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Errata
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About Principles of Data Science
Summary
Principles of Data Science is intended as introductory material for a one- or two-semester course on data science. It is appropriate for undergraduate students interested in the rapidly growing field of data science; this may include data science majors, data science minors, or students concentrating in business, finance, health care, engineering, the sciences, or a number of other fields where data science has become critically important. The material is designed to prepare students for future coursework and career applications in a data science–related field. It does not assume significant prior coding experience, nor does it assume completion of more than college algebra. The text provides foundational statistics instruction for students who may have a limited statistical background.
Coverage and Scope
Principles of Data Science emphasizes the use of Python code in relevant data science applications. Python provides a versatile programming language with libraries and frameworks for data manipulation, analysis, and machine learning. The book begins with an introduction to Python and presents Python libraries, algorithms, and functions as they are needed throughout. In occasional, focused instances, the authors also use Excel to illustrate the basic manipulation of data using functions, formulas, and tools for calculations, visualization, and financial analysis. R, a programming language used most often for statistical modeling, is briefly described and then summarized and applied to relevant examples in a book appendix. Excel and Python summaries are also provided in appendices at the end of the book.
The table of contents (TOC) is divided into ten chapters, organized in four units, intuitively following the standard data science cycle. The four units are:
Unit 1: Introducing Data Science and Data Collection
Unit 2: Analyzing Data Using Statistics
Unit 3: Predicting and Modeling Using Data
Unit 4: Maintaining a Professional and Ethical Data Science Practice
The learning objectives and curriculum of introductory data science courses vary, so this textbook aims to provide broader and more detailed coverage than an average single-semester course. Instructors can choose which chapters or sections they want to include in their particular course.
To enable this flexibility, chapters in this text can be used in a self-contained manner, although most chapters do cross-reference sections and chapters that precede or follow. More importantly, the authors have taken care to build topics gradually, from chapter to chapter, so instructors should bear this in mind when considering alternate sequence coverage.
Unit 1: Introducing Data Science and Data Collection starts off with Chapter 1’s explanation of the data science cycle (data collection and preparation, data analysis, and data reporting) and its practical applications in fields such as medicine, engineering, and business. Chapter 1 also describes various types of datasets and provides the student with basic data summary tools from the Python pandas library. Chapter 2 describes the processes of data collection and cleaning and the challenges of managing large datasets. It previews some of the qualitative ethical considerations that Chapters 7 and 8 later expand on.
Unit 2: Analyzing Data Using Statistics forms a self-contained unit that instructors may assign on a modular, more optional basis, depending on students’ prior coursework. Chapter 3 focuses on measures of center, variation, and position, leading up to probability theory and illustrating how to use Python with binomial and normal distributions. Chapter 4 goes deeper into statistical analysis, demonstrating how to use Python to calculate confidence intervals, conduct hypothesis tests, and perform correlation and regression analysis.
The three chapters in Unit 3: Predicting and Modeling Using Data form the core of the book. Chapter 5 introduces students to the concept and practical applications of time series. Chapter 5 provides focused examples of both Python and Excel techniques useful in forecasting time series, analyzing seasonality, and identifying measures of error. Chapter 6 starts with distinguishing supervised vs. unsupervised machine learning and then develops some common methods of data classification, including logistic regression, clustering algorithms, and decision trees. Chapter 6 includes Python techniques for more sophisticated statistical analyses such as regression with bootstrapping and multivariable regression. Finally, Chapter 6 refers back to the topics of data mining and big data introduced in Chapter 2.
Chapter 7 is a pedagogically rich chapter, with a balance of quantitative and qualitative content, covering the role of neural networks in deep learning and applications in large language models. The first four sections discuss the topics of neural networks (standard, recurrent, and convolutional), backpropagation, and deep learning. The real-life application of classifying handwritten numerals is used as an example. The last section dives into the important and rapidly changing technology of natural language processing (NLP), large language models (LLMs), and artificial intelligence (AI). While in-depth coverage of these evolving subjects is beyond the scope of this textbook, the pros/cons, the examples from technical and artistic applications, and the online resources provided in this section all serve as a good starting point for classroom discussion. This topic also naturally segues into the broader professional responsibility discussed in Chapter 8.
The final chapters in Unit 4: Maintaining a Professional and Ethical Data Science Practice help the student apply and adjust the specific techniques learned in the previous chapters to the real-life data analysis, decision-making, and communication situations they will encounter professionally. Chapter 8 emphasizes the importance of ethical considerations along each part of the cycle: data collection; data preparation, analysis, and modeling; and reporting and visualization. Coverage of the issues in this chapter makes students aware of the subjective and sensitive aspects of privacy and informed consent at every step in the process. At the professional level, students learn more about the evolving standards for the relatively new field of data science, which may differ among industries or between the United States and other countries.
