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Introduction to Data Analytics

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Introduction to Data Analytics

This book aims to give foundational understanding of Data Analytics to the reader who may have previous context about on-premises and cloud platforms.

Workforce
Author
Felix Amoruwa
Publisher
De Anza College
Source
LibreTexts
License
CC BY
Language
EN
Category
Information Technology
Read online Download PDF (open licence)
Contents· 264 sections
  1. 01: Foundations of Data Analytics
  2. 1.01: What is Data Analytics
  3. 1.02: Visual Progression of Analytics Types
  4. 1.03: Running Case Study - Mobile App Customer Churn Analysis
  5. 1.04: Key Roles - Data Analyst Data Scientist Data Engineer
  6. 1.05: Key Terms
  7. 1.06: Assessments
  8. 1.6.01: Review Questions
  9. 1.6.02: Critical Thinking Questions
  10. 1.6.03: Hands-On Lab Exercise
  11. 1.6.04: Case Study Analysis
  12. 1.07: Detailed Figure Captions - Chapter 1
  13. 1.08: References
  14. 02: Data Types Data Sources and Data Quality
  15. 2.01: Data Types
  16. 2.02: Data Sources
  17. 2.03: Data Quality
  18. 2.04: Metadata Data Governance and Data Stewardship
  19. 2.05: Chapter Summary and Industry Examples
  20. 2.06: Key Terms
  21. 2.07: Assessments
  22. 2.7.01: Review Questions
  23. 2.7.02: Critical Thinking Questions
  24. 2.7.03: Hands-On Lab Exercise
  25. 2.7.04: Case Study Analysis
  26. 2.08: Detailed Figure Captions - Chapter 2
  27. 2.09: References
  28. 03: Data Collection and Ingestion
  29. 3.01: Data Collection vs. Data Ingestion
  30. 3.02: Data Collection Methods
  31. 3.03: Data Ingestion Techniques
  32. 3.04: Challenges and Best Practices in Data Ingestion
  33. 3.05: Data Pipeline and Architecture Patterns
  34. 3.06: Industry Case Studies
  35. 3.07: Conclusion
  36. 3.08: Key Terms
  37. 3.09: Assessments
  38. 3.9.01: Review Questions
  39. 3.9.02: Critical Thinking Questions
  40. 3.9.03: Hands-On Lab Exercise
  41. 3.9.04: Case Study Analysis
  42. 3.10: Detailed Figure Captions - Chapter 3
  43. 3.11: References
  44. 04: Data Cleaning and Preparation
  45. 4.01: Importance of Data Cleaning in Analytics
  46. 4.02: Handling Missing Data (NULL Values)
  47. 4.03: Data Types and Data Transformation
  48. 4.04: Uniqueness Keys and Duplicate Data
  49. 4.05: Valid Ranges and Business Rules
  50. 4.06: Data Integrity and Constraints
  51. 4.07: Data Distribution and Skewness
  52. 4.08: Tools and Techniques for Data Cleaning and Preparation
  53. 4.09: Cleaning Data with SQL
  54. 4.10: Best Practices in Data Cleaning and Preparation
  55. 4.11: Key Terms
  56. 4.12: Assessments
  57. 4.12.01: Review Questions
  58. 4.12.02: Critical Thinking Questions
  59. 4.12.03: Hands-On Lab Exercise
  60. 4.12.04: Case Study Analysis
  61. 4.13: Detailed Figure Captions - Chapter 4
  62. 4.14: References
  63. 05: Exploratory Data Analysis (EDA)
  64. 5.01: Definition and Goals of EDA
  65. 5.02: Historical Background and Importance
  66. 5.03: Univariate Analysis Techniques
  67. 5.04: Bivariate Analysis Techniques
  68. 5.05: Multivariate Analysis Techniques
  69. 5.06: Handling Missing Data in EDA
  70. 5.07: Handling Outliers in EDA
  71. 5.08: Tools and Workflow for EDA
  72. 5.09: Best Practices in EDA
  73. 5.10: Common Mistakes in EDA and How to Avoid Them
  74. 5.11: Key Terms
  75. 5.12: Assessments
  76. 5.12.01: Review Questions
  77. 5.12.02: Critical Thinking Questions
  78. 5.12.03: Hands-On Lab Exercise
  79. 5.12.04: Case Study Analysis
  80. 5.13: Detailed Figure Captions - Chapter 5
  81. 5.14: References
  82. 06: Statistical Foundations for Analytics
  83. 6.01: Introduction- The Role of Statistics in Data Analytics
  84. 6.02: Descriptive Statistics- Summarizing Data
  85. 6.03: Probability Theory and Key Distributions
  86. 6.04: Inferential Statistics- From Samples to Populations
  87. 6.05: Real-World Applications of Statistical Analysis
