Getting Started
Set up your tools, write your first code, and build the foundation for everything that follows.
What is Data Science?
Data science is extracting insights from data using programming, statistics, and domain knowledge. In finance, this means analyzing market data, building forecasting models, automating reports, and creating visualizations. You'll work with tools like Python, pandas, and SQL to clean, transform, and analyze data.
What is Machine Learning?
Machine learning is teaching computers to learn patterns from data and make predictions. In finance, ML powers credit scoring, fraud detection, algorithmic trading, and risk modeling. You'll use libraries like scikit-learn and XGBoost to build models that improve with more data.
What is AI?
AI encompasses systems that can perform tasks requiring human-like intelligence. Today, this often means working with large language models (LLMs), building chatbots, automating document processing, and creating AI-powered tools. You'll learn prompt engineering, RAG systems, and how to integrate AI APIs into applications.
What This Section Covers
You'll install Python or R, set up VS Code or RStudio, and learn to use Jupyter notebooks for interactive coding. The starter courses teach programming fundamentals: variables, data types, loops, functions, and working with files. By the end, you'll be comfortable writing basic programs and ready for the data manipulation and analysis that comes next.
Python or R?
Python is the industry standard - used by most companies, essential for ML and AI, and versatile enough for automation, web scraping, and building applications. The ecosystem includes pandas for data, scikit-learn for ML, and countless AI libraries. R was built for statistics and shines at statistical modeling, hypothesis testing, and publication-quality visualizations with ggplot2. Many finance professionals learn both eventually: Python for production work and R for statistical analysis. If you're choosing one to start, go with Python.
Free Roadmap
Download our free mini roadmap - a step-by-step guide 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.
Quick Guides
Short reads to help you get set up and understand key concepts.
- ⚙️Setting Up Your Development Environment— Install Python, VS Code, and essential tools
- 🤔Python vs R: Which Should You Learn?— A practical comparison for finance professionals
- 📓Getting Started with Jupyter Notebooks— Interactive coding for data analysis
Start with These Courses
Both courses are free and will get you writing code within hours. If you're unsure which to choose, start with Python — it's more versatile and widely used in industry.
