Module A — Python environment and readable code

You install a virtual environment, run scripts from the terminal, and adopt a consistent project layout for data work. Exercises cover variables, collections, control flow, functions, and error handling patterns that make notebooks easier for others to run top-to-bottom. Style guidance emphasizes meaningful names, small functions, and comments that explain why a step exists rather than restating syntax. You also compare notebooks with plain modules so you know when to refactor exploratory code into reusable utilities as a dataset grows.

Module B — NumPy and pandas for real tables

Lessons move from arrays to DataFrames, teaching merges, joins, group-by summaries, and reshaping operations analysts use daily. You practice identifying duplicate keys, diagnosing silent Cartesian explosions, and filling missing values with explicit assumptions documented in markdown cells. Performance tips introduce chunking large CSVs and choosing dtypes that balance memory with precision. Case files mimic product usage logs and marketing exports so joins feel familiar rather than academic.

Module C — Visualization for exploration and reporting

You build matplotlib and seaborn charts that highlight distributions, trends, and segment comparisons without misleading axes. Color guidance covers contrast for accessibility and consistent palettes across a deck. Exercises include small multiples, faceting, and annotations that explain outliers. You learn to export figures at appropriate resolution and to pair each chart with a short interpretation a stakeholder could read without the notebook.

Module D — Capstone project

The capstone asks you to formulate a question, ingest and clean messy inputs, compute reproducible metrics, and deliver a concise recommendation memo. Peer review evaluates clarity of assumptions, edge-case handling, and how well visuals support the final storyline. You also submit a short README describing how to rerun the analysis on refreshed data, reflecting how teams hand off work between analysts.

Module E — Professional habits and next steps

You reflect on notebook hygiene, code review etiquette, and how to request compute or data access through proper channels. Career topics cover portfolio storytelling with anonymized outputs and how to describe uncertainty honestly in interviews. A supplemental reading list points to trustworthy documentation and community resources so learning continues after the bootcamp ends.