Chapter 9 circles back to some of the statistical concepts introduced in Chapters 3 and 4, with an emphasis on clear visual analysis of data trends. Chapter 9 provides a range of Python techniques to create boxplots and histograms for univariate data; to create line charts and trend curves for time series; to graph binomial, Poisson, and normal distributions; to generate heatmaps from geospatial data; and to create correlation heatmaps from multidimensional data.
Chapter 10 brings the student back to the practical decision-making setting introduced in Chapter 1. Chapter 10 helps the student address how to tailor the data analysis and presentation to the audience and purpose, how to validate the assumptions of a model, and how to write an effective executive summary.
The four Appendices (A–D) provide a practical set of references for Excel commands and commands for R statistical software as well as Python commands and algorithms. Appendix A uses a baseball dataset from Chapter 1 to illustrate basic Microsoft® Excel® software commands for manipulating, analyzing, summarizing, and graphing data. Appendix B provides a brief overview of data analysis with the open-source statistical computing package R, using a stock price example. Appendix C lists the approximately 60 Python algorithms used in the textbook, and Appendix D lists the code and syntax for the approximately 75 Python functions demonstrated in the textbook. Both Appendices C and D are organized in a tabular format, in consecutive chapter order, hyperlinked to the first significant use of each Python algorithm and function. (Instructors may find Appendices C and D especially useful in developing their teaching plan.)
Pedagogical Foundation
Because this is a practical, introductory-level textbook, math equations and code are presented clearly throughout. Particularly in the core chapters, students are introduced to key mathematical concepts, equations, and formulas, often followed by numbered Example Problems that encourage students to apply the concepts just covered in a variety of situations. Technical illustrations and Python code feature boxes build on and supplement the theory. Students are encouraged to try out the Python code from the feature boxes in the Google Colaboratory (Colab) platform.
The authors have included a diverse mix of data types and sources for analysis, illustration, and discussion purposes. Some scenarios are fictional and/or internal to standard Python libraries, while other datasets come from external, real-world sources, both corporate and government (such as Federal Reserve Economic Data (FRED), Statista, and Nasdaq). Most scenarios are either summarized in an in-line table, or have datasets provided in a downloadable student spreadsheet for import as a .CSV file (Chapter 1 also discusses the .JSON format) and/or with a hyperlink to the external source. Some examples focus on scientific topics (e.g., the “classic” Iris flower dataset, annual temperature changes), while other datasets reflect phenomena with more nuanced socioeconomic issues (gender-based salary differences, cardiac disease markers in patients).
While the book’s foundational chapters illustrate practical “techniques and tools,” the more process-oriented chapters iteratively build on and emphasize an underlying framework of professional, responsible, and ethical data science practice. Chapter 1 refers the student to several national and international data science organizations that are developing professional standards. Chapter 2 emphasizes avoiding bias in survey and sample design. Chapter 8 discusses relevant privacy legislation. For further class exploration, Chapters 7 and 8 include online resources on mitigating bias and discrimination in machine learning models, including related Python libraries such as HolisticAI and Fairlens. Chapter 10 references several executive dashboards that support transparency in government.
The Group Projects at the end of each chapter encourage students to apply the techniques and considerations covered in the book using either datasets already provided or new data sources that they might receive from their instructors or in their own research. For example, project topics include the following: collecting data on animal extinction due to global warming (Chapter 2), predicting future trends in stock market prices (Chapter 5), diagnosing patients for liver disease (Chapter 7), and analyzing the severity of ransomware attacks (Chapter 8).
Key Features
The key in-chapter features, depending on chapter content and topics, may include the following:
- Learning Outcomes (LOs) to guide the student’s progress through the chapter
- Example Problems, demonstrating calculations and solutions in-line
- Python code boxes, providing sample input code for and output from Google Colab
- Note boxes providing instructional tips to help with the practical aspects of the math and coding
- Data tables from a variety of social science and industry settings
- Technical charts and heatmaps to visually demonstrate code output and variable relationships
- Exploring Further boxes, with additional resources and online examples to extend learning
- Mathematical formulas and equations
- Links to downloadable spreadsheet containing key datasets referenced in the chapter for easy manipulation of data
End-of-chapter (EOC) elements, depending on chapter content and topics, may include the following:
- Key Terms
- Group Projects
- Chapter Review Questions
- Critical Thinking Questions
- Quantitative Problems
Answers [and Solutions] to Questions in the Book
The student-facing Answer Key at the end of the book provides the correct answer letter and text for Chapter Review questions (multiple-choice). An Instructor Solution Manual (ISM) will be available for verified instructors and downloadable from the restricted OpenStax Instructor Resources web page, with detailed solutions to Quantitative Problems, sample answers for Critical Thinking questions, and a brief explanation of the correct answer for Chapter Review questions. (Sample calculations, tables, code, or figures may be included, as applicable.) An excerpt of the ISM, consisting of the solutions/sample answers for the odd-numbered questions only, will also be available as a Student Solution Manual (SSM), downloadable from the public OpenStax Student Resources web page. (Answers to the Group Projects are not provided, as they are integrative, exploratory, open-ended assignments.)
