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Python Data Visualization with Matplotlib: From Core Charts to Publication Polish

Master matplotlib end to end - build every core chart, visualize statistics honestly, and polish to publication quality, all in live code.

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学習内容

Build the core matplotlib chart types from scratch in plain Python
Choose the right chart for a given question and defend the choice
Visualize distributions, spread, and correlation honestly with box plots, violins, ECDFs, fit lines, and heatmaps
Show uncertainty with error bars you can defend - and spot overfitting on sight
Control the parameters that matter: bins, alpha, scales, widths, legends, and colormaps
Recognize and avoid classic distortions: truncated axes, unsorted bars, misleading pies, rainbow colormaps
Take any chart from default output to publication quality, step by step
Define a reusable house style with rcParams and export at the right DPI and format
Compose multi-panel figures and dashboards with subplots and gridspec

カリキュラム

コースの中身

9セクション · 36本のレクチャー. 各セクションを開くと内容を確認できます。

1. The Four Foundations 4本のレクチャー

▸ The Line Chart: Reading a Trend プレビュー
· The Scatter Plot: Two Variables, One Truth
· The Bar Chart: Ranking Categories
· The Histogram: The Shape of a Column

2. Series, Composition and Uncertainty 4本のレクチャー

· Multiple Series and the Art of the Legend
· The Pie Chart and When Not to Use It
· Error Bars: Showing What You Don't Know
· Twin Axes: Power Tool, Loaded Gun

3. Layout, Scales and Judgment 4本のレクチャー

· Subplots: One Figure, Many Views
· Annotation: Pointing at the Story
· Scales: When Linear Lies
· The Chart Chooser: Same Data, Four Charts

4. Distributions First 4本のレクチャー

· Distributions First: The Histogram Revisited
· The Box Plot: Five Numbers That Matter
· The Violin Plot: Shape Is Information
· Jitter: Showing Every Tree

5. Correlation and Confidence 4本のレクチャー

· Correlation: Scatter Plus the Fit Line
· The Correlation Heatmap
· Comparing Groups Without Lying
· Confidence: Error Bars That Mean Something

6. Beyond the Basics 4本のレクチャー

· The ECDF: Every Point, No Bins
· Density in Two Dimensions
· Small Multiples: Four Panels, One Scale
· The Statistical Story: A Four-Panel Report

7. The Clean Chart 4本のレクチャー

· The Grammar of a Clean Chart
· Color With Intent
· Colormaps: Sequential, Diverging, Never Rainbow
· Typography and Labels That Respect the Reader

8. Ink, Emphasis and Annotation 4本のレクチャー

· Gridlines, Spines and the Ink Budget
· Annotation as Narration
· The Highlight: One Line Among Many
· Layout: Gridspec and Breathing Room

9. Style Systems and Delivery 4本のレクチャー

· Legends and Their Alternatives
· Themes with rcParams: Your House Style
· Export Discipline: DPI, Formats, Bounding
· The Makeover: From Default to Publication

このコースについて

The complete matplotlib journey in one course. Across 36 lessons - every chart built live in real code, every parameter changing on screen - you move from the core chart types (line, scatter, bar, histogram, legends, pies, error bars, twin axes, subplots, annotation, scales), through statistical visualization (box plots, violins, jitter, correlation and fit lines, heatmaps, ECDFs, 2D density, small multiples), to the craft of publication polish (color with intent, colormap classes, typography, the ink budget, annotation as narration, gridspec layout, rcParams house styles, and export discipline). Three consistent datasets, one honest method: the chart is the code, and the code runs.

対象者

Python learners who want a complete, real data-visualization skill
Analysts and researchers who present data and want their charts to be right
Data science learners who can plot but cannot yet argue with a chart
Anyone who needs to turn numbers into clear, honest, publication-ready visuals

受講の前提条件

Basic Python (variables, lists, calling functions)
A computer that can run Python with matplotlib and numpy
No prior plotting or statistics knowledge needed

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