Nguồn ngoài

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

Tác giả: Felix Amoruwa
Nhà xuất bản: De Anza College
Nguồn: LibreTexts
Giấy phép: CC BY
Ngôn ngữ: EN
Danh mục: Công nghệ thông tin

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Mục lục· 264 mục

01: Foundations of Data Analytics
1.01: What is Data Analytics
1.02: Visual Progression of Analytics Types
1.03: Running Case Study - Mobile App Customer Churn Analysis
1.04: Key Roles - Data Analyst Data Scientist Data Engineer
1.05: Key Terms
1.06: Assessments
1.6.01: Review Questions
1.6.02: Critical Thinking Questions
1.6.03: Hands-On Lab Exercise
1.6.04: Case Study Analysis
1.07: Detailed Figure Captions - Chapter 1
1.08: References
02: Data Types Data Sources and Data Quality
2.01: Data Types
2.02: Data Sources
2.03: Data Quality
2.04: Metadata Data Governance and Data Stewardship
2.05: Chapter Summary and Industry Examples
2.06: Key Terms
2.07: Assessments
2.7.01: Review Questions
2.7.02: Critical Thinking Questions
2.7.03: Hands-On Lab Exercise
2.7.04: Case Study Analysis
2.08: Detailed Figure Captions - Chapter 2
2.09: References
03: Data Collection and Ingestion
3.01: Data Collection vs. Data Ingestion
3.02: Data Collection Methods
3.03: Data Ingestion Techniques
3.04: Challenges and Best Practices in Data Ingestion
3.05: Data Pipeline and Architecture Patterns
3.06: Industry Case Studies
3.07: Conclusion
3.08: Key Terms
3.09: Assessments
3.9.01: Review Questions
3.9.02: Critical Thinking Questions
3.9.03: Hands-On Lab Exercise
3.9.04: Case Study Analysis
3.10: Detailed Figure Captions - Chapter 3
3.11: References
04: Data Cleaning and Preparation
4.01: Importance of Data Cleaning in Analytics
4.02: Handling Missing Data (NULL Values)
4.03: Data Types and Data Transformation
4.04: Uniqueness Keys and Duplicate Data
4.05: Valid Ranges and Business Rules
4.06: Data Integrity and Constraints
4.07: Data Distribution and Skewness
4.08: Tools and Techniques for Data Cleaning and Preparation
4.09: Cleaning Data with SQL
4.10: Best Practices in Data Cleaning and Preparation
4.11: Key Terms
4.12: Assessments
4.12.01: Review Questions
4.12.02: Critical Thinking Questions
4.12.03: Hands-On Lab Exercise
4.12.04: Case Study Analysis
4.13: Detailed Figure Captions - Chapter 4
4.14: References
05: Exploratory Data Analysis (EDA)
5.01: Definition and Goals of EDA
5.02: Historical Background and Importance
5.03: Univariate Analysis Techniques
5.04: Bivariate Analysis Techniques
5.05: Multivariate Analysis Techniques
5.06: Handling Missing Data in EDA
5.07: Handling Outliers in EDA
5.08: Tools and Workflow for EDA
5.09: Best Practices in EDA
5.10: Common Mistakes in EDA and How to Avoid Them
5.11: Key Terms
5.12: Assessments
5.12.01: Review Questions
5.12.02: Critical Thinking Questions
5.12.03: Hands-On Lab Exercise
5.12.04: Case Study Analysis
5.13: Detailed Figure Captions - Chapter 5
5.14: References
06: Statistical Foundations for Analytics
6.01: Introduction- The Role of Statistics in Data Analytics
6.02: Descriptive Statistics- Summarizing Data
6.03: Probability Theory and Key Distributions
6.04: Inferential Statistics- From Samples to Populations
6.05: Real-World Applications of Statistical Analysis
6.06: Common Pitfalls in Statistical Reasoning
6.07: Key Terms
6.08: Assessments
6.8.01: Review Questions
