홈 / 강좌 / 프로그래밍

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.

무료 수강 신청 → 강좌 미리보기 →

학습 내용

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

커리큘럼

강좌 구성

9개 섹션 · 36개 강의. 각 섹션을 펼쳐 어떤 내용을 다루는지 확인해 보세요.

1. The Four Foundations 4개 강의

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

2. Series, Composition and Uncertainty 4개 강의

· 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 4개 강의

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

4. Distributions First 4개 강의

· 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 4개 강의

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

6. Beyond the Basics 4개 강의

· 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 4개 강의

· 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 4개 강의

· 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 4개 강의

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

강좌 소개

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.

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