was seamlessly integrating vast financial data with individual client profiles to deliver tailored investment recommendations.

Solution:  Morgan Stanley developed an AI-powered platform that utilizes natural language processing and ML to analyze financial markets, client portfolios, and historical investment performance. The system identifies patterns and predicts market trends while considering each client’s financial goals, risk tolerance, and investment history. This integrated approach enables financial advisors to offer highly customized advice and proactive investment strategies.

• Improved Client Satisfaction:  Clients received more relevant and timely investment recommendations, enhancing their overall satisfaction and trust in the advisory services.
• Increased Efficiency: Advisors were able to manage client portfolios more effectively, using AI-driven insights to make faster and more informed decisions.
• Personalization through AI:  Advanced analytics and AI can significantly enhance the personalization of financial services, leading to better client engagement.
• Data-Driven Decision Making: Leveraging diverse data sets provides a comprehensive understanding crucial for tailored financial advising.

Case Study 23 – Optimizing Inventory Management in Retail (Walmart)

Challenge:  Walmart sought to improve inventory management across its vast network of stores and warehouses to reduce overstock and stockouts, which affect customer satisfaction and operational efficiency.

Solution:  Walmart implemented a robust data analytics system that integrates real-time sales data, supply chain information, and predictive analytics. This system uses machine learning algorithms to forecast demand for thousands of products at a granular level, considering factors such as seasonality, local events, and economic trends. The predictive insights allow Walmart to dynamically adjust inventory levels, optimize restocking schedules, and manage distribution logistics more effectively.

• Reduced Inventory Costs:  More accurate demand forecasts helped minimize overstock and reduce waste.
• Enhanced Customer Satisfaction: Improved stock availability led to better in-store experiences and higher customer satisfaction.
• Precision in Demand Forecasting:  Advanced data analytics and machine learning significantly enhance demand forecasting accuracy in retail.
• Integrated Data Systems:  Combining various data sources provides a comprehensive view of inventory needs, improving overall supply chain efficiency.

Case Study 24: Enhancing Network Security with Predictive Analytics (Cisco)

Challenge:  Cisco encountered difficulties protecting its extensive network infrastructure from increasingly complex cyber threats. The objective was to bolster their security protocols by anticipating potential breaches before they happen.

Solution:  Cisco developed a predictive analytics solution that leverages ML algorithms to analyze patterns in network traffic and identify anomalies that could suggest a security threat. By integrating this system with their existing security protocols, Cisco can dynamically adjust defenses and alert system administrators about potential vulnerabilities in real-time.

• Improved Security Posture:  The predictive system enabled proactive responses to potential threats, significantly reducing the incidence of successful cyber attacks.
• Enhanced Operational Efficiency: Automating threat detection and response processes allowed Cisco to manage network security more efficiently, with fewer resources dedicated to manual monitoring.
• Proactive Security Measures:  Employing predictive cybersecurity analytics helps organizations avoid potential threats.
• Integration of Machine Learning: Machine learning is crucial for effectively detecting patterns and anomalies that human analysts might overlook, leading to stronger security measures.

Case Study 25 – Improving Agricultural Efficiency with IoT and AI (Bayer Crop Science)

Challenge:  Bayer Crop Science aimed to enhance agricultural efficiency and crop yields for farmers worldwide, facing the challenge of varying climatic conditions and soil types that affect crop growth differently.

Solution:  Bayer deployed an integrated platform that merges IoT sensors, satellite imagery, and AI-driven analytics. This platform gathers real-time weather conditions, soil quality, and crop health data. Utilizing machine learning models, the system processes this data to deliver precise agricultural recommendations to farmers, including optimal planting times, watering schedules, and pest management strategies.

• Increased Crop Yields:  Tailored agricultural practices led to higher productivity per hectare.
• Reduced Resource Waste: Efficient water use, fertilizers, and pesticides minimized environmental impact and operational costs.
• Precision Agriculture:  Leveraging IoT and AI enables more precise and data-driven agricultural practices, enhancing yield and efficiency.
• Sustainability in Farming:  Advanced data analytics enhance the sustainability of farming by optimizing resource utilization and minimizing waste.

Related: Is Data Science Overhyped?

The power of data science in transforming industries is undeniable, as demonstrated by these 25 compelling case studies. Through the strategic application of machine learning, predictive analytics, and AI, companies are solving complex challenges and gaining a competitive edge. The insights gleaned from these cases highlight the critical role of data science in enhancing decision-making processes, improving operational efficiency, and elevating customer satisfaction. As we look to the future, the role of data science is set to grow, promising even more innovative solutions and smarter strategies across all sectors. These case studies inspire and serve as a roadmap for harnessing the transformative power of data science in the journey toward digital transformation.