6.8.02: Critical Thinking Questions
6.8.03: Hands-On Lab Exercise
6.8.04: Case Study Analysis
6.09: Detailed Figure Captions - Chapter 6
6.10: References
07: Data Visualization Principles
7.01: Importance of Data Visualization in Analytics
7.02: Theory of Visual Perception and Preattentive Attributes
7.03: Exploratory vs. Explanatory Visualization
7.04: Best Practices for Choosing Chart Types
7.05: Using Dimensions and Measures in Charting
7.06: Communicating Stories Effectively with Visuals
7.07: Design Principles- Clarity Consistency Accessibility and Color Usage
7.08: Tools and Libraries for Data Visualization
7.09: Practical Examples with Python- Code and Outputs
7.10: Common Visualization Mistakes and How to Avoid Them
7.11: Key Terms
7.12: Assessments
7.12.01: Review Questions
7.12.02: Critical Thinking Questions
7.12.03: Hands-On Lab Exercise
7.12.04: Case Study Analysis
7.13: Detailed Figure Captions - Chapter 7
7.14: References
08: Visualization Tools and Libraries
8.01: Categories of Visualization Tools
8.02: Open-Source Visualization Libraries (Python)
8.03: Commercial and Enterprise Visualization Tools
8.04: Choosing the Right Tool for the Job
8.05: Integration with Notebooks Cloud Services and BI Ecosystems
8.06: Accessibility Interactivity and Performance Considerations
8.07: Industry Case Studies and Use Cases
8.08: Key Terms
8.09: Assessments
8.9.01: Review Questions
8.9.02: Critical Thinking Questions
8.9.03: Hands-On Lab Exercise
8.9.04: Case Study Analysis
8.10: Detailed Figure Captions - Chapter 8
8.11: References
09: Advanced Visualization Techniques Dashboarding and Storytelling
9.01: Interactive Visualization Techniques
9.02: Advanced Chart Types and When to Use Them
9.2.01: Treemaps (Hierarchical Area Charts)
9.2.02: Bullet Charts (Bullet Graphs for KPIs)
9.2.03: Network Graphs (Node-Link Diagrams)
9.2.04: Sankey Diagrams (Flow Diagrams for Quantitative Flows)
9.2.05: Radial Plots (Circular Charts like Radar or Polar Charts)
9.03: Dashboard Design Principles
9.3.01: Layout and Visual Hierarchy
9.3.02: Reducing Cognitive Load (Simplify and Emphasize)
9.3.03: Interactive Filters and User Controls
9.3.04: Responsive and Adaptive Design
9.04: Real-Time Dashboard Considerations
9.05: Data Storytelling Methodology
9.06: Presenting to Different Audiences - Technical vs. Executive
9.07: Industry Case Studies - Effective Data Storytelling in Action
9.08: Key Terms
9.09: Assesements
9.9.01: Review Questions
9.9.02: Critical Thinking Questions
9.9.03: Hands-On Lab Exercise
9.9.04: Case Study Analysis
9.10: Detailed Figure Captions - Chapter 9
9.11: References
10: Predictive Analytics and Machine Learning Basics
10.01: Introduction to Predictive Analytics
10.02: Supervised vs. Unsupervised Learning
10.03: Overview of Common Algorithms in Predictive Analytics
10.3.01: Linear Regression
10.3.02: Logistic Regression
10.3.03: Decision Trees
10.3.04: Random Forests
10.3.05: Clustering Techniques (K-Means and Hierarchical Clustering)
10.04: Model Evaluation and Validation
10.4.01: Training Testing and Validation Splits
10.4.02: Cross-Validation
10.4.03: Confusion Matrix and Classification Metrics
10.4.04: Evaluating Model Generalization
10.05: Overfitting and Generalization
10.06: When to Use Machine Learning vs Traditional Analytics Methods
10.07: Key Terms
10.08: Assessments
