Data Types in Python

It assumes basic Python familiarity and is best suited for learners who have written some Python but want to deepen their understanding of how data is stored and structured. It is not intended as a first introduction to programming.

The course covers lists, tuples, strings, dictionaries, integers, floats, booleans, and sets in the first three chapters, then moves into advanced types from the collections module including Counter, defaultdict, and namedtuple, plus dataclasses.

The first two chapters apply each data type to the New York Baby Names dataset, while the third chapter uses Chicago metropolitan area transit data for a more extended, multistep case study.

The final chapter covers three collections types: Counter for frequency counting, defaultdict for building dictionaries without knowing their structure in advance, and namedtuple for storing records with named fields instead of positional indexes.

The course covers this directly in chapter three, showing how sets are optimized for logic operations like finding differences and overlaps between groups of data, tasks where lists and dictionaries are less efficient.

Python users in data-related roles who regularly work with structured data and want to choose the right data type for each situation rather than defaulting to lists and dictionaries for everything.