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Seaborn – Syllabus For Data Visualization

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Seaborn – Learning Syllabus

Table Of Contents:

  1. Basics of Seaborn & Setup
  2. Basic Plot Types
  3. Advanced Statistical Plots
  4. Pairwise Relationships & Correlation Plots
  5. Categorical Data Visualization
  6. Regression & Trend Analysis
  7. Customizing Seaborn Plots
  8. Working with Large Datasets & Complex Visualizations

(1) Basics of Seaborn & Setup

  1. What is Seaborn? Why use it over Matplotlib?

  2. Installing Seaborn (pip install seaborn)

  3. Importing Seaborn & Matplotlib (import seaborn as sns)

  4. Understanding Seaborn’s built-in datasets (sns.get_dataset_names())

  5. Loading datasets (sns.load_dataset("tips"))

(2) Basic Plot Types

  1. Line Plot (sns.lineplot())

  2. Scatter Plot (sns.scatterplot())

  3. Bar Plot (sns.barplot())

  4. Count Plot (sns.countplot())

  5. Histogram (sns.histplot())

  6. KDE Plot (sns.kdeplot())

(3) Advanced Statistical Plots

  1. Box Plot (sns.boxplot()) → Detect outliers

  2. Violin Plot (sns.violinplot()) → Show distribution + density

  3. Strip Plot (sns.stripplot()) → Individual points on a boxplot

  4. Swarm Plot (sns.swarmplot()) → Avoid overlapping points

(4) Pairwise Relationships & Correlation Plots

  1. Pair Plot (sns.pairplot()) → Relationship between all numerical features

  2. Joint Plot (sns.jointplot()) → Combination of scatter + histograms

  3. Heatmap (sns.heatmap()) → Show correlations

(5) Categorical Data Visualization

  1. Count Plot (sns.countplot())

  2. Box Plot (sns.boxplot())

  3. Violin Plot (sns.violinplot())

  4. Bar Plot (sns.barplot())

(6) Regression & Trend Analysis

  1. Linear Regression Plot (sns.regplot())

  2. Logistic Regression Visualization (sns.lmplot())

  3. Polynomial Regression Trend

(7) Customizing Seaborn Plots

  1. Changing colors & palettes (sns.color_palette())

  2. Modifying style & themes (sns.set_theme())

  3. Adjusting figure size (plt.figure(figsize=(10, 6)))

  4. Saving Plots (plt.savefig())

(8) Working with Large Datasets & Complex Visualizations

  1. Working with big datasets (data.sample(n=10000))

  2. Combining multiple plots (sns.FacetGrid())

  3. Interactive Seaborn with Plotly

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