10.8.01: Review Questions
10.8.02: Critical Thinking Questions
10.8.03: Hands-On Lab Exercise
10.8.04: Case Study Analysis
10.09: Detailed Figure Captions - Chapter 10
10.10: References
11: Business Intelligence and Embedded Analytics
11.01: Understanding Business Intelligence (BI) Fundamentals
11.1.01: Definition and Key Concepts of BI
11.1.02: BI Architecture and Core Components
11.1.03: Data Warehousing Concepts in BI
11.1.04: OLAP vs OLTP Systems
11.02: Review of the BI Tool Landscape
11.2.01: Enterprise BI Platforms
11.2.02: Open-Source BI Tools
11.2.03: Cloud-Native BI Solutions
11.2.04: Embedded Analytics Platforms and Integrations
11.03: Key Terms
11.04: Assessments
11.4.01: Review Questions
11.4.02: Critical Thinking Questions
11.4.03: Hands-On Lab Exercise
11.4.04: Case Study Analysis
11.05: Detailed Figure Captions - Chapter 11
11.06: References
12: Programming-Based Analytics Tools
12.01: Python for Data Analytics
12.1.01: Jupyter Notebooks and Development Environments
12.1.02: Data Loading and Manipulation (Python)
12.1.03: Modeling with scikit-learn
12.1.04: Tool Summary- Python Libraries
12.02: R for Statistical Analysis
12.2.01: R Studio and R Markdown
12.2.1.01: Data Wrangling and Visualization in R
12.2.1.02: Statistical Modeling in R
12.2.02: Tool Summary - R Packages
12.03: SQL for Data Manipulation
12.3.01: Query Performance Optimization
12.3.02: SQL Code Examples
12.04: Integration between Tools and Workflows
12.4.01: Environments and Collaboration
12.4.02: Tool Comparison Table
12.05: Key Terms
12.06: Assessments
12.6.01: Review Questions
12.6.02: Critical Thinking Questions
12.6.03: Hands-On Lab Exercise
12.6.04: Case Study Analysis
12.07: Detailed Figure Captions - Chapter 12
12.08: References
13: Cloud Analytics Platforms and Real-World Case Studies
13.01: Cloud Analytics Landscape
13.1.01: Major Cloud Analytics Services
13.1.02: Serverless Analytics Architectures
13.1.03: Cost Optimization Strategies
13.02: Real-World Case Studies
13.2.01: Retail - Customer Analytics and Personalization
13.2.02: Healthcare - Clinical Insights and Predictive Diagnostics
13.2.03: Finance - Risk Modeling and Fraud Detection
13.2.04: Manufacturing - Predictive Maintenance and IoT Analytics
13.03: Lessons Learned and Best Practices
13.04: Conclusion
13.05: Key Terms
13.06: Assessments
13.6.01: Review Questions
13.6.02: Critical Thinking Questions
13.6.03: Hands-On Lab Questions
13.6.04: Case Study Analysis
13.07: Detailed Figure Captions - Chapter 13
13.08: References
14: Machine Learning for Analytics and Future Trends
14.01: Advanced Machine Learning Techniques for Business Analytics
14.02: Automated Machine Learning (AutoML) and Democratization
14.03: AI-Powered Analytics and Augmented Intelligence
14.04: Emerging Trends in Analytics
14.4.01: Real-Time Analytics and Edge Computing
14.4.02: Explainable AI (XAI) and Model Interpretability
14.4.03: Data Mesh and Decentralized Analytics Architectures
14.4.04: Privacy-Preserving Analytics (Differential Privacy Federated Learning)
14.4.05: Comparing Traditional vs. Emerging Methods
14.05: Key Terms
14.06: Assessments
14.6.01: Review Questions
14.6.02: Critical Thinking Questions
14.6.03: Hands-On Lab Questions
14.6.04: Case Study Questions
14.07: Detailed Figure Captions - Chapter 14
14.08: References
15: New Chapter
16: New Chapter 1