The Art of Data Science (The Book + Lecture Videos)
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The Art of Data Science

A Guide for Anyone Who Works with Data

About the Book

Data analysis is a difficult process largely because few people can describe exactly how to do it. It's not that there aren't any people doing data analysis on a regular basis. It's that the process by which we state a question, explore data, conduct formal modeling, interpret results, and communicate findings, is a difficult process to generalize and abstract. Fundamentally, data analysis is an art. It is not yet something that we can easily automate. Data analysts have many tools at their disposal, from linear regression to classification trees to random forests, and these tools have all been carefully implemented on computers. But ultimately, it takes a data analyst—a person—to find a way to assemble all of the tools and apply them to data to answer a question of interest to people. 

This book writes down the process of data analysis with a minimum of technical detail. What we describe is not a specific "formula" for data analysis, but rather is a general process that can be applied in a variety of situations. Through our extensive experience both managing data analysts and conducting our own data analyses, we have carefully observed what produces coherent results and what fails to produce useful insights into data. This book is a distillation of our experience in a format that is applicable to both practitioners and managers in data science. 

If you are interested in obtaining a printed copy of this book, you can purchase one at Lulu.

The package containing the lecture videos offers short commentaries on each of the chapters and contains addtional explanatory material for each of the topics. In addition there is some material in the lectures that is not included in the book. 

About the Authors

Roger D. Peng
Roger D. Peng

Roger D. Peng is a Professor of Statistics and Data Sciences at the University of Texas, Austin. Previously, he was Professor of Biostatistics at the Johns Hopkins Bloomberg School of Public Health. His research focuses on the development of statistical methods for addressing environmental health problems and on developing tools for doing better data analysis. He is the author of the popular book R Programming for Data Science and 10 other books on data science and statistics. He is also the co-creator of the Johns Hopkins Data Science Specialization, the Simply Statistics blog where he writes about statistics for the public, the Not So Standard Deviations podcast with Hilary Parker, and The Effort Report podcast with Elizabeth Matsui. Roger is a Fellow of the American Statistical Association and is the recipient of the Mortimer Spiegelman Award from the American Public Health Association, which honors a statistician who has made outstanding contributions to public health. He can be found on Twitter and GitHub at @rdpeng.

Roger D. Peng

Episode 16

Elizabeth Matsui
Elizabeth Matsui

Elizabeth Matsui is a Professor of Population Health and Pediatrics at Dell Medical School at UT Austin and an Adjunct Professor of Pediatrics at Johns Hopkins University. She is also a practicing pediatric allergist/immunologist and epidemiologist and directs a research program focused on environmental exposures and lung health. Elizabeth can be found on Twitter @elizabethmatsui.


The Book + Lecture Videos

This package includes the book and lecture video files. The videos and chapters are aligned so that together they make an ideal self-learning curriculum in which students interested in data science can pair video lectures with reading material. The videos complement the reading material by extending concepts covered in the book and by providing visual and auditory presentation of the concepts. This self-guided curriculum can be covered at any pace and the completion of material should provide students with a solid foundation for thinking about the data science process. The complete package should be of interest to students interested in doing their own data analyses and to people who need to manage data science teams.

  • PDF

  • EPUB

  • WEB

  • English

Minimum price
Suggested price
The Book
  • PDF

  • EPUB

  • WEB

  • English

Minimum price
Suggested price

About the Contributors

Maggie Matsui
Maggie Matsui
Maggie Matsui provided all of the artwork for this book.

Table of Contents

  • 1. Data Analysis as Art
  • 2. Epicycles of Analysis
    • 2.1 Setting the Scene
    • 2.2 Epicycle of Analysis
    • 2.3 Setting Expectations
    • 2.4 Collecting Information
    • 2.5 Comparing Expectations to Data
    • 2.6 Applying the Epicycle of Analysis Process
  • 3. Stating and Refining the Question
    • 3.1 Types of Questions
    • 3.2 Applying the Epicycle to Stating and Refining Your Question
    • 3.3 Characteristics of a Good Question
    • 3.4 Translating a Question into a Data Problem
    • 3.5 Case Study
    • 3.6 Concluding Thoughts
  • 4. Exploratory Data Analysis
    • 4.1 Exploratory Data Analysis Checklist: A Case Study
    • 4.2 Formulate your question
    • 4.3 Read in your data
    • 4.4 Check the Packaging
    • 4.5 Look at the Top and the Bottom of your Data
    • 4.6 ABC: Always be Checking Your “n”s
    • 4.7 Validate With at Least One External Data Source
    • 4.8 Make a Plot
    • 4.9 Try the Easy Solution First
    • 4.10 Follow-up Questions
  • 5. Using Models to Explore Your Data
    • 5.1 Models as Expectations
    • 5.2 Comparing Model Expectations to Reality
    • 5.3 Reacting to Data: Refining Our Expectations
    • 5.4 Examining Linear Relationships
    • 5.5 When Do We Stop?
    • 5.6 Summary
  • 6. Inference: A Primer
    • 6.1 Identify the population
    • 6.2 Describe the sampling process
    • 6.3 Describe a model for the population
    • 6.4 A Quick Example
    • 6.5 Factors Affecting the Quality of Inference
    • 6.6 Example: Apple Music Usage
    • 6.7 Populations Come in Many Forms
  • 7. Formal Modeling
    • 7.1 What Are the Goals of Formal Modeling?
    • 7.2 General Framework
    • 7.3 Associational Analyses
    • 7.4 Prediction Analyses
    • 7.5 Summary
  • 8. Inference vs. Prediction: Implications for Modeling Strategy
    • 8.1 Air Pollution and Mortality in New York City
    • 8.2 Inferring an Association
    • 8.3 Predicting the Outcome
    • 8.4 Summary
  • 9. Interpreting Your Results
    • 9.1 Principles of Interpretation
    • 9.2 Case Study: Non-diet Soda Consumption and Body Mass Index
  • 10. Communication
    • 10.1 Routine communication
    • 10.2 The Audience
    • 10.3 Content
    • 10.4 Style
    • 10.5 Attitude
  • 11. Concluding Thoughts
  • About the Authors

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