Data visualisation
Anyone whose charts are read by other people — supervisors, journals, boards — and need to survive that reading.
5 modules from the Python for Data Analysis curriculum.
What you'll learn
By the end of this course
- Explain the Figure/Axes hierarchy and why the OO interface beats pyplot state
- Create the core chart types and match each to a question
- Control every element: titles, labels, ticks, limits, legends, annotations
- Build multi-panel figures with subplots
- Apply colour deliberately, including for colour-blind readers
- Annotate charts so the finding is unmissable
- Save figures at publication quality
- Diagnose misleading charts — truncated axes, wrong chart type, chart junk
Curriculum
5 sections · 5 lessons
Section 1
5 modules
Section 1
- Matplotlib FundamentalsCharts that communicate a finding rather than merely displaying data.~120 min
- Advanced MatplotlibA reusable house style and layouts that fit the story rather than a grid.~120 min
- Seaborn FundamentalsOne-line statistical charts — and knowing when to drop back to Matplotlib.~110 min
- Statistical VisualizationsWhere the previous notebooks taught you how to draw (Matplotlib) and how to draw quickly (Seaborn fundamentals), this notebook is about what to draw when you want a chart to make a statistical argument. A statistical visualization is not just a picture of numbers.~150 min
- Dashboard-quality ChartsThe previous notebooks taught you to draw correct charts. This one is about drawing finished charts — the kind you drop into a report, a slide, or an executive dashboard without apologising.~180 min
About this course
5 modules from the Python for Data Analysis curriculum.
This course covers Matplotlib Fundamentals, Advanced Matplotlib, Seaborn Fundamentals, Statistical Visualizations and Dashboard-quality Charts.
About 11h 20m of material across 5 modules, each with the dataset it teaches from and exercises with worked solutions.
Every module is a notebook you download and run yourself in JupyterLab — the point is that you execute the code, not that you watch someone else execute it. Recordings are added module by module; a module without one yet is taught in full by its notebook.
Who it's for
- Researchers and postgraduate students who need the analysis done properly, not just done
- Analysts moving from spreadsheets to something repeatable and auditable
- Anyone who has followed a Python tutorial and still cannot open their own data and get an answer
What you'll need
- A computer you can install Python and JupyterLab on — notebook 02 walks you through it, on Windows and on macOS
- No prior programming experience for the foundations; the later tracks assume the earlier ones
- The foundations covered earlier in this curriculum, or equivalent experience
- Your own dataset is welcome but not required — every module ships with the data it teaches from
Start Data visualisation today
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