Your raw data won't sort, size up, or summarize itself. In this module, you'll apply Python's built-in functions to control numeric precision, summarize a dataset with quick statistics, and sort, filter, and transform your data into the shape you actually need.

Your datasets are only going to grow, and plain Python lists can't keep up forever. In this module, you'll create NumPy arrays, extract and slice their values, reshape them for different computations, and apply fast, vectorized operations across entire datasets at once.

You can eyeball a dataset all day and still miss what actually matters inside it. In this module, you'll compute summary statistics, solve matrix-based problems, and run a hypothesis test to determine whether a difference in your data is real or just noise.

Your data rarely arrives in the shape you actually need to work with it. In this module, you'll create pandas Series and DataFrames, load data from a CSV file, and modify your DataFrames by handling missing values and adding, dropping, and renaming columns.

Your data almost never comes to you in one clean table. In this module, you'll combine data from multiple pandas objects, group it by category to compare patterns, and apply your own custom functions to transform it exactly the way you need.

Numbers on their own rarely convince anyone of anything. In this module, you'll create line, scatter, bar, and pie charts, examine distributions and pairwise relationships with seaborn, and organize multiple visualizations together using subplots.

You've built a full toolkit of Python functions, and the only way to make it truly yours is to use it. In this module, you'll apply that full toolkit to a real dataset you choose, from inspecting it to visualizing your results.