What will you learn in Data Visualization and Analysis With Seaborn Library Course
Understand the principles of statistical data visualization and when to use various plot types.
Create informative plots using Seaborn’s high-level API, including distribution, relational, and categorical charts.
Customize aesthetics, themes, and color palettes for publication-quality visuals.
Combine Seaborn with Pandas to explore and summarize datasets.
Integrate Seaborn with Matplotlib to fine-tune plots and annotations.
Program Overview
Module 1: Introduction to Seaborn & Setup
⏳ 1 hour
Topics: Seaborn installation, design philosophy, comparison with Matplotlib.
Hands-on: Install libraries, load sample datasets (e.g., “tips”), and render your first plots.
Module 2: Distribution Plots
⏳ 1.5 hours
Topics: Histograms, KDE plots, rug plots, and joint distributions.
Hands-on: Visualize univariate and bivariate distributions; overlay KDE and histogram.
Module 3: Relational Plots
⏳ 1.5 hours
Topics: Scatter plots, line plots,
relplot, andlineplotfeatures.Hands-on: Plot trends over time and relationships between variables with hue and style.
Module 4: Categorical Plots
⏳ 2 hours
Topics: Bar, count, box, violin, strip, and swarm plots.
Hands-on: Compare categories; customize order, orientation, and grouping.
Module 5: Matrix Plots & Heatmaps
⏳ 1 hour
Topics: Heatmaps, cluster maps, correlation matrix visualization.
Hands-on: Compute correlations and display annotated heatmaps with custom palettes.
Module 6: Styling & Customization
⏳ 1 hour
Topics: Themes (
darkgrid,whitegrid), context (talk,poster), palettes, and Matplotlib integration.Hands-on: Apply and switch themes; adjust figure size, labels, titles, and legend placement.
Module 7: Exploratory Data Analysis Workflow
⏳ 1.5 hours
Topics: Combining multiple plot types, facet grids, and pair plots for quick EDA.
Hands-on: Conduct a mini-EDA project on a real dataset, summarizing findings through visuals.
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Job Outlook
Data visualization specialists and analysts are in high demand across tech, finance, healthcare, and consulting.
Proficiency in Seaborn (and Matplotlib) leads to roles such as Data Analyst, BI Developer, and Visualization Engineer.
Typical salaries range from $70K–$100K USD, rising with experience and domain expertise.
Strong visualization skills enhance careers in data science, product analytics, and reporting functions.
Specification: Data Visualization and Analysis With Seaborn Library
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