  88. 6.06: Common Pitfalls in Statistical Reasoning
  89. 6.07: Key Terms
  90. 6.08: Assessments
  91. 6.8.01: Review Questions
  92. 6.8.02: Critical Thinking Questions
  93. 6.8.03: Hands-On Lab Exercise
  94. 6.8.04: Case Study Analysis
  95. 6.09: Detailed Figure Captions - Chapter 6
  96. 6.10: References
  97. 07: Data Visualization Principles
  98. 7.01: Importance of Data Visualization in Analytics
  99. 7.02: Theory of Visual Perception and Preattentive Attributes
  100. 7.03: Exploratory vs. Explanatory Visualization
  101. 7.04: Best Practices for Choosing Chart Types
  102. 7.05: Using Dimensions and Measures in Charting
  103. 7.06: Communicating Stories Effectively with Visuals
  104. 7.07: Design Principles- Clarity Consistency Accessibility and Color Usage
  105. 7.08: Tools and Libraries for Data Visualization
  106. 7.09: Practical Examples with Python- Code and Outputs
  107. 7.10: Common Visualization Mistakes and How to Avoid Them
  108. 7.11: Key Terms
  109. 7.12: Assessments
  110. 7.12.01: Review Questions
  111. 7.12.02: Critical Thinking Questions
  112. 7.12.03: Hands-On Lab Exercise
  113. 7.12.04: Case Study Analysis
  114. 7.13: Detailed Figure Captions - Chapter 7
  115. 7.14: References
  116. 08: Visualization Tools and Libraries
  117. 8.01: Categories of Visualization Tools
  118. 8.02: Open-Source Visualization Libraries (Python)
  119. 8.03: Commercial and Enterprise Visualization Tools
  120. 8.04: Choosing the Right Tool for the Job
  121. 8.05: Integration with Notebooks Cloud Services and BI Ecosystems
  122. 8.06: Accessibility Interactivity and Performance Considerations
  123. 8.07: Industry Case Studies and Use Cases
  124. 8.08: Key Terms
  125. 8.09: Assessments
  126. 8.9.01: Review Questions
  127. 8.9.02: Critical Thinking Questions
  128. 8.9.03: Hands-On Lab Exercise
  129. 8.9.04: Case Study Analysis
  130. 8.10: Detailed Figure Captions - Chapter 8
  131. 8.11: References
  132. 09: Advanced Visualization Techniques Dashboarding and Storytelling
  133. 9.01: Interactive Visualization Techniques
  134. 9.02: Advanced Chart Types and When to Use Them
  135. 9.2.01: Treemaps (Hierarchical Area Charts)
  136. 9.2.02: Bullet Charts (Bullet Graphs for KPIs)
  137. 9.2.03: Network Graphs (Node-Link Diagrams)
  138. 9.2.04: Sankey Diagrams (Flow Diagrams for Quantitative Flows)
  139. 9.2.05: Radial Plots (Circular Charts like Radar or Polar Charts)
  140. 9.03: Dashboard Design Principles
  141. 9.3.01: Layout and Visual Hierarchy
  142. 9.3.02: Reducing Cognitive Load (Simplify and Emphasize)
  143. 9.3.03: Interactive Filters and User Controls
  144. 9.3.04: Responsive and Adaptive Design
  145. 9.04: Real-Time Dashboard Considerations
  146. 9.05: Data Storytelling Methodology
  147. 9.06: Presenting to Different Audiences - Technical vs. Executive
  148. 9.07: Industry Case Studies - Effective Data Storytelling in Action
  149. 9.08: Key Terms
  150. 9.09: Assesements
  151. 9.9.01: Review Questions
  152. 9.9.02: Critical Thinking Questions
  153. 9.9.03: Hands-On Lab Exercise
  154. 9.9.04: Case Study Analysis
  155. 9.10: Detailed Figure Captions - Chapter 9
  156. 9.11: References
  157. 10: Predictive Analytics and Machine Learning Basics
  158. 10.01: Introduction to Predictive Analytics
  159. 10.02: Supervised vs. Unsupervised Learning
  160. 10.03: Overview of Common Algorithms in Predictive Analytics
  161. 10.3.01: Linear Regression
  162. 10.3.02: Logistic Regression
  163. 10.3.03: Decision Trees
  164. 10.3.04: Random Forests
  165. 10.3.05: Clustering Techniques (K-Means and Hierarchical Clustering)
  166. 10.04: Model Evaluation and Validation
  167. 10.4.01: Training Testing and Validation Splits
  168. 10.4.02: Cross-Validation
  169. 10.4.03: Confusion Matrix and Classification Metrics
  170. 10.4.04: Evaluating Model Generalization
  171. 10.05: Overfitting and Generalization
  172. 10.06: When to Use Machine Learning vs Traditional Analytics Methods
  173. 10.07: Key Terms
