Data Visualization with Python Training

Data Visualization with Python Training Outline

• Lesson 1: Importance of Data Visualization and Data Exploration

  • Topic 1: Introduction to data visualization and its importance
  • Topic 2: Overview of statistics
    • Activity 1: Compute mean, median, and variance for the following numbers and explain the difference between mean and median
  • Topic 3: A quick way to get a good feeling for your data
  • Topic 4: NumPy
    • Activity 1: Use NumPy to solve the previous activity
    • Activity 2: Indexing, slicing, and iterating
    • Activity 3: Filtering, sorting, and grouping
  • Topic 5: Pandas
    • Activity 1: Repeat the NumPy activities using pandas, what are the advantages and disadvantages of pandas?

• Lesson 2: All You Need to Know About Plots

  • Topic 1: Choosing the best visualization
  • Topic 2: Comparison plots
    • Line chart
    • Bar chart
    • Radar chart
    • Activity 1: Discussion round about comparison plots
  • Topic 3: Relation plots
    • Scatter plot
    • Bubble plot
    • Heatmap
    • Correlogram
    • Activity 1: Discussion round about relation plots
  • Topic 4: Composition plots
    • Pie chart
    • Stacked bar chart
    • Stacked area chart
    • Venn diagram
    • Activity 1: Discussion round about composition plots
  • Topic 5: Distribution plots
    • Histogram
    • Density plot
    • Box plot
    • Violin plot
    • Activity 1: Discussion round about distribution plots
  • Topic 6: Geo plots
  • Topic 7: What makes a good plot?
    • Activity 1: Given a small dataset and a plot, reason about the choice of visualization and presentation and how to improve it

• Lesson 3: Introduction to NumPy, Pandas, and Matplotlib

  • Topic 1: Overview and differences of libraries
  • Topic 2: Matplotlib
  • Topic 3: Seaborn
  • Topic 4: Geo plots with geoplotlib
  • Topic 5: Interactive plots with bokeh

• Lesson 4: Deep Dive into Data Wrangling with Python

  • Topic 1: Matplotlib
  • Topic 2: Pyplot basics
  • Topic 3: Basic plots
    • Activity 1: Comparison plots: Line, bar, and radar chart
    • Activity 2: Distribution plots: Histogram, density, and box plot
    • Activity 3: Relation plots: Scatter and bubble plot
    • Activity 4: Composition plots: Pie chart, stacked bar chart, stacked area chart, and Venn diagram
  • Topic 4: Legends
    • Activity 1: Adding a legend to your plot
  • Topic 5: Layouts
    • Activity 1: Displaying multiple plots in one figure
  • Topic 6: Images
    • Activity 1: Displaying a single and multiple images
  • Topic 7: Writing mathematical expressions

• Lesson 5: Simplification through Seaborn

  • Topic 1: From Matplotlib to Seaborn
  • Topic 2: Controlling figure aesthetics
    • Activity 1: Line plots with custom aesthetics
    • Activity 2: Violin plots
  • Topic 3: Colour palettes
    • Activity 1: Heatmaps with custom colour palettes
  • Topic 4: Multi-plot grids
    • Activity 1: Scatter multi-plot
    • Activity 2: Correlogram

• Lesson 6: Plotting Geospatial Data

  • Topic 1: Geoplotlib basics
    • Activity: Plotting geospatial data on a map
    • Activity: Choropleth plot
  • Topic 2: Tiles providers
  • Topic 3: Custom layers
    • Activity: Working with custom layers

• Lesson 7: Making Things Interactive with Bokeh

  • Topic 1: Bokeh basics
  • Topic 2: Adding Widgets
    • Activity 1: Extending plots with widgets
  • Topic 3: Animated Plots
    • Activity 1: Animating information

• Lesson 8: Combining What We've Learned

  • Topic 1: Recap
  • Topic 2: Free exercise
    • Activity 1: Given a new dataset, the students have to decide in small groups which data they want to visualize and which plot is best for the task.
    • Activity 2: Each group gives a quick presentation about their visualizations.

• Lesson 9: Application in Real Life and Conclusion of Course

  • Applying Your Knowledge to a Real-life Data Wrangling Task
  • An Extension to Data Wrangling