Python
Python programming for data science, from fundamentals to data manipulation with Pandas and NumPy.
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.
Why Python for Data Science?
Python has become the dominant language in data science for several reasons: readable syntax that's easy to learn, a massive ecosystem of libraries (NumPy, Pandas, scikit-learn, TensorFlow), strong community support, and versatility beyond just analysis. You can build web apps, automate tasks, and deploy models all in one language.
What You'll Learn
The courses below take you from Python basics through to professional-level data manipulation. You'll learn to work with arrays and matrices using NumPy, clean and transform data with Pandas, and handle real-world datasets with missing values, multiple file formats, and complex transformations.
Practical Applications
Every example uses financial data: stock prices, economic indicators, portfolio returns. You'll build scripts that automate data collection, create reusable analysis functions, and learn patterns you can apply directly at work.
