Charts that actually communicate instead of just showing data. Learn matplotlib, seaborn, and Plotly — plus the design principles that separate good visualizations from confusing ones.
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import plotly.express as px # Day 3: Interactive chart with Plotly fig = px.line(df, x='date', y='revenue', color='region', title='Revenue by Region Over Time') fig.update_layout( template='plotly_white', hovermode='x unified' ) fig.show() # Interactive in browser
Most chart tutorials show you how to make a bar chart. This course shows you when to use a bar chart, when not to, and how to make it clear enough that anyone understands immediately.
Matplotlib for control. Seaborn for statistical charts. Plotly for interactive dashboards. You'll know when to reach for each one.
Day 4 is pure design: right chart type, color theory, reducing chart junk, and making labels do the work. These principles apply to any tool.
Day 5 takes a messy CSV and builds a complete visual report — five charts that tell a coherent story. Portfolio-ready output.
Create your first charts. Line charts for time series, bar charts for comparisons, scatter plots for relationships. Control titles, labels, colors, and figure size.
Use seaborn for statistical charts. Histograms, box plots, violin plots, heatmaps, and pair plots. Understand what each chart reveals about distributions.
Build interactive charts with Plotly. Hover tooltips, zoom, and filter. Create subplots and faceted views. Export to HTML for sharing.
Choose the right chart type. Apply color principles. Reduce chart junk. Write titles that tell the story, not just label the axes.
Start with a messy CSV. Clean it with pandas. Build five charts that tell a story. Export as self-contained HTML. Walk away with a portfolio piece.
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Three days of intensive, hands-on AI training. Build production systems with real data, real APIs, and a cohort of peers. $1,490 all-in. Coming to 5 cities in October 2026.