Data Science, Machine Learning & AI
Welcome to your Data Science & AI learning hub. Browse all courses below, or use the sections to explore a specific topic.
Getting Started
Free Roadmap: Data Science & Analytics for Finance
PDF guide with step-by-step learning path for finance professionals entering data science.
Free Roadmap: Data Science & Analytics for Finance
PDF guide with step-by-step learning path for finance professionals entering data science.
Guides
Python
This section covers Python from the ground up. You'll start with syntax and basic programming, then move to NumPy for numerical computing and Pandas for data manipulation. These are the core libraries you'll use constantly when working with data.
R Programming
R was built for statistical analysis and remains the tool of choice for many statisticians and researchers. This section covers R fundamentals, the tidyverse for data manipulation, ggplot2 for creating publication-quality visualizations, and time series analysis for financial data.
R Programming for Data Science
Data manipulation with dplyr, tidyr, and the tidyverse
Data Visualization with R
Learn how to create beautiful data visualizations in R using Base R graphics and ggplot2
Financial Time Series Analysis in R
Time series analysis, forecasting, and financial modeling
Quantitative Foundations
Data science and finance are built on quantitative foundations. This section covers essential concepts: probability theory, statistical distributions, hypothesis testing, regression, and the finance-specific math you'll need for risk, portfolio analysis, and valuation.
Statistical Concepts and Market Returns
Statistical measures applied to financial data
Probability Concepts
Fundamental probability theory and concepts
Common Probability Distributions
Normal, binomial, Poisson, and other key distributions
Probability Distributions (Advanced)
Advanced distribution concepts for risk
Sampling and Estimation
Statistical sampling and parameter estimation
Hypothesis Testing
Statistical hypothesis testing and inference
Understanding Portfolio Math
Mathematical foundations for portfolio management: returns, risk, correlation, and diversification
Data Analysis
Most data work involves cleaning, transforming, and exploring data before any modeling begins. This section covers data manipulation techniques in both Python and R, exploratory data analysis approaches, and visualization tools. You'll also learn SQL basics for querying databases.
Machine Learning
Machine learning algorithms learn patterns from data to make predictions. This section covers supervised learning (regression, classification), unsupervised learning (clustering, dimensionality reduction), and practical considerations like model evaluation and selection. Applied finance ML courses are in the Finance Applications section.
Artificial Intelligence
This section covers modern AI, particularly large language models (LLMs) like GPT and Claude. You'll learn prompt engineering for effective AI interactions, how to build RAG (retrieval-augmented generation) systems that use your own documents, and how to leverage AI tools in your data science workflow.
Courses coming soon
Finance Applications
This is where your programming, statistics, and machine learning skills come together. Each course tackles a real finance problem using the tools you've learned: building credit scorecards, backtesting trading strategies, analyzing financial time series, and pricing derivatives. These are hands-on, applied projects.
Credit Risk Modelling in R
Build credit scoring models using R - from data prep to scorecard deployment
Machine Learning in Finance Using Python
Apply ML algorithms to financial prediction problems
Quantitative Trading Strategies in R
Build and backtest systematic trading strategies
Financial Time Series Analysis in R
Analyze and forecast financial time series data
Derivatives with R
Price and analyze options and other derivatives
Portfolio Analysis in R
Portfolio optimization and performance analysis
Investment Risk and Return Analysis in Python
Learn how to evaluate investment risks and returns using Python. Covers financial risk fundamentals, return calculations, statistical measures (mean, variance, skewness, kurtosis), and practical analysis techniques for real-world investment data.