Matplotlib & Data Visualization
Communicate data insights through clear, publication-quality visual charts.
Course overview
Visual communication turns raw numbers into actionable understanding. This course teaches Matplotlib's figure and axes architecture, creating publication-ready line plots, bar charts, scatter plots, histograms, box plots, custom styling, color palettes, and Seaborn statistical visualization.
Prerequisites
What you'll achieve
- Build clear, attractive visual plots using Matplotlib and Seaborn
- Choose the right chart type for distribution, correlation, and comparison
- Format figures with titles, labels, legends, annotations, and custom colors
- Create multi-panel subplot dashboards for visual reports
Curriculum
- 1Principles of effective data visualization and visual hierarchy
- 2Matplotlib object-oriented API: Figures and Axes
- 3Plot types: Line charts, bar charts, histograms & scatter plots
- 4Customizing plots: Colors, markers, grids, legends & annotations
- 5Creating multi-chart layouts with subplots and GridSpec
- 6Statistical visualization with Seaborn (boxplots, heatmaps, pairplots)
- 7Exporting publication-ready charts (PNG, PDF, SVG)
- 8Project: Exploratory visual data storytelling dashboard
Who is this for?
Analysts, students, researchers, and developers presenting data insights.
Contact admissions for current fee and available learning formats
Enrol in Matplotlib & Data Visualization
- 4 weeks
- Beginner to Intermediate
- Live online · One-to-one available · Group batches when scheduled
- 1-on-1 & small batches
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Ready to start your learning journey?
Apply today and our admissions team will review your application and guide you through the next steps.