Data Visualization with Python
With so much data being continuously generated, developers with a knowledge of data analytics and data visualization are always in demand. With Data Visualization with Python, you'll learn how to use Python with NumPy, Pandas, Matplotlib, and Seaborn to create impactful data visualizations with real world, public data. Data Visualization with Python takes a hands-on approach to the practical aspects of using Python to create effective data visuals. It contains multiple activities that use reallife business scenarios for you to practice and apply your new skills in a highly relevant context.
Duration: 3 Days
Data Visualization with Python is designed for developers and scientists, who want to get into data science or want to use data visualizations to enrich their personal and professional projects. You do not need any prior experience in data analytics and visualization; however, it'll help you to have some knowledge of Python and familiarity with high school level mathematics. Even though this is a beginner level course on data visualization, experienced developers will be able to improve their Python skills by working with real-world data.
This course will provide you with knowledge of the following:
• Understand and use various plot types with Python
• Explore and work with different plotting libraries
• Understand and create effective visualizations
• Improve your Python data wrangling skills
• Work with industry-standard tools like Matplotlib, Seaborn, and Bokeh
• Understand different data formats and representations
Data Visualization with Python is designed for developers and scientists, who want to get into data science or want to use data visualizations to enrich their personal and professional projects. You do not need any prior experience in data analytics and visualization; however, it'll help you to have some knowledge of Python and familiarity with high school level mathematics. Even though this is a beginner level course on data visualization, experienced developers will be able to improve their Python skills by working with real-world data.
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 image
• 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: Color palettes
• Activity 1: Heatmaps with custom color 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