Module #1 Introduction to Data Visualization Overview of data visualization, importance of charts and graphs, and introduction to Python libraries for data visualization
Module #2 Setting Up the Environment Installing required Python libraries (Matplotlib, Seaborn, Plotly), setting up Jupyter Notebook or IDE
Module #3 Introduction to Matplotlib Basic concepts of Matplotlib, creating simple plots, customizing plot elements
Module #4 Line Charts and Time Series Data Creating line charts, customizing line styles, working with time series data
Module #5 Scatter Plots and Correlation Analysis Creating scatter plots, customizing markers and colors, understanding correlation and regression analysis
Module #6 Bar Charts and Histograms Creating bar charts, customizing bars and colors, creating histograms
Module #7 Pie Charts and Donut Charts Creating pie charts, customizing pie slices and colors, creating donut charts
Module #8 Introduction to Seaborn Basic concepts of Seaborn, creating informative and attractive statistical graphics
Module #9 Visualizing Categorical Data with Seaborn Creating bar plots, count plots, and box plots with Seaborn
Module #10 Visualizing Numerical Data with Seaborn Creating scatter plots, regression plots, and heatmaps with Seaborn
Module #11 Introduction to Plotly Basic concepts of Plotly, creating interactive plots
Module #12 Creating Interactive Line Charts and Scatter Plots with Plotly Creating interactive line charts and scatter plots, customizing interactive elements
Module #13 Creating Interactive Bar Charts and Histograms with Plotly Creating interactive bar charts and histograms, customizing interactive elements
Module #14 Working with 3D Plots and Maps with Plotly Creating 3D plots, working with map data, creating interactive maps
Module #15 Customizing Charts and Graphs Customizing plot elements, using themes and styles, adding annotations and labels
Module #16 Working with Real-World Data Loading and cleaning real-world datasets, creating charts and graphs to visualize insights
Module #17 Best Practices for Data Visualization Design principles, color theory, and best practices for effective data visualization
Module #18 Advanced Topics in Data Visualization Working with big data, creating animations and interactive dashboards, using other Python libraries for data visualization
Module #19 Project:Creating a Data Visualization Dashboard Applying learned concepts to create a comprehensive data visualization dashboard
Module #20 Project:Visualizing a Real-World Dataset Applying learned concepts to visualize insights from a real-world dataset
Module #21 Project:Creating an Interactive Data Visualization Applying learned concepts to create an interactive data visualization using Plotly or other libraries
Module #22 Final Project:Creating a Comprehensive Data Visualization Report Applying learned concepts to create a comprehensive data visualization report
Module #23 Conclusion and Next Steps Summary of key takeaways, resources for further learning, and next steps for continued development
Module #24 Appendix:Troubleshooting Common Issues Troubleshooting common issues and errors in data visualization with Python
Module #25 Appendix:Advanced Data Visualization Techniques Advanced data visualization techniques, including network visualization and geospatial visualization
Module #26 Appendix:Data Visualization in Other Python Libraries Overview of other Python libraries for data visualization, including Bokeh and Altair
Module #27 Appendix:Data Visualization for Machine Learning Using data visualization for machine learning, including visualizing model performance and hyperparameter tuning
Module #28 Appendix:Data Visualization for Data Science Using data visualization for data science, including data exploration and insight generation
Module #29 Appendix:Data Visualization Best Practices for Communication Best practices for communicating insights and results using data visualization
Module #30 Course Wrap-Up & Conclusion Planning next steps in Creating Charts and Graphs with Python career
