Course 5
Data Visualization with Python
This course is focused on teaching students how to create insightful and effective visualizations using Python. Students will learn how to use popular Python libraries (such as Matplotlib, Seaborn, and Plotly) to explore, analyze, and communicate data in visually compelling ways.
Week 1
Introduction to Data Visualization in Python
- Importance of data visualization in data science.
- Overview of visualization libraries: Matplotlib, Seaborn, Plotly, and others.
- Basics of plotting with Matplotlib: line plots, scatter plots, and bar charts.
Week 2
Working with Seaborn for Advanced Visualization
- Creating advanced plots: pair plots, violin plots, heatmaps, and box plots.
- Customizing visualizations: colors, labels, themes, and styles.
- Plotting categorical and numerical data with Seaborn.
Week 3
Interactive Visualizations with Plotly
- Introduction to Plotly and its interactive features.
- Creating interactive scatter plots, line charts, and 3D plots.
- Dash: creating interactive web applications for data visualization.
Week 4
Data Visualization Best Practices
- Principles of effective data visualization: clarity, simplicity, and focus.
- Choosing the right chart for your data: bar charts, line charts, histograms, and pie charts.
- Avoiding common pitfalls in data visualization (misleading visuals, over-complication).
Week 5
Visualizing Geospatial Data
- Plotting geographic data: maps and choropleths.
- Visualizing data with GeoPandas and Plotly maps.
- Handling geographic data and coordinates.
Week 6
Final Project: Creating a Data Dashboard
- Design and develop an interactive data visualization dashboard.
- Use Python libraries (Matplotlib, Seaborn, Plotly, Dash) to create a compelling dashboard.
- Present the final dashboard with an emphasis on interactive data exploration and insights.