Amelia S.
Sample The critique checklist is great
I use the checklist before every presentation now.
Data visualization in Python: charts people understand
A free course on choosing the right chart and building it well with matplotlib, seaborn and Plotly, from exploration to a shared report
Created by Priya Raman
- Updated September 2026
- English
- All levels
What you will learn
- Choose an appropriate chart type for comparisons, trends, distributions and relationships
- Build and fully customise charts with matplotlib's figure and axes interface
- Create exploratory statistical charts quickly with seaborn
- Use colour, labels and annotations to guide the reader
- Replace cluttered charts with small multiples
- Make shareable interactive charts with Plotly
Course content
Principles and first charts
Exploring data with seaborn
- Video: seaborn basics and tidy data 11 min
- Video: Distributions and comparisons 11 min
- Video: Relationships and regression lines 10 min
- Article: The seaborn objects interface 9 min
Clarity and layout
- Video: Annotations that explain 10 min
- Video: Small multiples instead of clutter 12 min
- Video: Fixing a cluttered chart step by step 11 min
- Video: Showing uncertainty in charts 10 min
- Article: Typography and sizes for slides and reports 8 min
Interactive charts with Plotly
- Video: Plotly Express in a few lines 10 min
- Article: When interactivity helps and when it hurts 8 min
- Video: Maps and geographic data 11 min
- Article: Animated charts with care 8 min
- Video: Sharing interactive charts as HTML 9 min
Putting it into practice
- Article: A chart critique checklist 8 min
- Video: Building a one page visual report 14 min
- Video: Charts in dashboards 9 min
- Article: Next steps and resources 6 min
- Quiz: Final check 5 min
Requirements
- Basic Python and a little pandas
Description
A good chart answers a question at a glance. A bad chart makes people squint, misread or stop trusting the analysis. The difference is rarely the library you use. It is the choices: which chart type, which comparison, which colours, which labels and what to leave out.
This free course teaches those choices together with the Python tools to make them real. We use matplotlib for full control, seaborn for fast statistical charts, and Plotly for interactive charts you can share in a browser.
What you will learn
- How to pick a chart from the question you want to answer
- The anatomy of a matplotlib figure, and how to control every part
- Fast exploratory charts with seaborn
- Colour that helps rather than decorates, including for colour blind readers
- Labels, titles and annotations that tell the reader what to see
- Small multiples instead of crowded charts
- Interactive charts with Plotly and when interactivity is worth it
- Exporting charts for slides, reports and the web
The approach
Each section starts with a real question about a public dataset, shows a first attempt that is technically correct but hard to read, and improves it step by step. You will learn to critique charts, including your own, using a short checklist.
You need basic Python and some pandas. Design experience is not required. The course is useful whether you are an analyst, a data scientist, an engineer building dashboards or a student.
Who this course is for
- Analysts and data scientists who want clearer charts
- Engineers building internal dashboards
- Students presenting data in reports
Instructor
Priya Raman
Data analyst teaching pandas, charts and clear answers from messy data
- 4.5 instructor rating
- 15 reviews
- 8,571 students
- 2 courses
- Sample
I work as a data analyst, which mostly means taking messy spreadsheets and database exports and turning them into answers people can act on. My daily tools are Python, pandas, SQL and Jupyter, with Polars creeping in for larger files.
I teach with public datasets so you see the real problems: missing values, odd date formats, duplicated rows. We clean them together and then make charts that are easy to read.
I believe a simple, well labelled chart beats a clever one, and that the best analysis is the one someone else can rerun next month.
Student reviews
Sarah P.
Sample My charts look professional now
The highlight technique and takeaway titles changed my reports overnight. My manager noticed immediately.
Ibrahim N.
Sample Free and excellent
Clear explanations of why some charts work and others do not, not just how to code them.
Rashid K.
Sample Changed how I think about charts
Starting from the question instead of the data is such a simple idea, and it works.
Lena W.
Sample Great matplotlib foundation
The object oriented interface finally makes sense. The Plotly section is shorter than I hoped.
Carlos J.
Sample Small multiples lesson is gold
Replacing a spaghetti line chart with small multiples was the single biggest improvement in my work.
Hye-won L.
Sample Accessible colour advice
Very useful guidance on colour vision deficiencies. I now check every chart with a simulator.
Patrick O.
Sample Wanted more dashboards
The chart design content is strong. The dashboard lesson felt brief compared with the rest.
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