  174. 10.08: Assessments
  175. 10.8.01: Review Questions
  176. 10.8.02: Critical Thinking Questions
  177. 10.8.03: Hands-On Lab Exercise
  178. 10.8.04: Case Study Analysis
  179. 10.09: Detailed Figure Captions - Chapter 10
  180. 10.10: References
  181. 11: Business Intelligence and Embedded Analytics
  182. 11.01: Understanding Business Intelligence (BI) Fundamentals
  183. 11.1.01: Definition and Key Concepts of BI
  184. 11.1.02: BI Architecture and Core Components
  185. 11.1.03: Data Warehousing Concepts in BI
  186. 11.1.04: OLAP vs OLTP Systems
  187. 11.02: Review of the BI Tool Landscape
  188. 11.2.01: Enterprise BI Platforms
  189. 11.2.02: Open-Source BI Tools
  190. 11.2.03: Cloud-Native BI Solutions
  191. 11.2.04: Embedded Analytics Platforms and Integrations
  192. 11.03: Key Terms
  193. 11.04: Assessments
  194. 11.4.01: Review Questions
  195. 11.4.02: Critical Thinking Questions
  196. 11.4.03: Hands-On Lab Exercise
  197. 11.4.04: Case Study Analysis
  198. 11.05: Detailed Figure Captions - Chapter 11
  199. 11.06: References
  200. 12: Programming-Based Analytics Tools
  201. 12.01: Python for Data Analytics
  202. 12.1.01: Jupyter Notebooks and Development Environments
  203. 12.1.02: Data Loading and Manipulation (Python)
  204. 12.1.03: Modeling with scikit-learn
  205. 12.1.04: Tool Summary- Python Libraries
  206. 12.02: R for Statistical Analysis
  207. 12.2.01: R Studio and R Markdown
  208. 12.2.1.01: Data Wrangling and Visualization in R
  209. 12.2.1.02: Statistical Modeling in R
  210. 12.2.02: Tool Summary - R Packages
  211. 12.03: SQL for Data Manipulation
  212. 12.3.01: Query Performance Optimization
  213. 12.3.02: SQL Code Examples
  214. 12.04: Integration between Tools and Workflows
  215. 12.4.01: Environments and Collaboration
  216. 12.4.02: Tool Comparison Table
  217. 12.05: Key Terms
  218. 12.06: Assessments
  219. 12.6.01: Review Questions
  220. 12.6.02: Critical Thinking Questions
  221. 12.6.03: Hands-On Lab Exercise
  222. 12.6.04: Case Study Analysis
  223. 12.07: Detailed Figure Captions - Chapter 12
  224. 12.08: References
  225. 13: Cloud Analytics Platforms and Real-World Case Studies
  226. 13.01: Cloud Analytics Landscape
  227. 13.1.01: Major Cloud Analytics Services
  228. 13.1.02: Serverless Analytics Architectures
  229. 13.1.03: Cost Optimization Strategies
  230. 13.02: Real-World Case Studies
  231. 13.2.01: Retail - Customer Analytics and Personalization
  232. 13.2.02: Healthcare - Clinical Insights and Predictive Diagnostics
  233. 13.2.03: Finance - Risk Modeling and Fraud Detection
  234. 13.2.04: Manufacturing - Predictive Maintenance and IoT Analytics
  235. 13.03: Lessons Learned and Best Practices
  236. 13.04: Conclusion
  237. 13.05: Key Terms
  238. 13.06: Assessments
  239. 13.6.01: Review Questions
  240. 13.6.02: Critical Thinking Questions
  241. 13.6.03: Hands-On Lab Questions
  242. 13.6.04: Case Study Analysis
  243. 13.07: Detailed Figure Captions - Chapter 13
  244. 13.08: References
  245. 14: Machine Learning for Analytics and Future Trends
  246. 14.01: Advanced Machine Learning Techniques for Business Analytics
  247. 14.02: Automated Machine Learning (AutoML) and Democratization
  248. 14.03: AI-Powered Analytics and Augmented Intelligence
  249. 14.04: Emerging Trends in Analytics
  250. 14.4.01: Real-Time Analytics and Edge Computing
  251. 14.4.02: Explainable AI (XAI) and Model Interpretability
  252. 14.4.03: Data Mesh and Decentralized Analytics Architectures
  253. 14.4.04: Privacy-Preserving Analytics (Differential Privacy Federated Learning)
  254. 14.4.05: Comparing Traditional vs. Emerging Methods
  255. 14.05: Key Terms
  256. 14.06: Assessments
  257. 14.6.01: Review Questions
  258. 14.6.02: Critical Thinking Questions
  259. 14.6.03: Hands-On Lab Questions
  260. 14.6.04: Case Study Questions
  261. 14.07: Detailed Figure Captions - Chapter 14
  262. 14.08: References
  263. 15: New Chapter
  264. 16: New Chapter 1