From Adding Numbers to Analyzing Real Data in Python
The sessions in this unit provide compact introductions to essential Python programming concepts for scientific computing and data analysis. They cover Python’s fundamental data types, how to store and manipulate data using variables, and perform basic mathematical operations in Python. Building on these foundations, you’ll explore NumPy for efficient array-based computations, Pandas for analyzing tabular datasets, and the visualization libraries Matplotlib and Seaborn for creating plots and figures. These exercises provide hands-on experience with data manipulation, statistical analysis, and visual exploration of real-world datasets.
Tools
Sessions
Intro to Python and Numpy
Variables, types, summary statistics, arrays, and code speed
Array Programming in Numpy
Working with data in numpy arrays. Accessing data with indices and boolean filtering
Analyzing Tabular Data With Pandas
How to work with datasets in a tabular format using the Pandas ecosystem
Data Visualization with Matplotlib and Seaborn
Imparative plotting with Matplotlib vs. declarative plotting with Seaborn
Statistics with Pingouin
Perform statistical tests including t-tests, ANOVA, and correlations using Pingouin