Data Visualization Libraries in Python
( 30 Modules )

Module #1
Introduction to Data Visualization
Overview of data visualization, importance, and popular libraries in Python

Module #2
Setting up the Environment
Installing required libraries, setting up Jupyter Notebook, and basic Python concepts

Module #3
Matplotlib Basics
Introduction to Matplotlib, basic plots, and customizing plots

Module #4
Matplotlib Advanced
Customizing plots, working with multiple plots, and 3D plots

Module #5
Seaborn Basics
Introduction to Seaborn, visualizing statistical relationships, and heatmaps

Module #6
Seaborn Advanced
Customizing Seaborn plots, FacetGrid, and PairGrid

Module #7
Plotly Basics
Introduction to Plotly, interactive plots, and basic chart types

Module #8
Plotly Advanced
Customizing Plotly plots, 3D plots, and animations

Module #9
Bokeh Basics
Introduction to Bokeh, interactive plots, and basic chart types

Module #10
Bokeh Advanced
Customizing Bokeh plots, linking plots, and data tables

Module #11
Altair Basics
Introduction to Altair, interactive plots, and basic chart types

Module #12
Altair Advanced
Customizing Altair plots, layering, and data transformations

Module #13
Data Preparation for Visualization
Handling missing data, data cleaning, and data transformation

Module #14
Working with Different Data Types
Visualizing categorical, numerical, and datetime data

Module #15
Visualization Best Practices
Effective visualization, color theory, and visualization ethics

Module #16
Real-World Applications
Case studies, examples, and projects using data visualization libraries

Module #17
Advanced Topics in Data Visualization
Geospatial visualization, network visualization, and interactive dashboards

Module #18
Deploying Visualizations
Deploying visualizations to the web, using Dash, and Flask

Module #19
Working with Big Data
Visualizing large datasets, using Dask, and distributed computing

Module #20
Collaboration and Version Control
Using Git, GitHub, and collaboration tools for data visualization projects

Module #21
Data Visualization in Machine Learning
Visualizing machine learning models, metrics, and results

Module #22
Data Storytelling
Crafting compelling narratives with data visualization

Module #23
Advanced Visualization Tools
Using other visualization libraries, such as Pygal, Folium, and Missingno

Module #24
Visualization for Specific Domains
Visualizing data in finance, healthcare, and social sciences

Module #25
Creating Interactive Dashboards
Building interactive dashboards using Dash, Flask, and React

Module #26
Advanced Data Visualization Techniques
Using clustering, dimensionality reduction, and other advanced techniques

Module #27
Visualization for Streaming Data
Visualizing real-time data, using Apache Kafka, and streaming analytics

Module #28
Data Visualization in Python for Non-Programmers
Using data visualization libraries without extensive programming knowledge

Module #29
Best Practices for Data Visualization Projects
Designing effective data visualization projects, from concept to delivery

Module #30
Course Wrap-Up & Conclusion
Planning next steps in Data Visualization Libraries in Python career

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Data Visualization Libraries in Python ( 30 Modules )

Data Visualization Libraries in Python
( 30 Modules )