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Top 10 real-world 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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• Data Science

Case Study: Modularizing a Package | R-bloggers

• Read original article here

“Would it be possible…?”, “I think it would be nice if…”, “Can you implement…?”. User feedback is a reliable source of valuable ideas for package improvement, but it’s easy to get too eager and implement everything the users want, especially when you’ve only started making a name for yourself. I and Dominik have fallen victim to that too. Our package deepdep was initially created as a university project. There were four of initial authors: the two of us and our colleagues, Hubert and Szymon. The teacher had a use case in mind (creating layered dependency plots) and we wanted to implement all that could get us a good grade. So we added everything more or less related to dependency plots that we could implement at the time. Fast forward a few years and a question came up regarding using a repository mirror other than the CRAN mirror we hardcoded. The function in question? . We’ve exchanged a few messages and it turned out that only served as a safeguard against using other mirrors within itself. In fact, the whole backend that downloads the data needed a rewrite. tried to provide a unified API for retrieving dependencies from different sources and did the same for DESCRIPTION… but they ended up messy and counterintuitive. The user could only get data from CRAN, CRAN with Bioconductor, or from the local library that was first in . No handling Bioconductor only, no using CRAN as a fallback for local library, no querying other repositories (e.g. R-universe). The functions had to grow a lot if we wanted them to be as universal as possible. The other issue made us realize that the plotting feature is optional to some; that the key feature is collecting dependency data in a table, which only needs a small fraction of dependencies (httr and jsonlite). We moved a lot of previous Imports to Suggests (ggplot2, ggraph, graphlayouts, igraph, and scales), lightening deepdep significantly… but that’s a topic for another post. It was time to ask ourselves: “what does «deepdep» mean to us?”. The answer was: “it’s a package that helps with analyzing and visualizing hierarchy of package dependencies”. No more, no less. The functions that extracted dependencies of a package or a DESCRIPTION file were just tools to accomplish that goal. They were exported because “we couldn’t let a good function go to waste”, not because they presented a functionality we wanted to provide. If a user would want to use one of these, they’d have to install the whole deepdep; it would be like installing ggplot2 for and instead of plotting. The time has come for a separation. The idea is to modularize – to allow the user to install what they want. If they want to retrieve a list of dependencies for one package or a list of available packages in a repository, they should not need to install deepdep. They should be able to install a separate package that deepdep imports: woodendesc. This package is a complete rewrite of these functionalities, but much more flexible and much more potent. To show the difference, this is how you’d get packages available on CRAN and Bioconductor in old deepdep: You can’t do much more than that. The only other option is to get locally available packages. This is the signature of the function: But woodendesc goes three steps further. There are functions for many different sources of packages, each of them optimized for minimal network usage and maximal cache utilization: # Simple CRAN extractor woodendesc::wood_cran_packages() # Allows `release` parameter to query old releases woodendesc::wood_bioc_packages() # The user can specify different paths woodendesc::wood_local_packages() # Functions below not possible in old deepdep: woodendesc::wood_runiverse_packages("turtletopia") woodendesc::wood_url_packages("http://www.omegahat.net/R") woodendesc::wood_core_packages() And if you’d want a single function like ? Easy, just call with specified repos (by default it only queries CRAN): You can do it with all the sources above and even pass most parameters: Now, you can see why’d we separate these functionalities into a new package. There are analogous functions for version codes and dependencies (about 20 functions total!) and they’d overwhelm the original intent of deepdep. Adding woodendesc as a dependency of a deepdep costs nothing because the alternative is to include this code within deepdep itself – so it’d have to be tested and maintained anyways. But sometimes modularizing is a bit extra. A new package is not always the answer If you have a function if your package that doesn’t fit the general idea, don’t rush to move it into a separate package. There’s one important question to ask before: “Will it be used by anything else than my package?” And don’t be proactive here. If your answer is: “not right now, but perhaps in the future…”, just wait for the future. Keep the function in the package until the time comes and simply remove or deprecate it then (depending on how popular it gets). There’s one such functionality in deepdep: and . Analyzing download statistics is not exactly the goal of deepdep, but there’s no point in making it into a separate package; these two don’t introduce any new dependencies nor do they crowd the namespace. And no one expressed any interest in having it separate from deepdep yet. Besides, nobody creates a package around a single function. You might have noticed that woodendesc consists of functions that served as a backbone of deepdep while querying and plotting download statistics are more of an extension. There’s one package we’ve created that was planned to be extended since the beginning: tidysq. It’s a package that compresses biological sequences (e.g. DNA/RNA) by coding each letter with fewer bits (3 in DNA/RNA case). We’ve included a few basic operations like reversing, subsetting, translating to amino acids, and reading a FASTA file – the most common file format for biological sequences. We’ve intentionally omitted many more advanced functions, though. Why? Because there are countless functions and algorithms we could implement and that’d make tidysq huge. Instead, we’d gone the route of modularization. The idea is to have tidysq with the base functionality and several packages depending on tidysq, oriented towards certain aspects of working with biological sequences. For example, if we were to create a set of and functions for various formats like FASTQ or BAM/SAM, we’d place it in a separate package that’d have tidysq in Depends (and LinkingTo) fields. We’d call it something like “tidysqfiles” to signify that it’s an extension to tidysq. (We may or may not be working on such a package.) If you want to see a real-life example of a package ecosystem, see mlr3 and mlr3verse.

