Principles of Data Science

by OpenStax

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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.

Description from LibreTexts.

Subjects
Engineering, Data Science
Licence
CC BY 4.0, as LibreTexts states it
Who can read
Tweens profiles and older, judged from its title and subjects

Contents

LibreTexts writes equations in TeX, and here they show as TeX; the PDF has them typeset.

  1. Front Matter
  2. TitlePage
  3. InfoPage
  4. Table of Contents
  5. Licensing
  6. 1: What Are Data and Data Science?
  7. 1.0: Introduction
  8. 1.1: What Is Data Science?
  9. 1.2: Data Science in Practice
  10. 1.3: Data and Datasets
  11. 1.4: Using Technology for Data Science
  12. 1.5: Data Science with Python
  13. 1.6: Key Terms
  14. 1.7: Group Project
  15. 1.8: Chapter Review
  16. 1.9: Critical Thinking
  17. 1.10: Quantitative Problems
  18. 1.11: References
  19. 2: Collecting and Preparing Data
  20. 2.0: Introduction
  21. 2.1: Overview of Data Collection Methods
  22. 2.2: Survey Design and Implementation
  23. 2.3: Web Scraping and Social Media Data Collection
  24. 2.4: Data Cleaning and Preprocessing
  25. 2.5: Handling Large Datasets
  26. 2.6: Key Terms
  27. 2.7: Group Project
  28. 2.8: Critical Thinking
  29. 2.9: References
  30. 3: Descriptive Statistics- Statistical Measurements and Probability Distributions
  31. 3.0: Introduction
  32. 3.1: Measures of Center
  33. 3.2: Measures of Variation
  34. 3.3: Measures of Position
  35. 3.4: Probability Theory
  36. 3.5: Discrete and Continuous Probability Distributions
  37. 3.6: Key Terms
  38. 3.7: Group Project
  39. 3.8: Quantitative Problems
  40. 4: Inferential Statistics and Regression Analysis
  41. 4.0: Introduction
  42. 4.1: Statistical Inference and Confidence Intervals
  43. 4.2: Hypothesis Testing
  44. 4.3: Correlation and Linear Regression Analysis
  45. 4.4: Analysis of Variance (ANOVA)
  46. 4.5: Key Terms
  47. 4.6: Group Project
  48. 4.7: Quantitative Problems
  49. 5: Time Series and Forecasting
  50. 5.0: Introduction
  51. 5.1: Introduction to Time Series Analysis
  52. 5.2: Components of Time Series Analysis
  53. 5.3: Time Series Forecasting Methods
  54. 5.4: Forecast Evaluation Methods
  55. 5.5: Key Terms
  56. 5.6: Group Project
  57. 5.7: Critical Thinking
  58. 5.8: Quantitative Problems
  59. 6: Decision-Making Using Machine Learning Basics
  60. 6.0: Introduction
  61. 6.1: What Is Machine Learning?
  62. 6.2: Classification Using Machine Learning
  63. 6.3: Machine Learning in Regression Analysis
  64. 6.4: Decision Trees
  65. 6.5: Other Machine Learning Techniques
  66. 6.6: Key Terms
  67. 6.7: Group Project
  68. 6.8: Chapter Review
  69. 6.9: Critical Thinking
  70. 6.10: Quantitative Problems
  71. 6.11: References
  72. 7: Deep Learning and AI Basics
  73. 7.0: Introduction
  74. 7.1: Introduction to Neural Networks
  75. 7.2: Backpropagation
  76. 7.3: Introduction to Deep Learning
  77. 7.4: Convolutional Neural Networks
  78. 7.5: Natural Language Processing
  79. 7.6: Key Terms
  80. 7.7: Group Project
  81. 7.8: Chapter Review
  82. 7.9: Critical Thinking
  83. 7.10: Quantitative Problems
  84. 7.11: References
  85. 8: Ethics Throughout the Data Science Cycle
  86. 8.0: Introduction
  87. 8.1: Ethics in Data Collection
  88. 8.2: Ethics in Data Analysis and Modeling
  89. 8.3: Ethics in Visualization and Reporting
  90. 8.4: Key Terms
  91. 8.5: Group Project
  92. 8.6: Chapter Review
  93. 8.7: Critical Thinking
  94. 8.8: References
  95. 9: Visualizing Data
  96. 9.0: Introduction
  97. 9.1: Encoding Univariate Data
  98. 9.2: Encoding Data That Change Over Time
  99. 9.3: Graphing Probability Distributions
  100. 9.4: Geospatial and Heatmap Data Visualization Using Python
  101. 9.5: Multivariate and Network Data Visualization Using Python
  102. 9.6: Key Terms
  103. 9.7: Group Project
  104. 9.8: Critical Thinking
  105. 10: Reporting Results
  106. 10.0: Introduction
  107. 10.1: Writing an Informative Report
  108. 10.2: Validating Your Model
  109. 10.3: Effective Executive Summaries
  110. 10.4: Key Terms
  111. 10.5: Group Project
  112. 10.6: Chapter Review
  113. 10.7: Critical Thinking
  114. 10.8: References
  115. 11: Appendix
  116. 11.0: Appendix A- Review of Excel for Data Science
  117. 11.1: Appendix B- Review of R Studio for Data Science
  118. 11.2: Appendix C- Review of Python Algorithms
  119. 11.3: Appendix D- Review of Python Functions
  120. 12: Answer Key
  121. 12.0: Chapter 1
  122. 12.1: Chapter 3
  123. 12.2: Chapter 5
  124. 12.3: Chapter 6
  125. 12.4: Chapter 7
  126. 12.5: Chapter 8
  127. 12.6: Chapter 10
  128. Back Matter
  129. Index
  130. Glossary
  131. Detailed Licensing
  132. Detailed Licensing