Learning Library
Browse free courses and practical ebook-based resources on data science, machine learning, AI, quant finance, and other finance concepts.
Getting Started with Python
Learn Python fundamentals: syntax, data types, and basic programming concepts.
Getting Started with R Programming
Learn R basics including syntax, data types, and RStudio.
Python for Data Science
NumPy, data structures, and essential tools for data science.
Data Manipulation Using Pandas
Master Pandas for data wrangling and analysis.
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
Introduction to Machine Learning
ML fundamentals: concepts, workflow, and key algorithms.
Machine Learning in Finance Using Python
Apply ML algorithms to financial prediction problems using Python and scikit-learn.
Credit Risk Modelling in R
Build credit scoring models using R - from data prep to scorecard deployment.
Financial Time Series Analysis in R
Analyze and forecast financial time series data.
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.
Quantitative Trading Strategies in R
Build and backtest systematic trading strategies.
Derivatives with R
Price and analyze options and other derivatives.
SQL for Data Analysis - Foundations
A practical guide to SQL for analysts. Learn to query data, join tables, build summaries, and write the SQL that answers real business questions.