Free Self-Paced Course
Advanced Data Science
9 Weeks · 299 Pages · Computer Science — Data Science
This free, self-paced course provides a structured 9-week syllabus for learning Advanced Data Science. Each module builds on the previous one, guiding you from foundational concepts through advanced topics with 299 pages of in-depth reading material. All content is drawn from our comprehensive Advanced Data Science textbook, organized into a clear weekly schedule that you can follow at your own pace.
Weekly Syllabus
- Part II: Deep Learning
- Chapter 6: Neural Networks from Scratch
- Chapter 7: Training Deep Networks — Initialization, Batch Normalization, Dropout, Learning Rate Schedules, and the Dark Art of Making It Converge
- Chapter 8: Convolutional Neural Networks — Architecture, Intuition, and Computer Vision Applications
- Chapter 9: Recurrent Networks and Sequence Modeling — RNNs, LSTMs, GRUs, and Their Limitations
- Chapter 10: The Transformer Architecture — Attention Is All You Need (and Why It Changed Everything)
- Chapter 11: Large Language Models — Architecture, Training, Fine-Tuning, RAG, and Practical Applications
- Chapter 12: Generative Models — VAEs, GANs, Diffusion Models, and the Frontier of Data Generation
- Chapter 13: Transfer Learning, Foundation Models, and the Modern Deep Learning Workflow
- Chapter 14: Graph Neural Networks and Geometric Deep Learning — When Your Data Has Structure Beyond Grids and Sequences
- Part III: Causal Inference
- Chapter 15: Beyond Prediction — Why Correlation Isn't Enough and What Causal Inference Offers
- Chapter 16: The Potential Outcomes Framework — Counterfactuals, ATEs, and the Fundamental Problem of Causal Inference
- Chapter 17: Graphical Causal Models — DAGs, d-Separation, and Structural Causal Models
- Chapter 18: Causal Estimation Methods — Matching, Propensity Scores, Instrumental Variables, Difference-in-Differences, and Regression Discontinuity
- Chapter 19: Causal Machine Learning — Heterogeneous Treatment Effects, Uplift Modeling, and Double Machine Learning
- Part V: Production ML Systems
- Chapter 24: ML System Design — Architecture Patterns for Real-World Machine Learning
- Chapter 25: Data Infrastructure — Feature Stores, Data Warehouses, Lakehouses, and the Plumbing Nobody Teaches
- Chapter 26: Training at Scale — Distributed Training, GPU Optimization, and Managing Compute Costs
- Chapter 27: ML Pipeline Orchestration — Airflow, Dagster, Prefect, and Designing Robust Data Workflows
- Chapter 28: ML Testing and Validation Infrastructure — Data Contracts, Behavioral Testing, and Great Expectations
- Chapter 29: Continuous Training and Deployment — CI/CD for ML, Canary Deployments, Shadow Mode, and Progressive Rollout
- Chapter 30: Monitoring, Observability, and Incident Response — Keeping ML Systems Healthy in Production
- Part VII: Leadership and Synthesis
- Chapter 36: Capstone — Designing, Building, and Governing a Production Recommendation System
- Chapter 37: Reading Research Papers — How to Stay Current, Evaluate Claims, and Separate Signal from Hype
- Chapter 38: The Staff Data Scientist — Technical Leadership, Mentoring, Strategy, and Shaping the Roadmap
- Chapter 39: Building and Leading a Data Science Organization — Hiring, Team Structure, Culture, and Scaling Impact
How to Use This Syllabus
- Read at your own pace. Each module is designed for roughly one week of study, but there are no deadlines. Spend as much time as you need on each topic.
- Follow in order or jump around. The modules are arranged sequentially for a structured learning path, but feel free to skip to any topic that interests you most.
- No sign-up needed. Every page in this syllabus links directly to free, open-access content. Just click a topic and start reading immediately.
Ready to Start Learning?
Access the full Advanced Data Science textbook with all chapters, examples, and exercises.
Open the Full Textbook