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• A Blog From a Human-engineer-being http://www.erogol.com/ (RSS)
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Case Study Project

“Case Study Project” is an initiative that looks to promote and facilitate the usage of R programming language for data manipulation, data analysis, programming, and computational aspects of statistical topics in authentic research applications. Specifically, it looks to solve various research problems using the extensive resource database of different packages available in R, related to mathematics or analytics. Furthermore, it aims to provide students and researchers with guidance in the process of approaching and solving a research problem or a complete data analysis project using R. 

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12 Data Science Case Studies: Across Various Industries

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

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. Almost every industry uses data science in some way. You can learn more about data science fundamentals in this Data Science course content .

Let’s look at the top 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.

List of Data Science Case Studies 2024

• 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
• Urban Planning and Smart Cities:  Traffic management in smart cities such as Pune and Bhubaneswar
• Agricultural Yield Prediction:  Farmers Edge in Canada uses Data science to help farmers improve their produce
• Transportation Industry:  Uber optimizes their ride-sharing feature and track the delivery routes through data analysis
• Environmental Industry:  NASA utilizes Data science to predict potential natural disasters, World Wildlife analyzes deforestation to protect the environment

Top 12 Data Science Case Studies

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. Data science case studies reveal how 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 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.  

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 case studies 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.  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.  

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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Teaching data science in the cloud.

Posted on April 3, 2022 by RStudio | Open source & professional software for data science teams on RStudio in R bloggers | 0 Comments

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Data science and programming languages like R and Python are some of the most in-demand skills in the world. RStudio Cloud is a simple but powerful solution for teaching and learning analytics at scale. RStudio Cloud solves many of the technical and financial challenges associated with teaching data science. It’s also a joy to use for professors, students, and IT administrators.

Pete Knast hosted the RStudio Cloud Live Series to discuss approaches and tools for teaching in the cloud. These webinars were presented by Dr. Brian Anderson, Dr. Patricia Menéndez, and Dr. Mine Çetinkaya-Rundel.

We received many great pedagogical questions during the sessions. We also received inquiries about RStudio Cloud’s functionality and implementation. Below, we share insights from our presenters.

• Implementing teaching in the cloud
• Teaching data science

Using RStudio Cloud

We also provide more information on RStudio Cloud for you to explore.

• Learn more about RStudio Cloud
• Resources and links

Implementing Teaching in the Cloud

How do I get started with talking to my college dean and IT about the possibility of hosting RStudio Cloud? What was the budget approval process?

Dr. Menéndez: First, I wanted to see what RStudio Cloud could offer to the units I am teaching. I wrote a pros/cons document with the reasons and why it was worth investing in it. I discussed the functionality of the tool with the RStudio team. We also had a conversation with the IT team and we decided to use the server from RStudio Cloud.

I discussed with the RStudio team to see if the budget was aligned to what I needed, and then with the department head and the department manager. Finally, the department manager discussed the budget with the RStudio team.

I wonder about the partnership between administration, IT, and faculty. Our IT has limited ability to implement remote and cloud resources.

Dr. Anderson: This is where cloud solutions can be helpful. By minimizing the direct IT support on student machines, or in a lab space, cloud solutions should not add significantly to IT workload. Of course, how the school deploys the cloud solution is a variable in that workload, as is the extent to which the cloud solution integrates well with other IT platforms used for instruction.

Ideally, a cloud solution would be familiar to both faculty and IT, in the sense that the cloud version of the tool has a similar look and functionality as its desktop version, if applicable. Support agreements tied to licensing can also be a possibility, along with a robust help area for students to seek solutions to common software challenges. It is, though, critical for a new initiative to be either led by faculty or otherwise have significant faculty support.

