Bestseller हिन्दी में

Seaborn Complete Professional Bootcamp

Your complete journey from Seaborn beginner to professional data visualization expert!

4.7

Expert

Certificate of Completion

Complete this course and earn a verified certificate to showcase your achievement.

Verified & ShareableShare on LinkedIn, resume, or portfolio

QR Code VerificationEmployers can instantly verify online

Unique Certificate IDTamper-proof with unique serial number

Industry RecognisedAccepted by 500+ companies across India

This is to certify that

Your Name Here

has successfully completed

Seaborn Complete Professional Bootcamp

6 Modules

Course Curriculum

6 Modules · 23 Chapters · 23 Topics · 151 Sub-topics

Introduction to Seaborn

1 Topics

Getting Started with Seaborn

6 Sub-topics

What is Seaborn and Why Use It

Seaborn vs Matplotlib

Installing Seaborn

Importing Seaborn and Initial Setup

Understanding Seaborn's Philosophy

Overview of Seaborn Plot Types

First Steps with Seaborn

1 Topics

Creating Your First Plots

6 Sub-topics

Loading Built-in Datasets

Creating Your First Scatter Plot

Creating Your First Line Plot

Understanding Figure and Axes

Basic Plot Customization

Saving Your First Plot

Seaborn Plot Interface

1 Topics

Understanding Plot Functions

6 Sub-topics

Understanding Figure-Level vs Axes-Level Functions

Using relplot() - The Relational Plot Function

Using displot() - The Distribution Plot Function

Using catplot() - The Categorical Plot Function

When to Use Each Interface Type

Converting Between Interface Types

Distribution Plots

1 Topics

Visualizing Data Distributions

7 Sub-topics

Histograms with histplot()

Kernel Density Estimation with kdeplot()

Empirical Cumulative Distribution with ecdfplot()

Rug Plots for Data Density

Combining Multiple Distribution Plots

Bivariate Distribution Plots

Customizing Bins and Bandwidth

Categorical Plots Part 1

1 Topics

Basic Categorical Visualizations

5 Sub-topics

Strip Plots with stripplot()

Swarm Plots with swarmplot()

Box Plots with boxplot()

Violin Plots with violinplot()

Understanding Categorical Data Representation

Categorical Plots Part 2

1 Topics

Advanced Categorical Visualizations

5 Sub-topics

Bar Plots with barplot()

Count Plots with countplot()

Point Plots with pointplot()

Adding Error Bars and Confidence Intervals

Comparing Multiple Categories

Relational Plots

1 Topics

Exploring Relationships in Data

7 Sub-topics

Scatter Plots with scatterplot()

Line Plots with lineplot()

Using Hue for Color Encoding

Using Size for Point Scaling

Using Style for Marker Variation

Adding Multiple Semantic Mappings

Customizing Markers and Lines

Regression Plots

1 Topics

Statistical Regression Visualization

7 Sub-topics

Linear Regression with regplot()

Advanced Regression with lmplot()

Adding Confidence Intervals

Polynomial Regression Visualization

LOWESS Smoothing

Residual Plots with residplot()

Logistic Regression Visualization

Matrix Plots

1 Topics

Heatmaps and Matrix Visualizations

7 Sub-topics

Heatmaps with heatmap()

Correlation Matrices Visualization

Annotating Heatmaps

Cluster Maps with clustermap()

Customizing Color Maps

Adding Dendrograms

Pivot Tables with Heatmaps

Multi-Plot Grids Part 1

1 Topics

Creating Multi-Panel Visualizations

6 Sub-topics

Understanding FacetGrid

Creating Grids with FacetGrid()

Mapping Functions to Grids

Using Row and Column Variables

Customizing Grid Layout

Adding Legends to Grids

Multi-Plot Grids Part 2

1 Topics

Advanced Grid Visualizations

7 Sub-topics

PairGrid for Pairwise Relationships

Creating Pair Plots with pairplot()

Customizing Diagonal and Off-Diagonal Plots

JointGrid for Bivariate Analysis

Creating Joint Plots with jointplot()

Adding Marginal Distributions

Combining Different Plot Types in Grids

Color Palettes Part 1

1 Topics

Understanding Color in Visualization

6 Sub-topics

Understanding Color in Data Visualization

Qualitative Color Palettes

Sequential Color Palettes

Diverging Color Palettes

Using color_palette()