About the Authors
Senior Contributing Authors
Dr. Shaun V. Ault, Valdosta State University. Dr. Ault joined the Valdosta State University faculty in 2012, serving as Department Head of Mathematics from 2017 to 2023 and Professor since 2021. He holds a PhD in mathematics from The Ohio State University, a BA in mathematics from Oberlin College, and a Bachelor of Music from the Oberlin Conservatory of Music. He previously taught at Fordham University and The Ohio State University. He is a Certified Institutional Review Board Professional and holds membership in the Mathematical Association of America, American Mathematical Society, and Society for Industrial and Applied Mathematics. He has research interests in algebraic topology and computational mathematics and has published in a number of peer-reviewed journal publications. He has authored two textbooks: Understanding Topology: A Practical Introduction (Johns Hopkins University Press, 2018) and, with Charles Kicey, Counting Lattice Paths Using Fourier Methods. Applied and Numerical Harmonic Analysis, Springer International (2019).
Dr. Soohyun Nam Liao, University of California San Diego. Dr. Liao joined the UC San Diego faculty in 2015, serving as Assistant Teaching Professor since 2021. She holds PhD and MS degrees in computer science and engineering from UC San Diego and a BS in electronics engineering from Seoul University, South Korea. She previously taught at Princeton University and was an engineer at Qualcomm Inc. She focuses on computer science (CS) education research as a means to support diversity and equity (DEI) in CS programs. Among her recent co-authored papers is, with Yunyi She, Korena S. Klimczak, and Michael E. Levin, “ClearMind Workshop: An ACT-Based Intervention Tailored for Academic Procrastination among Computing Students,” SIGCSE (1) 2024: 1216-1222. She has received a National Science Foundation grant to develop a toolkit for A14All (data science camps for high school students).
Larry Musolino, Pennsylvania State University. Larry Musolino joined the Penn State, Lehigh Valley, faculty in 2015, serving as Assistant Teaching Professor of Mathematics since 2022. He received an MS in mathematics from Texas A&M University, a MS in statistics from Rochester Institute of Technology (RIT), and MS degrees in computer science and in electrical engineering, both from Lehigh University. He received his BS in electrical engineering from City College of New York (CCNY). He previously was a Distinguished Member of Technical Staff in semiconductor manufacturing at LSI Corporation. He is a member of the Penn State OER (Open Educational Resources) Advisory Group and has authored a calculus open-source textbook. In addition, he co-authored an open-source Calculus for Engineering workbook. He has contributed to several OpenStax textbooks, authoring the statistics chapters in the Principles of Finance textbook and editing and revising Introductory Statistics, 2e, and Introductory Business Statistics, 2e.
The authors wish to express their deep gratitude to Developmental Editor Ann West for her skillful editing and gracious shepherding of this manuscript. The authors also thank Technical Editor Dhawani Shah (PhD Statistics, Gujarat University) for contributing technical reviews of the chapters throughout the content development process.
Contributing Authors
Wisam Bukaita, Lawrence Technological University
Aeron Zentner, Coastline Community College
Reviewers
Wisam Bukaita, Lawrence Technological University
Drew Lazar, Ball State University
J. Hathaway, Brigham Young University-Idaho
Salvatore Morgera, University of South Florida
David H. Olsen, Utah Tech University
Thomas Pfaff, Ithaca College
Jian Yang, University of North Texas
Aeron Zentner, Coastline Community College
Additional Resources
Student and Instructor Resources
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We realize that students benefit when academic integrity ground rules are established early in the course. To that end, OpenStax has created an interactive to aid with academic integrity discussions in your course.
At OpenStax we are also developing resources supporting authentic learning experiences and assessment. Please visit this book’s page for updates. For an in-depth review of academic integrity strategies, we highly recommend visiting the International Center of Academic Integrity (ICAI) website at https://academicintegrity.org/.
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