Data Science

Index

• 1. Foundations of Data Science
  • 1.1 Discipline and Scope
  • 1.2 The Data Science Lifecycle
  • 1.3 Types of Data
  • 1.4 Framing Analytical Questions
• 2. Mathematical Foundations
  • 2.1 Linear Algebra
  • 2.2 Calculus and Optimization
  • 2.3 Probability Theory
  • 2.4 Discrete Mathematics and Information Theory
• 3. Statistical Foundations
  • 3.1 Descriptive Statistics
  • 3.2 Sampling and Estimation
  • 3.3 Hypothesis Testing
  • 3.4 Bayesian Statistics
  • 3.5 Causal Inference
• 4. Programming and Tooling
  • 4.1 Python for Data Science
  • 4.2 R for Statistical Computing
  • 4.3 SQL and Query Languages
  • 4.4 Development Environment and Practices
• 5. Data Acquisition and Storage
  • 5.1 Data Sources
  • 5.2 Data Storage Systems
  • 5.3 Data Pipelines
• 6. Data Wrangling and Preparation
  • 6.1 Data Cleaning
  • 6.2 Data Transformation
  • 6.3 Feature Engineering
  • 6.4 Handling Imbalanced and Skewed Data
• 7. Exploratory Data Analysis and Visualization
  • 7.1 Exploratory Analysis Workflow
  • 7.2 Visualization Principles
  • 7.3 Visualization Practice
  • 7.4 Dashboards and Reporting
• 8. Experimentation and Decision Science
  • 8.1 Experimental Design
  • 8.2 A/B Testing in Practice
  • 8.3 Quasi-Experiments and Observational Studies
  • 8.4 Decision Analysis
• 9. Machine Learning Fundamentals
  • 9.1 Core Concepts
  • 9.2 Generalization
  • 9.3 Model Evaluation
  • 9.4 Model Selection and Tuning
• 10. Supervised Learning
  • 10.1 Regression
  • 10.2 Classification
• 11. Unsupervised Learning
  • 11.1 Clustering
  • 11.2 Dimensionality Reduction
  • 11.3 Pattern Discovery
  • 11.4 Anomaly Detection
• 12. Deep Learning
  • 12.1 Neural Network Basics
  • 12.2 Architectures
  • 12.3 Training at Scale
  • 12.4 Transfer and Foundation Models
• 13. Specialized Data Domains
  • 13.1 Time Series Analysis
  • 13.2 Natural Language Processing
  • 13.3 Computer Vision
  • 13.4 Network and Geospatial Data
  • 13.5 Recommender Systems
• 14. Big Data and Distributed Computing
  • 14.1 Scaling Concepts
  • 14.2 Distributed Processing Frameworks
  • 14.3 Streaming Data
  • 14.4 Performance Engineering
• 15. Productionizing Models and MLOps
  • 15.1 Model Deployment
  • 15.2 ML Engineering Infrastructure
  • 15.3 Monitoring and Maintenance
  • 15.4 Collaboration and Governance of Code
• 16. Interpretability, Ethics, and Governance
  • 16.1 Model Interpretability
  • 16.2 Fairness and Bias
  • 16.3 Privacy and Security
  • 16.4 Governance and Regulation
• 17. Communication and Professional Practice
  • 17.1 Communicating Results
  • 17.2 Working with Stakeholders
  • 17.3 Reproducibility and Rigor
  • 17.4 Applied Capstone Practice

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