Final Project Rubric


for Python for Data Science Course


Points Necessary by Team Size

For an individual worker (team of 1), a 100% grade requires 25 earned points. For a team of two, a 100% grade requires 35 points.

Team Size Pts for 100% … for 90% … for 80%
1 25 22.5 20
2 35 31.5 28


Earning Points

Data Acquisition
Points Max Task
2 2 Acquire a dataset from the internet
1 3 For each dataset acquired and used beyond the first
2 2 Create a dataset yourself to work with other data you found online; e.g. a mapping of state abbreviations to state names.
Data Wrangling
Points Max Task
4 4 Use at least one join
3 3 Melt and/or reshape your data
Modeling
Points Max Task
2 6 For each linear model trained and used in your project
3 9 For each nonlinear model trained and used in your project
0.5 3 For each statistical metric (mean, median, mode) included in your final deliverable(s)
1 6 For each plot included in your final deliverable(s)
Meta
Points Max Task
6 6 Version control your code using a public GitHub repository (easy points – this makes it infinitely easier for me to grade). If you have two team members, both people must have a commit recorded in the Git history to get these points.
0.5 3 For each additional Git commit beyond the first.
Up to 5 5 Points at the instructor’s discretion for particularly impressive projects
Stretch Goals (topics not included in the course lectures)
Points Task
4 Pull data from a web-based API (I recommend the Requests library)
5 Scrape data from a website using Beautiful Soup and/or Scrapy.
5 Create a web dashboard using Dash.
4 Correctly train and make predictions using a neural network (I recommend Keras).