Learn
The sections below are organized by topic. If you're new, begin with Getting Started to set up your environment and learn the basics. Otherwise, pick any section that matches what you want to learn. Each contains courses, code examples, and exercises.
Getting Started
Your launchpad into data science, ML, and AI. Learn what these fields involve, set up your tools, and take your first steps.
Python
Python programming for data science, from fundamentals to data manipulation with Pandas and NumPy.
R Programming
R for statistical computing: data manipulation with tidyverse, visualization with ggplot2, and time series analysis.
Quantitative Foundations
Statistics, probability, and finance math. The quantitative skills that underpin data science and finance.
Data Analysis
Turn raw data into insights. Data wrangling, exploratory analysis, SQL, and visualization techniques.
Machine Learning
Build predictive models. Supervised and unsupervised learning, model evaluation, and real-world applications.
Artificial Intelligence
Large language models, prompt engineering, RAG systems, and AI tools for productivity.
Finance Applications
Apply your skills to real finance problems. Risk modeling, trading strategies, portfolio analysis, and capstone projects.