Can RStudio Cloud integrate with my school’s existing authentication setup?

Pete Knast: Yes, depending on your school’s Single Sign-On setup, we can likely integrate with it. We’ve integrated with popular ones like Shibboleth, Google Auth, and SAML. If you’re using something else, let us know.

Could we see a demo of the rscloud package?

Pete Knast: The rscloud package is an API wrapper for the rstudio.cloud service. The initial release includes APIs for managing space memberships and listing projects within a space. You can see a demo of the package on our RStudio Cloud YouTube playlist .

Teaching Data Science

Do students generally have any prior programming experience? Do you do a primer first with GitHub?

Dr. Menéndez: The students generally need to learn both R and GitHub. I teach them to use Git through both the command line interface and through RStudio. If you learn through the command-line interface, then you can use Git with other programming languages like Matlab or Python. As part of the course, students learn how to create a repository and how to store it in GitHub. I use RStudio Cloud for the first few weeks to get them familiar with R and RStudio. Once we start learning Git, I transition them to using RStudio installed locally on their machines.

I have found that teaching Markdown is the most difficult for students. How do you approach this?

Dr. Menéndez: Students struggle at the beginning with Markdown. First, I teach them about R and reproducibility. Then, we talk about integrating code with text to create a report. I introduce this sequentially so they understand why they need markdown and what the benefits are. In the reproducibility unit, we follow the same structure so that students without a lot of R knowledge are able to create reproducible reports using R and R Markdown while learning how to use Git and GitHub.

In the past, I’ve had students pick some dataset and by the end of the semester create a report based on their chosen dataset. Do you use the same dataset for your entire course?

Dr. Menéndez: For each lecture and assignment, we use different datasets. We may use the same dataset in different contexts, but in general, we use different real-life examples each time.

Can you speak more to teaching business students analytics? We are developing similar programs and need to differentiate them from data science.

Dr. Anderson: From my perspective, what differentiates business analytics from data science is context and workflow. When we think about using analytics to solve a business problem or inform a decision, communication is paramount to this process. What makes analytics useful in business is the ability to influence and persuade, which requires the analyst to have a deep understanding of the business context and the ability to communicate key insights in a clear and compelling way. As such, communication exists as coequal in workflow importance to, for example, modeling and data generation. Further, because of the nature of how we communicate in a business context, data visualization skills take on greater salience. To be clear, data science workflows also include context, communication, and visualizations. My argument is that a business analytics curriculum will place different emphases on workflow elements than would, potentially, a data science curriculum.

On the financial constraints of adopting private solutions, would you think it possible to teach how to do, e.g., financial analysis with R? What about employment opportunities with this route?

Dr. Anderson: Yes, I think R, particularly with additional tools like RStudio, could be extended to teach business fundamentals that are typically taught using MS Excel. The challenge, however, will be ensuring that business graduates, particularly in finance and accounting, also have excellent MS Excel skills. In that sense, leveraging R is best positioned as a complement to, and not a replacement of, MS Excel.

Does it make sense to use RStudio Cloud for the entirety of the course or to have students transition to local machines?

Dr. Çetinkaya-Rundel: That is a good question and it depends on the goals of your course. As someone who generally teaches introductory courses, I want students to understand that the “cloud” is not an esoteric thing — that it is actually somebody else’s machine. We want students to understand what it means for things to live on the cloud so if they start working on a project with sensitive data, they know they shouldn’t just upload it to RStudio Cloud without first making sure that it is okay.

If the goals of your course include software installation, then it makes sense to transition. One benefit of teaching installation later is that students are not both new to R and installation, so they can distinguish between an R error and an error due to their setup.

When people go on to do things past their university life, chances are they will continue to work in the cloud. A lot of academic computing happens on computing clusters. This notion of doing things on the cloud eases onboarding, but it is not just an unrealistic “baby steps” solution.

Dr. Menéndez: The students with no coding experience tend to be very nervous at the beginning. In addition to learning R, RStudio, and reproducibility, they also need to learn the command line interface and Git/GitHub. Using RStudio Cloud in the first four weeks makes it easy because students feel safe opening projects, running code, etc. They learn about version control and installing packages. After a few weeks, they feel very confident. Then, we slowly transition to the desktop.

Can you speak to your use of the functionality that allows you as the instructor to access the projects of the students?

Dr. Menéndez: This is a great feature from RStudio Cloud. I can click into a feature that lets me see all of the students’ projects. I can search for a specific student in the members’ space and then click on their project to open it. I’d prefer students create a reproducible example when they need help, but this gives me the flexibility to see what’s happening if a student is really stuck.

