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Seaborn for Data Scientists

Create stunning statistical visualizations with Seaborn expertise!

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Seaborn for Data Scientists

4 Modules

Course Curriculum

4 Modules · 12 Chapters · 12 Topics · 80 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

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

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

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

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

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.)

Integration with Other Libraries

1 Topics

Seaborn in Data Science Workflows

7 Sub-topics

Seaborn with Pandas for Data Analysis

Using Seaborn with NumPy Arrays

Statsmodels Integration

Scipy Statistics Visualization

Plotly for Interactive Alternatives

Integration with Jupyter Notebooks

Seaborn in Production Environments

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

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

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Seaborn for Data Scientists

Certificate of Completion

Course Curriculum

Student Reviews

Frequently Asked Questions

Start Your Journey Today

Seaborn for Data Scientists

Certificate of Completion

Course Curriculum

Student Reviews

Frequently Asked Questions

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