Statistical Computing and Computation - Final Project

Completed final project is due by 5:00 pm on Saturday, April 30th.

Guidelines

1. You may form a team of between 1 - 6 students to complete the project.

2. The primary goal of the project is to do something interesting using something related to the course. This is purposefully open ended to give you the opportunity to work on something you are interested in and to produce work that you could potentially show to a prospective employer or use as part of your graduation portfolio.

   Some examples of possible topics,

   • Implementing an interface to a Web API (e.g. Spotify, GitHub, etc.)
   • Collecting / assembling a data set via webscraping or similar approach
   • Analyzing a data set
   • Implementing a specific algorithm from a paper

3. The primary way in which you will be evaluate will be based on the evidence of effort in your project and the quality of the write up. What this means is that a "failed" project (e.g. an analysis without a significant result) that is well documented will receive a better grade than a less ambitious "successful" project with a worse write up.

   The expectation is that most projects should be roughly 5-10 pages - including text, code, figures, and other output. This is meant only as a very rough guide to calibrate your work (there are no penalties for the report being too short or too long).

4. We have included a very bare template, final_project.ipynb which only includes basic section headings. Feel free to add, adjust, remove these as needed to fit your specific project, they are included only as general guidelines for structuring your write up.

5. You may include all of your code in the IPython Notebook or in one or more python script files - make sure your write up makes it clear how the code is meant to be run and how your results can reproduced. Additional data files and other supplementary materials are also fine as long as they are neatly organized and explicitly discussed in the write up.

6. As with all of the other homework assignments in the class, everyone is expected to contribute equally to the final product - there will be a peer evaluation distributed after the assignment is completed to assess this. Failure to sufficiently contribute may result in a penalty to your individual grade.