Data Analytics with Applications in Business

by Elbert L. Hearon

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Publisher
Auora University
Subjects
Business
Licence
CC BY 4.0, as LibreTexts states it
Who can read
Tweens profiles and older, judged from its title and subjects

Contents

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  1. Front Matter
  2. Overview of Data Analytics
  3. InfoPage
  4. Table of Contents
  5. Licensing
  6. 1: Introduction to Data Analytics
  7. 1.1: Study Guide
  8. 1.2: Introduction to Data Analytics and Its Categories
  9. 1.2.1: What is Data Analytics
  10. 1.2.2: Types of Analytics
  11. 1.2.3: Review Questions
  12. 1.3: Understanding Big Data and Big Data Analytics
  13. 1.3.1: Types of Data
  14. 1.3.2: Big Data Analytics and the "5 V's"
  15. 1.3.3: Technology Used in Big Data Analytics
  16. 1.3.4: Machine Learning and Artificial Intelligence in Analytics
  17. 1.3.5: Review questions
  18. 1.4: Tools and Technologies
  19. 1.4.1: Programming Languages
  20. 1.4.2: Visualization Tools
  21. 1.4.3: Spreadsheet Software
  22. 1.5: Data Governance
  23. 1.6: Careers in Analytics
  24. 1.7: Chapter 1 Summary
  25. 1.8: Chapter 1 Exercises
  26. 2: Data Preparation
  27. 2.1: Study Guide
  28. 2.2: Data Preparation
  29. 2.3: Data Collection
  30. 2.4: Data Quality
  31. 2.5: Data Cleaning
  32. 2.5.1: Removing Duplicates
  33. 2.5.2: Handling Missing Data
  34. 2.5.3: Text Cleaning and Standardization
  35. 2.5.4: Formatting Inconsistencies
  36. 2.5.5: Outlier Detection
  37. 2.5.6: Data Validation and Restriction
  38. 2.5.7: Combining and Separating Data
  39. 2.5.8: Filtering and Sorting for Quality Checks
  40. 2.5.9: Error Checking
  41. 2.6: Comparing Tools for Enterprise Data Preparation and Visualization
  42. 2.7: Ethical and Legal Considerations
  43. 2.8: Chapter 2 Summary
  44. 2.9: Chapter 2 Exercises
  45. 2.10: Chapter 2 References
  46. 3: Exploratory Data Analysis
  47. 3.1: Introduction to Exploratory Data Analysis
  48. 3.1.1: Descriptive Statistics using Excel
  49. 3.1.2: Descriptive Statistics and Visual Exploration Using Tableau Public
  50. 3.1.2.1: Introduction to Tableau Public - Connecting an Excel File
  51. 3.1.2.2: Show Me Function- Tableau Basics
  52. 3.1.2.3: Building Charts and Graphs in Tableau
  53. 3.2: Visualizing Data
  54. 3.2.1: Distribution and Spread
  55. 3.2.2: Comparison and Trends
  56. 3.2.3: Quick Reference - Chart Types for EDA
  57. 3.3: Challenges in Exploratory Data Analysis
  58. 3.4: Chapter 3 Summary
  59. 3.5: Chapter 3 Exercises
  60. 3.6: Appendix 1 - Creating Visualizations in Microsoft Excel
  61. 4: Correlation Analysis
  62. 4.1: Study Guide
  63. 4.2: Introduction to Correlation Analysis
  64. 4.3: Pearson Correlation Coefficient
  65. 4.4: Interpreting Correlation Coefficients
  66. 4.5: Visualizing Correlations
  67. 4.6: Business Applications of Correlation Analysis
  68. 4.7: Understanding Limitations of Correlation Analysis
  69. 4.8: Chapter 4 Summary
  70. 4.9: Chapter 4 Exercises
  71. 4.10: References and Tools for Correlation Analysis
  72. 5: Linear Regression Analysis
  73. 5.1: Study Guide
  74. 5.2: Introduction to Linear Regression
  75. 5.3: Assumptions of Linear Regression Analysis
  76. 5.4: Simple Linear Regression Analysis
  77. 5.4.1: Model and Equation for Simple Linear Regression Analysis
  78. 5.4.2: Simple Linear Regression Output
  79. 5.5: Multiple Linear Regression
  80. 5.5.1: Model and Equation for Multiple Linear Regression Models
  81. 5.5.2: Multiple Linear Regression Output
  82. 5.6: Business Applications of Linear Regression Analysis
  83. 5.7: Using Dummy Variables in Linear Regression Analysis
  84. 5.8: Using Interaction Variables in Linear Regression Analysis
  85. 5.9: Multicollinearity
  86. 5.10: Outliers
  87. 5.11: Variable Selection Methods
  88. 5.12: Common Pitfalls and Best Practices
  89. 5.13: Leading Tools for Performing Linear Regression Analysis
  90. 5.14: Steps for Building a Linear Regression Model
  91. 5.15: Summary
  92. 5.16: Case Study using Linear Regression Analysis
  93. 5.16.1: Dataset
  94. 5.16.2: Data Exploration
  95. 5.16.3: Simple Linear Regression Model Output
  96. 5.16.4: Multiple Regression Model
  97. 5.17: Chapter 5 Exercises
  98. 5.18: Chapter 5 References
  99. 5.19: Appendix 1(Optional) - Advanced Topics in Linear Regression
  100. 5.20: Appendix 2 (Optional) - Multiple Linear Regression Models using Python
  101. 6: Logistic Regression
  102. 6.1: Study Guide
  103. 6.2: Introduction to Logistic Regression
