Data Science, AI Systems & Machine Learning Engineering
Master Statistical Computing, Exploratory Data Analysis, Production ML Pipelines & Generative AI
Bridge the gap between raw statistical modeling, exploratory data analysis, leak-proof scikit-learn pipelines, and enterprise Generative AI deployments. Master hypothesis testing, QLoRA fine-tuning, LangChain/LlamaIndex RAG, agentic tool-calling, and high-availability MLOps runbooks.
Target Career Outcomes
Graduates of this track qualify for senior and lead roles across industry verticals:
- Data Scientist
- Machine Learning Engineer
- AI Systems Engineer
- MLOps Infrastructure Architect
- Quantitative Data Analyst
Technologies & Frameworks:
Complete 4-Course Curriculum
Every course builds systematically on previous foundations, transitioning learners from theory to production systems.
Python Zero to Hero: Complete Programming Mastery
Master Python 3.12 from absolute zero to hero-level fluency: syntax, data structures, OOP architecture, robust engineering, and scientific computing
The definitive zero-to-hero foundation for software engineering, data science, and AI. This course takes learners from absolute ground zero to professional, hero-level mastery across 7 comprehensive modules and 45 hands-on lessons. Covering core syntax, advanced collections, control flow, functional programming, OOP design patterns, custom exceptions, robust file I/O, modern Python 3.12 features (structural pattern matching, type hints, dataclasses), pytest testing, and an introduction to the scientific stack (NumPy and Pandas). Learners emerge with the engineering depth and confidence required for modern software development and advanced data science.
Exploratory Data Analysis & Statistical Computing with Python
Master data wrangling, empirical distributions, and hypothesis testing in Python
The essential foundational course for modern data science and AI engineering. Build rock-solid data intuition by wrangling unstructured datasets, assessing data cleanliness programmatically, visualizing probability distributions, performing hypothesis tests, and delivering three comprehensive capstone projects.
Applied AI Systems & Machine Learning Engineering
Build end-to-end ML pipelines, MLOps serving, LangChain RAG, and QLoRA fine-tuning
The core systems engineering curriculum for AI practitioners. Journey from linear and logistic regression models through leakage-free scikit-learn transformers, model evaluation under class imbalance, containerized model serving with FastAPI and Docker, to cutting-edge Generative AI: LangChain/LlamaIndex vector databases, retrieval-augmented generation (RAG), and PEFT/QLoRA parameter-efficient fine-tuning on consumer GPUs.
The AI Engineer’s Production Cookbook & Ops Manual
20 battle-tested recipes, triage decision trees, and production deployment checklists
The operational desk reference for running production AI systems. Features copy-paste implementation recipes, triage decision trees for debugging hallucinations and covariate drift, GPU memory calculation formulas, cost optimization runbooks, and pre-flight launch checklists.
Graduate with Defensible GitHub Artifacts
Employers and hiring managers don't want to hear about multiple choice test scores. When you complete this track, you walk away with real, production-tested deliverables: code repositories, security review bundles, and system designs that demonstrate your expertise.
Track Enrollment Options
Learn at your own pace with continuous access to all 115 lessons, lab repositories, and automated quizzes.
Join a scheduled cohort with weekly live code walkthroughs, direct instructor feedback, and peer study groups.