From default matplotlib blobs to publication-quality figures. Matplotlib foundations, Seaborn statistical charts, interactive Plotly dashboards, and the design principles that separate charts that inform from charts that confuse.
This is a text-first course that links out to the best supporting material on the internet instead of trying to replace it. The goal is to make this the best course on data visualization and visual storytelling you can find — even without producing a single minute of custom video.
This course is built by people who ship production data systems for a living. It reflects how things actually work on real projects — not how the documentation describes them.
Every day has working code snippets you can paste into your editor and run right now. The emphasis is on understanding what each line does, not memorizing syntax.
Instead of shooting videos that go stale in six months, Precision AI Academy links to the definitive open-source implementations, official documentation, and the best conference talks on the topic.
Each day is designed to finish in about an hour of focused reading plus hands-on work. You can do the whole course over a week of lunch breaks. No calendar commitment, no live classes, no quizzes.
Each day stands alone. Read them in order for the full picture, or jump straight to the day that answers the question you have today.
The object-oriented API most tutorials skip. Figures, axes, subplots, and the configuration system that lets you control every pixel. Build a reusable chart template.
Distribution plots, regression plots, categorical charts, and pair plots — the charts that reveal statistical patterns in one function call.
Hover tooltips, zoom, pan, and animated charts for web embedding. Plotly Express for fast charts; go.Figure for full control.
Color, contrast, data-ink ratio, and the chart types that match each data shape. The principles from Edward Tufte applied to Python charts.
Combine matplotlib, Seaborn, and Plotly into a script that generates a multi-page PDF or HTML report from raw data. The capstone for everything in the course.
Instead of shooting our own videos, Precision AI Academy links to the best deep-dives already on YouTube. Watch them alongside the course. All external, all free, all from builders who ship this stuff.
Comprehensive walkthroughs of the OO API, subplots, and the style system that most analysts never learn.
Statistical chart types, the FacetGrid, and how Seaborn integrates with pandas DataFrames.
Building interactive charts and embedding them in web apps. Covers Plotly Express and Dash components.
Design principles, color theory, and the chart choices that communicate data honestly.
The best way to understand any technology is to read the production-grade implementations that prove it works. These repositories implement patterns from every day of this course.
The foundation of this course. Reading the source for Axes and Figure clarifies why some configuration changes stick and others don't.
Statistical visualization built on matplotlib. The source for FacetGrid and PairGrid shows how it coordinates multiple axes.
Interactive charts for web embedding. The graph_objects module shows how Plotly's JSON spec maps to chart properties.
Declarative visualization based on Vega-Lite. A different mental model from matplotlib — worth knowing when Plotly feels like too much.
You spend hours in Excel making charts that still look wrong. This course teaches the Python workflow that produces better charts in less time.
Your model is great. The chart explaining it is not. This course covers the design principles that make statistical results legible to non-statisticians.
You need to generate charts programmatically. This course covers the full Python visualization stack from static PDFs to interactive web embeds.
The 2-day in-person Precision AI Academy bootcamp covers data visualization and visual storytelling hands-on. 5 U.S. cities. $1,490. 40 seats max. June–October 2026 (Thu–Fri).
Reserve Your Seat