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Principles of Data Science

by Shaun V. Ault, Soohyun Nam Liao and Larry Musolino

Published by OpenStax

Data ScienceStatisticsBusinessCollege

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.

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Attribution

Adapted from Principles of Data Science by Shaun V. Ault, Soohyun Nam Liao, and Larry Musolino, published by OpenStax (Rice University) under CC BY-NC-SA 4.0 (https://creativecommons.org/licenses/by-nc-sa/4.0/). Access the original for free at https://openstax.org/details/books/principles-data-science. Changes: converted to CuraCourse lessons, with interactive practice added.

Table of contents

69 pages in 12 modules. Sign up to read and adopt them.

  1. Preface1 page
    1. Preface
  2. 1What Are Data and Data Science?7 pages
    1. Introduction
    2. 1.1What Is Data Science?
    3. 1.2Data Science in Practice
    4. 1.3Data and Datasets
    5. 1.4Using Technology for Data Science
    6. 1.5Data Science with Python
    7. Practice
  3. 2Collecting and Preparing Data7 pages
    1. Introduction
    2. 2.1Overview of Data Collection Methods
    3. 2.2Survey Design and Implementation
    4. 2.3Web Scraping and Social Media Data Collection
    5. 2.4Data Cleaning and Preprocessing
    6. 2.5Handling Large Datasets
    7. Practice
  4. 3Descriptive Statistics: Statistical Measurements and Probability Distributions7 pages
    1. Introduction
    2. 3.1Measures of Center
    3. 3.2Measures of Variation
    4. 3.3Measures of Position
    5. 3.4Probability Theory
    6. 3.5Discrete and Continuous Probability Distributions
    7. Practice
  5. 4Inferential Statistics and Regression Analysis6 pages
    1. Introduction
    2. 4.1Statistical Inference and Confidence Intervals
    3. 4.2Hypothesis Testing
    4. 4.3Correlation and Linear Regression Analysis
    5. 4.4Analysis of Variance (ANOVA)
    6. Practice
  6. 5Time Series and Forecasting6 pages
    1. Introduction
    2. 5.1Introduction to Time Series Analysis
    3. 5.2Components of Time Series Analysis
    4. 5.3Time Series Forecasting Methods
    5. 5.4Forecast Evaluation Methods
    6. Practice
  7. 6Decision-Making Using Machine Learning Basics7 pages
    1. Introduction
    2. 6.1What Is Machine Learning?
    3. 6.2Classification Using Machine Learning
    4. 6.3Machine Learning in Regression Analysis
    5. 6.4Decision Trees
    6. 6.5Other Machine Learning Techniques
    7. Practice
  8. 7Deep Learning and AI Basics7 pages
    1. Introduction
    2. 7.1Introduction to Neural Networks
    3. 7.2Backpropagation
    4. 7.3Introduction to Deep Learning
    5. 7.4Convolutional Neural Networks
    6. 7.5Natural Language Processing
    7. Practice
  9. 8Ethics Throughout the Data Science Cycle5 pages
    1. Introduction
    2. 8.1Ethics in Data Collection
    3. 8.2Ethics in Data Analysis and Modeling
    4. 8.3Ethics in Visualization and Reporting
    5. Practice
  10. 9Visualizing Data7 pages
    1. Introduction
    2. 9.1Encoding Univariate Data
    3. 9.2Encoding Data That Change Over Time
    4. 9.3Graphing Probability Distributions
    5. 9.4Geospatial and Heatmap Data Visualization Using Python
    6. 9.5Multivariate and Network Data Visualization Using Python
    7. Practice
  11. 10Reporting Results5 pages
    1. Introduction
    2. 10.1Writing an Informative Report
    3. 10.2Validating Your Model
    4. 10.3Effective Executive Summaries
    5. Practice
  12. Appendices4 pages
    1. AAppendix A: Review of Excel for Data Science
    2. BAppendix B: Review of R Studio for Data Science
    3. CAppendix C: Review of Python Algorithms
    4. DAppendix D: Review of Python Functions