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Seaborn

A comprehensive Seaborn course for statistical visualization in AI and data science, covering themes, relational plots, categorical plots, distributions, regression plots, heatmaps, pair plots, faceting, model analysis, and clear exploratory data visualizations.

Course map
Beginner → practical: This 60-chapter course starts with the basics and progresses into AI-oriented workflows. Every chapter includes simple explanations, technical words in parentheses, real-world code, expected output, step-by-step explanation, exercises, common mistakes, and Q&A.

Complete 60-Chapter Course

  1. Chapter 1: What Seaborn Is
  2. Chapter 2: Installing and Importing Seaborn
  3. Chapter 3: Seaborn and Matplotlib
  4. Chapter 4: Loading a DataFrame
  5. Chapter 5: Themes
  6. Chapter 6: Styles
  7. Chapter 7: Contexts
  8. Chapter 8: Color Palettes Concepts
  9. Chapter 9: Setting a Theme
  10. Chapter 10: Figure-Level vs Axes-Level Functions
  11. Chapter 11: Scatterplot
  12. Chapter 12: Lineplot
  13. Chapter 13: Relplot
  14. Chapter 14: Hue
  15. Chapter 15: Style Mapping
  16. Chapter 16: Size Mapping
  17. Chapter 17: Categorical Data
  18. Chapter 18: Stripplot
  19. Chapter 19: Swarmplot Concepts
  20. Chapter 20: Boxplot
  21. Chapter 21: Violinplot
  22. Chapter 22: Boxenplot Concepts
  23. Chapter 23: Barplot
  24. Chapter 24: Countplot
  25. Chapter 25: Pointplot
  26. Chapter 26: Catplot
  27. Chapter 27: Histplot
  28. Chapter 28: KDE Plot Concepts
  29. Chapter 29: ECDF Plot Concepts
  30. Chapter 30: Displot
  31. Chapter 31: Multiple Distributions
  32. Chapter 32: Bivariate Distributions
  33. Chapter 33: Jointplot
  34. Chapter 34: Pairplot
  35. Chapter 35: PairGrid Concepts
  36. Chapter 36: Regression Plots
  37. Chapter 37: Regplot
  38. Chapter 38: Lmplot
  39. Chapter 39: Residual Plot Concepts
  40. Chapter 40: Confidence Intervals Concepts
  41. Chapter 41: Heatmaps
  42. Chapter 42: Correlation Heatmaps
  43. Chapter 43: Annotations in Heatmaps
  44. Chapter 44: Clustermap Concepts
  45. Chapter 45: FacetGrid
  46. Chapter 46: Row and Column Facets
  47. Chapter 47: Facet Titles
  48. Chapter 48: Ordering Categories
  49. Chapter 49: Rotating Labels
  50. Chapter 50: Handling Missing Data
  51. Chapter 51: Visualizing Class Balance
  52. Chapter 52: Visualizing Feature Distributions
  53. Chapter 53: Visualizing Outliers
  54. Chapter 54: Visualizing Correlations
  55. Chapter 55: Visualizing Model Errors
  56. Chapter 56: Comparing Groups
  57. Chapter 57: Mini Project: Exploratory Data Analysis
  58. Chapter 58: Mini Project: Feature Relationship Report
  59. Chapter 59: Mini Project: Classification Data Review
  60. Chapter 60: Capstone: AI Statistical Visualization Dashboard

Course features

  • Responsive top logo and main menu.
  • Chapter menu below the main header, with all 60 links on mobile and desktop.
  • Chapter search, font increase/decrease, night view, print, and go-to-top.
  • Google Translate area with RTL-safe behavior and no intentional horizontal page movement.
  • Original lessons, SEO metadata, structured data, course map, sitemap, robots file, and legal notice.