Data Science & Applied AI

Are you serious about breaking into data science or AI - but tired of scattered tutorials, half-finished courses, and "learn Python in 24 hours" promises?

This book gives you something different: a complete, structured, 14-week university-level curriculum - from Python fundamentals to building and deploying LLM-powered AI applications - without a $60,000 master's program.

Modeled on graduate-level coursework. Designed for self-directed learners.

Every week is structured like a university class:

• Clear learning objectives (what you will actually be able to do)
• Curated readings from leading textbooks and free online resources
• A real, graded-style assignment that produces a portfolio artifact
• The tools and libraries professionals use on the job

No filler. No hand-holding. Just the program.

WHAT YOU WILL COVER:

Phase 1 - Foundations (Weeks 1-3): Python, NumPy, mathematics for ML (linear algebra, calculus, probability), and exploratory data analysis with Pandas.

Phase 2 - Data Engineering and Visualization (Weeks 4-5): SQL through window functions, ETL pipeline design, data cleaning, and interactive dashboards with Plotly and Streamlit.

Phase 3 - Machine Learning (Weeks 6-9): Supervised learning, feature engineering, model interpretation with SHAP, clustering, and dimensionality reduction.

Phase 4 - Deep Learning (Weeks 10-11): Neural networks from scratch, backpropagation, PyTorch, CNNs, RNNs, and transfer learning.

Phase 5 - Applied AI (Weeks 12-13): How LLMs work, prompt engineering, retrieval-augmented generation (RAG), agentic AI, and production AI applications.

Phase 6 - Capstone (Week 14): A GitHub repository, technical research report, live deployed demo, and recorded presentation.

WHO THIS IS FOR:

• Career changers wanting a structured path into data science or AI
• Software engineers moving into ML and AI roles
• Analysts who want to go deeper into modeling and AI
• Recent graduates wanting a rigorous supplement to their degree
• Self-taught programmers tired of jumping between resources

Prerequisites: Basic programming experience, high school algebra, willingness to do the work. No prior data science knowledge required.

BY THE END OF WEEK 14, YOU WILL:

• Build and deploy production-ready ML models end-to-end
• Design and fine-tune deep learning architectures
• Build LLM-powered applications with RAG, agents, and tool use
• Communicate findings through professional data visualizations
• Present a complete capstone portfolio project to a technical audience

Stop collecting courses. Start finishing one.