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10 Modules / ~100 pages
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~25 Modules / ~400 pages
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Data Visualization with Python Basics
( 25 Modules )

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


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