This course introduces common data visualization techniques and design principles using python with the seaborn, bokeh, plotly, and networkx packages. Students design and implement interactive, multivariate, text, hierarchical, network, and temporal data visualizations. Restriction: Restricted to Graduate level; Restricted to Analytics Majors

Assignment 2 - Introduction to Data Visualization with Seaborn - (50 points)



Due date: 2/4/2018 at 11:59pm

Objectives



  • Familiarize yourself with Seaborn
  • Implement basic visualization techniques

Introduction to Seaborn



  • For this assignment, you will pick a dataset from the CORGIS Datasets Project. Make sure to try a few datasets before you finalize one.
  • Explore the data in Tableau software to learn about the data.
  • Using Seaborn, write a Python program to visualize aspects of the data using the following techniques:
    1. Bar chart
    2. Histogram or Boxplot
    3. Scatter plot - bivariate
    4. Scatter plot - trivariate (use color to represent another attribute). Make sure to display a legend
    5. Swarm plot
    6. Faceted Histogram

Extra credit



Extra credit will be available for the use of violin plots (2 points) and other visualization techniques (1 point per technique).

Submitting the assignment



  • For this assignment, create a folder that contains your report, your python code, and screenshots for each visualization technique.
  • Your report must contain your name, title of the assignment - "Introduction to Data Visualization with Seaborn" and the name of the data file used for the assignment along with output images/screenshots for each of the required visualization techniques and a caption for each of them informing the user what s/he should notice in that image.
  • Submit a zip file that contains all the contents of the folder - report, python code, and screenshots on Canvas.