Built-in Palette Names

Color Palettes Part 2

1 Topics

Advanced Color Customization

7 Sub-topics

Creating Custom Palettes

Using Matplotlib Colormaps

Light and Dark Palettes

Cubehelix Palettes

Setting Palettes Globally with set_palette()

Color Palette Best Practices

Accessibility in Color Choice

Styling and Themes

1 Topics

Customizing Plot Appearance

7 Sub-topics

Understanding Seaborn Themes

Using set_theme() for Global Styling

Built-in Styles (darkgrid, whitegrid, dark, white, ticks)

Context Settings (paper, notebook, talk, poster)

Font Scaling with set_context()

Customizing RC Parameters

Temporarily Overriding Styles with axes_style()

Axes and Figure Customization

1 Topics

Fine-Tuning Plot Elements

7 Sub-topics

Working with Matplotlib Axes in Seaborn

Setting Titles and Labels

Customizing Tick Labels and Positions

Adding Grids and Spines

Setting Axis Limits and Scales

Rotating Labels for Readability

Creating Subplots with Seaborn

Statistical Estimation in Plots

1 Topics

Statistical Analysis in Visualization

7 Sub-topics

Understanding Estimators in Seaborn

Confidence Intervals and Error Bars

Using Different Statistical Functions

Bootstrapping in Seaborn

Controlling CI with ci Parameter

Seed and n_boot for Reproducibility

Aggregation Functions (mean, median, etc.)

Working with DataFrames

1 Topics

Data Preparation for Seaborn

7 Sub-topics

Long-Form vs Wide-Form Data

Preparing Data for Seaborn

Using the data Parameter

Column-Based Plotting with x and y

Handling Missing Values

Using Index as Plot Variable

Melting and Reshaping Data for Seaborn

Advanced Customization Part 1

1 Topics

Complex Customization Techniques

6 Sub-topics

Combining Seaborn with Matplotlib

Creating Complex Multi-Plot Layouts

Custom Color Functions

Programmatic Plot Generation

Dynamic Plot Styling

Plot Element Positioning

Time Series Visualization

1 Topics

Plotting Temporal Data

7 Sub-topics

Plotting Time-Based Data

Date Formatting on Axes

Time Series Line Plots

Aggregating Time Data

Seasonal Patterns Visualization

Multiple Time Series Comparison

Time-Based Heatmaps

Best Practices and Optimization

1 Topics

Professional Visualization Development

7 Sub-topics

Code Organization for Visualization

Reusable Plotting Functions

Plot Validation and Testing

Performance Optimization Tips

Memory Management

Documentation and Commenting

Version Control for Visualizations

Real-World Applications Part 2

1 Topics

Industry Applications

7 Sub-topics

Financial Data Visualization

Web Analytics Plots

A/B Testing Results

Survey Data Analysis

Geospatial Data (with Limitations)

Quality Control Charts

Report Automation

Saving and Exporting

1 Topics

Plot Export and File Management

7 Sub-topics

Saving Plots with savefig()

File Formats (PNG, SVG, PDF, EPS)

Resolution and DPI Settings

Transparent Backgrounds

Bbox Settings for Cropping

Exporting for Publications

Batch Saving Multiple Plots

Project Portfolio Development

1 Topics

Building Professional Portfolio

7 Sub-topics

Building a Visualization Portfolio

Documenting Visualization Projects

GitHub Portfolio Setup

Creating Visualization Case Studies

Presenting Data Stories

Portfolio Best Practices

Continuous Learning and Updates

0.0

Course Rating

No reviews yet. Be the first to review this course!

Frequently Asked Questions

No FAQs for this course yet.

Preview this course

₹5,999 ₹8,999 33% OFF

Lifetime access to all materials

Certificate of completion

Available in multiple languages

Access on mobile & desktop

7-Day Money-Back Guarantee Not satisfied? Get a full refund within 7 days, no questions asked. Zero risk.

Seaborn Complete Professional Bootcamp

Certificate of Completion

Course Curriculum

Student Reviews

Frequently Asked Questions

Start Your Journey Today

Seaborn Complete Professional Bootcamp

Certificate of Completion

Course Curriculum

Student Reviews

Frequently Asked Questions

Related Courses

Pandas for Absolute Beginners

NumPy Mega Course

Pandas for Data Analysis

Start Your Journey Today