Principles of Data Science

Principles of Data Science is intended to support one- or two-semester courses in data science. It is appropriate for data science majors and minors as well as students concentrating in business, finance, health care, engineering, the sciences, and a number of other fields where data science has become critically important.

The authors have included a diverse mix of scenarios, examples, and data types for analysis and discussion purposes. These include both fictional contexts and real-world sources, such as the Federal Reserve Economic Database and Nasdaq. Data sets focus on a range of topics: business, science, social sciences. Applications include healthcare, physical sciences, demographics, policy, and finance. Data ethics and the emergence of artificial intelligence are covered deeply – both in their own chapters and as consistent threads throughout the course material.

The authors and contributors have developed rich in-chapter example problems and extensive practice exercises that encourage students to apply concepts in a variety of situations. Technical illustrations and Python code support and supplement the principles and theory. The text also includes direct links to downloadable data sets and Python code, as well as guidance on how to use them.

Senior Contributing Authors
- Dr. Shaun V. Ault, Valdosta State University
- Dr. Soohyun Nam Liao, University of California San Diego
- Larry Musolino, Pennsylvania State University

Contributing Authors
- Wisam Bukaita, Lawrence Technological University
- Aeron Zentner, Coastline Community College

Preface

Unit 1 Introducing Data Science and Data Collection

Chapter 1 What Are Data and Data Science?
o Introduction
o 1.1 What Is Data Science?
o 1.2 Data Science in Practice
o 1.3 Data and Datasets
o 1.4 Using Technology for Data Science
o 1.5 Data Science with Python
o Key Terms
o Group Project
o Chapter Review
o Critical Thinking
o Quantitative Problems
o References

Chapter 2 Collecting and Preparing Data
o Introduction
o 2.1 Overview of Data Collection Methods
o 2.2 Survey Design and Implementation
o 2.3 Web Scraping and Social Media Data Collection
o 2.4 Data Cleaning and Preprocessing
o 2.5 Handling Large Datasets
o Key Terms
o Group Project
o Critical Thinking
o References
Unit 2 Analyzing Data Using Statistics

Chapter 3 Descriptive Statistics: Statistical Measurements and Probability Distributions
o Introduction
o 3.1 Measures of Center
o 3.2 Measures of Variation
o 3.3 Measures of Position
o 3.4 Probability Theory
o 3.5 Discrete and Continuous Probability Distributions
o Key Terms
o Group Project
o Quantitative Problems

Chapter 4 Inferential Statistics and Regression Analysis
o Introduction
o 4.1 Statistical Inference and Confidence Intervals
o 4.2 Hypothesis Testing
o 4.3 Correlation and Linear Regression Analysis
o 4.4 Analysis of Variance (ANOVA)
o Key Terms
o Group Project
o Quantitative Problems

Unit 3 Predicting and Modeling Using Data

Chapter 5 Time Series and Forecasting
o Introduction
o 5.1 Introduction to Time Series Analysis
o 5.2 Components of Time Series Analysis
o 5.3 Time Series Forecasting Methods
o 5.4 Forecast Evaluation Methods
o Key Terms
o Group Project
o Critical Thinking
o Quantitative Problems

Chapter 6 Decision-Making Using Machine Learning Basics
o Introduction
o 6.1 What Is Machine Learning?
o 6.2 Classification Using Machine Learning
o 6.3 Machine Learning in Regression Analysis
o 6.4 Decision Trees
o 6.5 Other Machine Learning Techniques
o Key Terms
o Group Project
o Chapter Review
o Critical Thinking
o Quantitative Problems
o References

Chapter 7 Deep Learning and AI Basics
o Introduction
o 7.1 Introduction to Neural Networks
o 7.2 Backpropagation
o 7.3 Introduction to Deep Learning
o 7.4 Convolutional Neural Networks
o 7.5 Natural Language Processing
o Key Terms
o Group Project
o Chapter Review
o Critical Thinking
o Quantitative Problems
o References

Unit 4 Maintaining a Professional and Ethical Data Science Practice

Chapter 8 Ethics Throughout the Data Science Cycle
o Introduction
o 8.1 Ethics in Data Collection
o 8.2 Ethics in Data Analysis and Modeling
o 8.3 Ethics in Visualization and Reporting
o Key Terms
o Group Project
o Chapter Review
o Critical Thinking
o References

Chapter 9 Visualizing Data
o Introduction
o 9.1 Encoding Univariate Data
o 9.2 Encoding Data That Change Over Time
o 9.3 Graphing Probability Distributions
o 9.4 Geospatial and Heatmap Data Visualization Using Python
o 9.5 Multivariate and Network Data Visualization Using Python
o Key Terms
o Group Project
o Critical Thinking

Chapter 10 Reporting Results
o Introduction
o 10.1 Writing an Informative Report
o 10.2 Validating Your Model
o 10.3 Effective Executive Summaries
o Key Terms
o Group Project
o Chapter Review
o Critical Thinking
o References

Appendix A Appendix A: Review of Excel for Data Science
Appendix B Appendix B: Review of R Studio for Data Science
Appendix C Appendix C: Review of Python Algorithms
Appendix D Appendix D: Review of Python Functions

Answer Key
• Chapter 1
• Chapter 3
• Chapter 5
• Chapter 6
• Chapter 7
• Chapter 8
• Chapter 10
Index