Activity 6: Second Visualization Design
Due: In-class on Thursday, Sept. 15, 2016 at 11:00am
Team: This is a solo assignment; you should type all the code to your solution. However, feel free to get help from others/Google/etc.
Grading: This assignment is the mid-week activity for Experience 3,
worth 10 points. There is no partial credit.
No Late Submissions: We will be using the
solution to this activity in lecture on Thursday; no late submissions
will be accepted.
Assignment
This activity has two parts:
-
Compute: Complete a Python script to find the percentage of
students who received an A in each course during Fall 2014.
-
Visualize: Create a hand-drawn visualization of a visualization
using this week's dataset.
Part 1: Compute
A workbook branch has been set up for this activity. To get the branch,
run the following commands in your workbook directory:
git fetch release
git merge release/exp_activity6 master -m "merge"
In py/compute.py, complete the Python script to compute a list
of dictionaries. Each entry dictionary must contain the following:
-
Course Subject, the subject of the course (ex: CS)
-
Course Number, the number of the course (ex: 205)
-
Percentage A, the percentage of students who received an A in the course (ex: 0.725)
As an example, ECON 302 should have the following entry:
{
"Course Subject": "ECON",
"Course Number": "203",
"Percentage A": 0.153979239
}
Your "Percentage A" may be slightly different. I choose to
ignore students with "W"s and only count "A+"s and
"A" in my percentage (not "A-"s). You can make
different choices here and you will get a similar but slightly different value.
Part 2: Visualize
Create a hand-drawn visualization, similar to what you did in
Activity 4.
-
You can use data found in the dataset or assume that the data has been
processed (eg: averages across multiple years, etc)
-
You should use different visual encodings as appropriate; you will need
to identify what you used on the back of your visualization
On the back of your visualization, you must include:
- Your name
-
A list of all visual encodings used in your visualization, including
the data source and the method of encoding the data (eg: hue, x-axis, etc;
similar to Activity 3 and Activity 4)
Submission
Your Python for this activity is submitted digitally via git.
View detailed submission instructions here.
You will turn the visualization in at the start of lecture on Thursday.
We may spend class time discussing the visualizations created, so be prepared
to talk about your work to the class.