ISYE 6414

Students will be invited to given a login for this site after Final Project groups are finalized onJanuary 27.

Checkpoints, the Final Report and Peer Reviews will be submitted via this account.

Your individual code will be submitted via your group's GitHub repository, which we'll invite you to after your account is created.

Final Project Introduction

Overview

Objective

Perform a professional analysis using everything you've learned in ISYE 6414 and write it up in a professional, academic manner resembling what one would see in a peer-reviewed paper.

Each team member must find and use at least 3 datasets from 3 different data sources, combine them in an interesting way, and test questions or assumptions about the information they contain for their individual analysis. See the data sources requirement for details.

Team Project Requirements

This is a team project — you cannot work solo. You can (and should!) form a team of 5, but if you don't, we'll eventually assign you to one.

Choose Your Own Topic

Your team must choose your own research topic and problem to investigate. There are no pre-defined project skeletons this semester — you have full freedom (and responsibility) to identify a problem that interests your team and can be addressed with the statistical methods from this course.

Some people will likely drop the course. If groups become much too small, we'll reassign/combine groups to maintain appropriate team sizes.

Smaller Teams, Same Standards

Teams that choose to remain smaller than 4-5 members will be graded at the same level as full-sized teams. The Final Report requirements (page length, number of analyses, citations, etc.) remain the same regardless of team size. We strongly encourage forming full teams.

Everyone is expected to contribute to each element of the project. If you don't contribute adequately, you'll be penalized (up to being given a 0 for that deliverable).

Project Timeline

gantt title ISYE 6414 Final Project Timeline (Spring 2026) dateFormat YYYY-MM-DD axisFormat %b %-d section Progress Checkpoints Team Formation :cp1, 2026-01-12, 14d CP1 - Team Due :milestone, m1, 2026-01-26, 0d Topic Selection :cp2, after m1, 7d CP2 - Topic Due :milestone, m2, 2026-02-02, 0d Data Sources :cp3, after m2, 20d CP3 - Data Sources Due :milestone, m3, 2026-02-22, 0d Literature Review :cp4, after m3, 7d CP4 - Lit Review Due :milestone, m4, 2026-03-01, 0d Methods Proposal :cp5, after m4, 21d CP5 - Methods Due :milestone, m5, 2026-03-22, 0d Spring Break :crit, sb, 2026-03-23, 5d Progress Report :cp6, 2026-03-28, 8d CP6 - Progress Due :milestone, m6, 2026-04-05, 0d section Peer Reviews Round 1 Reviews :pr1, 2026-02-15, 7d Round 1 Due :milestone, pr1m, 2026-02-22, 0d Round 2 Reviews :pr2, 2026-03-22, 7d Round 2 Due :milestone, pr2m, 2026-03-29, 0d Round 3 Reviews :pr3, 2026-04-21, 7d Round 3 Due :milestone, pr3m, 2026-04-28, 0d section Final Deliverables Code Development :c1, 2026-02-22, 58d Code Due :milestone, cm, 2026-04-21, 0d Final Report Writing :f1, 2026-02-22, 65d Final Report Due :milestone, fm, 2026-04-28, 0d

Grading

The Final Project is worth 35%% of your total grade for ISYE 6414.

Grade Breakdown

Deliverable	Due	Weight
Peer Ratings (Round 1)	Feb 22, 11:59 PM US Eastern Time	5%
Peer Ratings (Round 2)	Mar 29, 11:59 PM US Eastern Time	5%
Code	Apr 21, 11:59 PM US Eastern Time	40%
Peer Ratings (Round 3)	Apr 28, 11:59 PM US Eastern Time	5%
Final Report	Apr 28, 11:59 PM US Eastern Time	45%

Progress Checkpoints (Optional)

Throughout the semester, there are checkpoints allowing you to submit your progress and receive TA feedback.Submitting is optional, but it'sthe only way to get coachingbefore your graded deliverables are due.

View Checkpoint Schedule

The Peer Ratings are per student, while the Final Report and Code grades will be assigned to each member of a Group.

Rubrics

Rubrics are available for each graded component:

Final Report Rubric — view the Rubric tab
Peer Ratings Rubric — scroll to the Peer Review Rubric section

Useful Guidelines

Make sure you pattern your Literature Review on those you see in high quality peer-reviewed research papers (you need 20+ citations). For an excellent example, see Burnam et al. (2014).
Have at least 20 peer-reviewed journal article citations.
Each individual analysis must use at least 3 datasets from 3 different data sources, joined together, with at least one dataset having a few hundred rows. See the data sources requirement for details.
You better split at least train & test and report performance metrics. You'd be even wiser if you're going to pick from amongst the various models you'll build as a group by splitting into train, test and validate sets.
We expect you to perform all the professional steps you learn in this class: EDA + outlier screening, GoF testing, variable selection, and attempt to fix data/model when appropriate.
Include graphics when they're useful. Everyone likes pretty pictures: appropriate context and utility is what makes them pretty in data analytical paper.