Module #1 Introduction to Data Visualization Overview of data visualization, importance, and Python libraries used for data visualization
Module #2 Setting up Python for Data Visualization Installing Python, necessary libraries, and IDE setup for data visualization
Module #3 Python Basics for Data Visualization Review of basic Python concepts:variables, data types, loops, conditional statements
Module #4 Importing and Cleaning Data Using Pandas to import and clean datasets for visualization
Module #5 Introduction to Matplotlib Basic plotting with Matplotlib:line plots, scatter plots, bar charts
Module #6 Customizing Matplotlib Plots Customizing plots:colors, labels, titles, and adding additional features
Module #7 Introduction to Seaborn Visualizing statistical relationships with Seaborn:heatmaps, scatterplots
Module #8 Seaborn Visualizations:Distributions and Categorical Data Visualizing distributions and categorical data with Seaborn
Module #9 Introduction to Plotly Interactive visualizations with Plotly:line plots, scatter plots, bar charts
Module #10 Plotly Interactive Visualizations Creating interactive visualizations with Plotly:hover-over text, zooming, and more
Module #11 Data Visualization Best Practices Design principles for effective data visualization:color, typography, and more
Module #12 Visualizing Geospatial Data Using Folium and Plotly to visualize geospatial data:maps and location-based data
Module #13 Visualizing Time Series Data Using Pandas and Matplotlib to visualize time series data:line plots, area charts
Module #14 Visualizing Categorical Data Using Seaborn and Matplotlib to visualize categorical data:bar charts, heatmaps
Module #15 Visualizing Correlations and Relationships Using Seaborn and Matplotlib to visualize correlations and relationships:scatter plots, correlation matrices
Module #16 Creating Interactive Dashboards Using Dash and Plotly to create interactive dashboards
Module #17 Deploying Data Visualizations Deploying data visualizations to the web:using GitHub Pages, Heroku, and more
Module #18 Case Study:Exploring a Dataset Applying data visualization skills to a real-world dataset:exploratory data analysis
Module #19 Case Study:Visualizing a Story Using data visualization to tell a story:creating a narrative with data
Module #20 Advanced Data Visualization Techniques Advanced techniques:animation, 3D visualization, and more
Module #21 Working with Big Data Scaling data visualization for large datasets:using Dask, Spark, and more
Module #22 Data Visualization in Machine Learning Using data visualization in machine learning:visualizing models, feature importance, and more
Module #23 Best Practices for Data Visualization in Industry Applying data visualization skills in industry:considerations for stakeholder communication
Module #24 Final Project:Creating a Data Visualization Portfolio Creating a data visualization portfolio:applying skills to a real-world project
Module #25 Course Wrap-Up & Conclusion Planning next steps in Data Visualization with Python Basics career
