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Principles of Data Science

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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.

Data ScienceEngineering
Author
OpenStax
Publisher
LibreTexts
Source
LibreTexts
License
CC BY
Language
EN
Category
Information Technology
Read online Download PDF (open licence)
Contents· 122 sections
  1. 01: What Are Data and Data Science
  2. 1.00: Introduction
  3. 1.01: What Is Data Science
  4. 1.02: Data Science in Practice
  5. 1.03: Data and Datasets
  6. 1.04: Using Technology for Data Science
  7. 1.05: Data Science with Python
  8. 1.06: Key Terms
  9. 1.07: Group Project
  10. 1.08: Chapter Review
  11. 1.09: Critical Thinking
  12. 1.10: Quantitative Problems
  13. 1.11: References
  14. 02: Collecting and Preparing Data
  15. 2.00: Introduction
  16. 2.01: Overview of Data Collection Methods
  17. 2.02: Survey Design and Implementation
  18. 2.03: Web Scraping and Social Media Data Collection
  19. 2.04: Data Cleaning and Preprocessing
  20. 2.05: Handling Large Datasets
  21. 2.06: Key Terms
  22. 2.07: Group Project
  23. 2.08: Critical Thinking
  24. 2.09: References
  25. 03: Descriptive Statistics- Statistical Measurements and Probability Distributions
  26. 3.00: Introduction
  27. 3.01: Measures of Center
  28. 3.02: Measures of Variation
  29. 3.03: Measures of Position
  30. 3.04: Probability Theory
  31. 3.05: Discrete and Continuous Probability Distributions
  32. 3.06: Key Terms
  33. 3.07: Group Project
  34. 3.08: Quantitative Problems
  35. 04: Inferential Statistics and Regression Analysis
  36. 4.00: Introduction
  37. 4.01: Statistical Inference and Confidence Intervals
  38. 4.02: Hypothesis Testing
  39. 4.03: Correlation and Linear Regression Analysis
  40. 4.04: Analysis of Variance (ANOVA)
  41. 4.05: Key Terms
  42. 4.06: Group Project
  43. 4.07: Quantitative Problems
  44. 05: Time Series and Forecasting
  45. 5.00: Introduction
  46. 5.01: Introduction to Time Series Analysis
  47. 5.02: Components of Time Series Analysis
  48. 5.03: Time Series Forecasting Methods
  49. 5.04: Forecast Evaluation Methods
  50. 5.05: Key Terms
  51. 5.06: Group Project
  52. 5.07: Critical Thinking
  53. 5.08: Quantitative Problems
  54. 06: Decision-Making Using Machine Learning Basics
  55. 6.00: Introduction
  56. 6.01: What Is Machine Learning
  57. 6.02: Classification Using Machine Learning
  58. 6.03: Machine Learning in Regression Analysis
  59. 6.04: Decision Trees
  60. 6.05: Other Machine Learning Techniques
  61. 6.06: Key Terms
  62. 6.07: Group Project
  63. 6.08: Chapter Review
  64. 6.09: Critical Thinking
  65. 6.10: Quantitative Problems
  66. 6.11: References
  67. 07: Deep Learning and AI Basics
  68. 7.00: Introduction
  69. 7.01: Introduction to Neural Networks
  70. 7.02: Backpropagation
  71. 7.03: Introduction to Deep Learning
  72. 7.04: Convolutional Neural Networks
  73. 7.05: Natural Language Processing
  74. 7.06: Key Terms
  75. 7.07: Group Project
  76. 7.08: Chapter Review
  77. 7.09: Critical Thinking
  78. 7.10: Quantitative Problems
  79. 7.11: References
  80. 08: Ethics Throughout the Data Science Cycle
  81. 8.00: Introduction
  82. 8.01: Ethics in Data Collection
  83. 8.02: Ethics in Data Analysis and Modeling
  84. 8.03: Ethics in Visualization and Reporting
  85. 8.04: Key Terms
  86. 8.05: Group Project
  87. 8.06: Chapter Review
  88. 8.07: Critical Thinking
  89. 8.08: References
  90. 09: Visualizing Data
  91. 9.00: Introduction
  92. 9.01: Encoding Univariate Data
  93. 9.02: Encoding Data That Change Over Time
  94. 9.03: Graphing Probability Distributions
  95. 9.04: Geospatial and Heatmap Data Visualization Using Python
  96. 9.05: Multivariate and Network Data Visualization Using Python
  97. 9.06: Key Terms
  98. 9.07: Group Project
  99. 9.08: Critical Thinking
  100. 10: Reporting Results
  101. 10.00: Introduction
  102. 10.01: Writing an Informative Report
  103. 10.02: Validating Your Model
  104. 10.03: Effective Executive Summaries
  105. 10.04: Key Terms
  106. 10.05: Group Project
  107. 10.06: Chapter Review
  108. 10.07: Critical Thinking
  109. 10.08: References
  110. 11: Appendix
  111. 11.00: Appendix A- Review of Excel for Data Science
  112. 11.01: Appendix B- Review of R Studio for Data Science
  113. 11.02: Appendix C- Review of Python Algorithms
  114. 11.03: Appendix D- Review of Python Functions
  115. 12: Answer Key
  116. 12.00: Chapter 1
  117. 12.01: Chapter 3
  118. 12.02: Chapter 5
  119. 12.03: Chapter 6
  120. 12.04: Chapter 7
  121. 12.05: Chapter 8
  122. 12.06: Chapter 10