See how Insoft Services is responding to COVID-19

# Data Visualization with Python

X

We would love to hear from you. Please complete this form to pre-book or request further information about our delivery options.

## Subscribe

I'd like to receive emails with the latest updates and promotions from Insoft.

## Data Protection & Privacy

I hereby allow Insoft Ltd. to contact me on this topic. Further, I authorise Insoft Ltd. processing, using collecting and storing my personal data for the purpose of these activities. All your data will be protected and secured as outlined in our privacy policy. 1 Days Course Data Science Classroom + Online

## Overview

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.

This Data Visualization with Python course takes a hands-on approach to the practical aspects of using Python to create effective data visuals. It contains multiple activities that use real-life business scenarios for you to practice and apply your new skills in a highly relevant context.

See other courses available

## Outline

Lesson One: 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 Two: All you need to know about plots

• Topic 1: Choosing the best visualization
• Topic 2: Comparison plots
• Line chart
• Bar 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
• 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: Color 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
• 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