  104. 6.3: Logistic Regression Model, Explanation, and Numeric Example
  105. 6.4: Binary Classification Explained
  106. 6.5: How Logistic Regression Works - Intuitively
  107. 6.6: Logistic Regression in Action
  108. 6.7: Evaluating Model Performance
  109. 6.8: Business Applications and Takeaways
  110. 6.9: Steps for Building a Logistic Regression Model
  111. 6.10: Case Study - Logistic Regression Model to Predict Churn
  112. 6.10.1: Customer Dataset
  113. 6.10.2: Exploratory Data Analysis
  114. 6.10.3: Python Code for Logistic Regression
  115. 6.10.4: Model Output and Interpretation
  116. 6.11: Appendix - Formulas used in Logistic Regression
  117. 7: Decision Trees
  118. 7.1: Introduction to Decision Trees
  119. 7.2: Classification Trees
  120. 7.3: Regression Trees
  121. 7.4: Case Study - Using Classification Tree to Predict Customer Cancellation
  122. 7.4.1: Customer Dataset
  123. 7.4.2: Exploratory Data Analysis
  124. 7.4.3: Classification Tree Output and Interpretation
  125. 7.4.4: Python Code for Case Study Classification Treem
  126. 7.5: Exercises
  127. 8: Random Forests
  128. 8.1: Study Guide
  129. 8.2: What is a Random Forest?
  130. 8.3: Business Use Cases for Random Forests
  131. 8.4: How Random Forests Work
  132. 8.5: Strengths, Limitations, and Practical Considerations
  133. 8.6: Interpreting Random Forests (Classification)- Output and Diagnostics
  134. 8.7: Case Study - Using Random Forests to Predict Customer Cancellation
  135. 8.7.1: Customer Dataset
  136. 8.7.2: Exploratory Data Analysis
  137. 8.7.3: Random Forest using Python to Build the Churn Model
  138. 8.7.4: Random Forest Model Output and Interpretation
  139. 8.8: Chapter Summary
  140. 8.9: Exercises
  141. 9: Gradient Boosting
  142. 9.1: Study Guide
  143. 9.2: Introduction to Gradient Boosting
  144. 9.3: Conceptual Foundations
  145. 9.4: Key Components of Gradient Boosting
  146. 9.5: The Gradient Boosting Algorithm
  147. 9.6: Hyperparameter Tuning for Gradient Boosting
  148. 9.7: Popular Implementations for Python
  149. 9.8: Gradient Boosting Classification Example - Promotional Email Campaign
  150. 9.9: Advantages and Disadvantages
  151. 9.10: Common Pitfalls and Best Practices
  152. 9.11: Summary and Key Takeaways
  153. 9.12: Exercises
  154. 9.13: Case Study - Gradient Bossing Model to Predict Churn
  155. 9.13.1: Customer Dataset
  156. 9.13.2: Exploratory Data Analysis
  157. 9.13.3: Python Code
  158. 9.13.4: Model Output and Interpretation
  159. 10: Support Vector Machines
  160. 10.1: Study Guide
  161. 10.2: Introduction to Support Vector Machines
  162. 10.3: Key Concepts of Support Vector Machines
  163. 10.4: Support Vector Machines with Linearly Separable Data
  164. 10.5: Non-Linearly Separable Data and the Kernel Trick
  165. 10.6: Kernel Selection in Support Vector Machines (SVMs)
  166. 10.7: Advantages and Limitations of SVMs
  167. 10.8: Case Study - SVM Model to Predict Churn using Python
  168. 10.8.1: Dataset used for SVM Classification Case Study
  169. 10.8.2: Exploratory Data Analysis Used for SVM Classification Case Study
  170. 10.8.3: Python Code used for SVM Classification Case Study
  171. 10.8.4: Model Output and Interpretation from SVM Classification Case Study
  172. 10.9: Chapter 9 Summary
  173. 10.10: Exercises
  174. 11: Auto Machine Learning (AutoML)
  175. 11.1: Overview of AutoML
  176. 11.1.1: AutoML Code Snippet for Curious Learnings (Python)
  177. 11.1.2: Dataset Used in the AutoML Example
  178. 11.1.3: AutoML Example
  179. 11.1.3.1: NOTE- Transparency of AutoML Models
  180. 11.1.3.2: Python Code for AutoML Example
  181. 11.1.3.3: Output
  182. 12: Cluster Analysis
  183. 12.1: Study Guide
  184. 12.2: Introduction to Cluster Analysis
  185. 12.3: Applications of Cluster Analysis
  186. 12.4: K-Means Clustering
  187. 12.5: Hierarchical Clustering
  188. 12.6: DBSCAN
  189. 12.7: Gaussian Mixture Models
  190. 12.8: Final Considerations and Enhancements
  191. 12.9: Chapter 12 Summary
  192. 12.10: Exercises
  193. 12.11: Case Study - Customer Segmentation using K-Means Clustering
  194. 12.11.1: Why K-means Clustering is Popular
  195. 12.11.2: Steps for Building a K-means Clustering System
  196. 12.11.3: Dataset Used for Case Study
  197. 12.11.4: Encoding Categorical Variables
  198. 12.11.5: Elbow Plot to Determine # of Clusters and Python Code
  199. 12.11.6: Python Code for K-Means Clustering
  200. 12.11.7: K-Means Clustering Output and Interpretation
  201. 12.11.8: New Page
  202. Back Matter
  203. Index
  204. Glossary
  205. Detailed Licensing
  206. Detailed Licensing