Is there a way to distribute data files to students without setting up a project?

Dr. Çetinkaya-Rundel: If you are not setting up a project, there isn’t necessarily an RStudio Cloud-specific solution. If you have access to a place to host your dataset, such as a GitHub repository, then you can read the files using the URL. If the goal is to teach students how to move files, then I provide instructions on how to download a dataset onto their computer and then upload it into RStudio Cloud.

How does RStudio Cloud scale (say, when you have a class of 30 students versus a class of 300)?

Dr. Menéndez: RStudio Cloud scales very well. You share the RStudio Cloud space for a unit with a link, so it doesn’t matter if you have 30 students or 300 students. They click the link to enter the workspace. The issue is when you are getting questions from 300 students, but that is more about content than the technology. For that, we open several Zoom channels where the students can seek help for technical issues if they arise. I communicate with my teaching associates via Slack so that the teaching team is all connected.

How do you handle grading?

Dr. Çetinkaya-Rundel: There isn’t a grading feature in RStudio Cloud but it is a feature request. If you’re not having students submit their work elsewhere (e.g., GitHub, or your school’s learning management system), I would recommend going into each project, creating a file for the feedback or leaving it inline, and recording grades in a separate CSV file you can upload to wherever you store grades.

Dr. Menéndez: I do not use something that integrates RStudio Cloud with LMSs. Students work on their assignments using RStudio Cloud and then they download their R studio projects into a ZIP folder. Afterwards, they upload the folder into the LMS system and share their project link. We mark the RStudio project and the knitted files and I provide students feedback via the LMS system.

Learn More About RStudio Cloud

Is it true you can access Jupyter Notebooks in RStudio Cloud?

Yes, this is still in Beta but you can request access if you have a paid plan. We will be transitioning to general availability later this year. Even if you do not have a paid plan and are interested in trying it out, send us an email and we can set you up.

How does RStudio Cloud differ from RStudio Server? Are there any additional benefits that RStudio Cloud may provide?

RStudio Cloud has a few features that don’t exist in other RStudio offerings, like the ability to create workspaces or assignments. RStudio Cloud is also a hosted SaaS offering so RStudio handles the infrastructure for you.

Where can one go to start experimenting with RStudio Cloud in our classrooms? Who should I email for practical steps?

You can send questions to [email protected] . To start experimenting, sign up for a free account here: https://rstudio.cloud/plans/free .

Is there an educator plan? I’m wondering how a large course (100+) can use RStudio Cloud given the limitations of workspaces and hrs/month.

The various paid tiers which are discounted heavily for educators don’t have any limitations on workspaces or hours. We have numerous degree-granting institutions that use RStudio Cloud for courses that cover hundreds and even thousands of students.

I’ve seen discounted academic licenses, but not free. Is this accurate?

There is a free license that anyone can use, even non-academics. There are also multiple types of discounts for academics depending on your use case, such as if you are an instructor at a degree-granting institution, academic researcher, TA, or student.

I was told last year by someone from RStudio that they were working on a collaboration feature in the RStudio Cloud. Is this feature going to be released soon?

Yes, we are hoping to have true collaborative editing much like you will find in a Google Doc or other RStudio offerings by the end of Q1 2022.

How does RStudio Cloud differ from RStudio Server? Are there limitations on the use of libraries or any other add-in that you would normally use on the desktop or server version? Are there any additional benefits that RStudio Cloud may provide over RStudio Server?

RStudio Cloud is different in that it offers additional capabilities so there are no real limitations. You can read more here:

• FAQ for Leadership and IT
• RStudio Cloud & RStudio Workbench: Academic Teaching/Research IT Overview

Resources and Links

We have a lot more to share:

• Visit the RStudio Cloud product page .
• Sign up for a free account .
• Book a call with an RStudio Cloud Expert .

Watch the presenters’ full recordings:

• Leveraging the Cloud for Analytics Instruction at Scale: Challenges and Opportunities by Dr. Brian Anderson
• Find Dr. Menéndez slides on GitHub .
• RStudio Cloud Demo with Dr. Mine Çetinkaya-Rundel.

Educational resources

Find resources for the use of RStudio in education:

• RStudio Cloud Primers with interactive courses in data science basics.
• RStudio Cloud cheat sheets for learning and using your favorite R packages and the RStudio IDE.
• RStudio Cloud YouTube playlist with how-to’s for creating a shared space, project types, and more.

Read past blog posts on RStudio Cloud:

• Do, Share, Teach, and Learn Data Science with RStudio Cloud
• Learning Data Science with RStudio Cloud: A Student’s Perspective

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