Introduction to Python for Data Science

Learn Python fundamentals and data analysis by writing programs, working with data structures, exploring datasets, creating visualizations, and applying analytical techniques to real-world data.

Familiarize yourself with the building blocks of Python! Learn about data types and operators, built-in functions, type conversion, whitespace, and style guidelines.

Use data structures to order and group different data types together! Learn about the types of data structures in Python, along with more useful built-in functions and operators.

Build logic into your code with control flow tools! Learn about conditional statements, repeating code with loops and useful built-in functions, and list comprehensions.

Learn how to use functions to improve and reuse your code! Learn about functions, variable scope, documentation, lambda expressions, iterators, and generators.

Leverage Python libraries and packages to solve data analysis problems efficiently, install and manage tools with pip, and use community-built resources to accelerate insight generation.

Explore datasets with Pandas and Polars by loading data into DataFrames, inspecting structure and quality, generating summaries, and comparing tools to support effective analysis workflows.

Analyze and summarize data with Pandas using filtering, aggregation, grouping, and pivot tables to uncover patterns, compare categories, and generate actionable insights.

Visualize and communicate data insights with charts that reveal distributions, trends, correlations, and outliers while selecting effective techniques for exploration and storytelling.

Review key Python, data analysis, and visualization skills while reinforcing workflows for exploring data and communicating insights effectively.