DATA 510: Data Science Capstone
May 11, 2026
By the end of this session, you will be able to:
Over the semester you propose, plan, and execute a real data science project that integrates core MS program skills and produces a portfolio-quality artifact aligned with your career goals.
Projects should be consequential: plausible impact on an organization or problem domain. Grades reward steady progress, communication quality, and how you present and defend work (including feedback from industry judges where applicable).
Classes meet Monday, 6:00 PM to 10:00 PM, Ford 102.
Each evening typically splits into two blocks (6:00 to 7:30 PM and 8:00 to 10:00 PM). Expect a blend of seminar discussion, short lectures, group work, mentoring, and guests (the mix may shift with guest schedules).
You will:
Prerequisites: DATA 501, DATA 502, DATA 503, DATA 504.
Minimum grade: A grade of C- or better is required for this course to count toward university credit.
Textbook: None. Readings and links will appear in Canvas.
You will practice how to:
Canvas combines weighted components below. If anything here disagrees with a dated Canvas announcement or assignment page, follow Canvas.
| Component | Weight |
|---|---|
| Professional development activities | 10% |
| Weekly peer and meta-project evaluations | 5% |
| Project process and practices (includes Git workflow) | 5% |
| Technical writing practice | 5% |
| Project proposal | 9% |
| Data summary | 9% |
| Poster rough draft | 8% |
| Written report rough draft | 9% |
| Final poster presentation | 18% |
| Final write-up | 14% |
| Project website and dissemination | 8% |
| Total | 100% |
| Average | Letter |
|---|---|
| 92.00+ | A |
| 90.00 to 91.99 | A- |
| 88.00 to 89.99 | B+ |
| 82.00 to 87.99 | B |
| 80.00 to 81.99 | B- |
| 78.00 to 79.99 | C+ |
| Average | Letter |
|---|---|
| 72.00 to 77.99 | C |
| 70.00 to 71.99 | C- |
| 68.00 to 69.99 | D+ |
| 62.00 to 67.99 | D |
| 60.00 to 61.99 | D- |
| 59.99 and below | F |
Professional development (10%). Every other week (or so) opens with structured career preparation (even if you are not job searching immediately).
Weekly peer and meta-project evaluations (5%). Short structured reflections on your team and the two peer projects in your assigned cluster (quality of feedback you gave, what you learned, risks you see).
Project process and practices (5%). Includes Git workflow, chartering, weekly summaries, and related habits. Git commit quality is reviewed weekly; grading for improvement incorporates feedback especially in the last five weeks of the term.
Technical writing practice (5%). In-class exercises with discussion. Partial credit only for makeup work if you miss the class session.
Project proposal (9%). Short plan covering the research question, data sources, planned analysis, ethics notes, and feasibility. It is the default roadmap when scope needs to tighten.
Data summary (9%). Evidence that needed data are gathered and organized or pipelines run without ongoing manual babysitting. After this milestone, no further active ingestion work should be required.
Poster rough draft (8%). First end-to-end story on the wall; placeholders allowed, but the narrative arc should be complete enough to critique.
Written report rough draft (9%). First full draft of publishable prose across required sections. After this milestone, no further methodology or findings changes (editing and polish continue).
Final poster presentation (18%). Conference-style poster session for peers, faculty, and industry guests; joint across cohorts on the last class evening.
Final write-up (14%). Polished, proofread permanent record; published and linked from portfolios.
Project website and dissemination (8%). Public-facing project materials (for example GitHub Pages) with clear links suitable for employers.
You may work solo or in a self-selected team of two or three.
Regardless of team size, every project sits in a meta-project cluster: your capstone is grouped with two other projects for the whole semester. You follow those peers closely and give structured weekly feedback (and receive it).
Teams of two or three should carry noticeably higher scope, risk, or surface area than a comparable solo project (integration depth, evaluation rigor, division of labor, documentation).
Instructor approval of team scope happens at the project proposal. Proposals that look like solo scope with extra names should expect revision requests.
DDS is the course operating model for transparent, prioritized iteration tailored to data science.
It emphasizes:
Core artifacts include items, backlog, item breakdown board (IBB), task board, and the weekly progress report (often README updates plus snapshots when required).
README or the filename specified in Canvas should routinely cover:
Standups and backlog refinement are in-class work, expected finished by the end of the evening session, including cross-team touchpoints inside your meta-project cluster.
Written follow-ups post by Canvas deadlines.
Each week you:
Strong comments cite visible evidence, separate curiosity from blocking risks, and surface ethics and engineering concerns early (PII, leakage, reproducibility).
Each deliverable has a 24-hour grace window after the stated deadline with no penalty.
Beyond that you lose 20% of that deliverable’s points per day late.
Exceptions (no grace window): Final presentations (single shared slot), writing assignments tied to technical writing, and Git-related assignments as labeled in Canvas.
Participation matters. You are responsible for making up missed material.
More than three unexcused absences may result in a failing grade for the course.
Incompletes are only considered for prolonged illness or family emergencies that remove you from campus for an extended period late in the term.
They are not granted for falling behind due to motivation, misunderstanding, or time management alone. If you are worried, come talk early.
Email a written petition before the last class period. Explain why a regrade is warranted with references to course standards. Decisions after review are final.
Cheating and plagiarism undermine capstone credibility; penalties range from assignment failure to course failure.
Constructive behaviors build trust; disruptive behaviors (including unauthorized recording) interfere with learning and may be addressed under university policies.
Expect roughly 8 to 12 hours of out-of-class work most weeks for a four-hour weekly meeting (more when deadlines cluster).
Contact Accessible Education Services if you need accommodations; talk to the instructor early if course design creates barriers.
| Week | Date | Block 1 (6:00 to 7:30 PM) | Block 2 (8:00 to 10:00 PM) | Deliverables due end of class unless noted |
|---|---|---|---|---|
| 1 | 5/11 | Syllabus workshop; Research vs. EDA | Project brainstorming | Project brainstorming survey |
| 2 | 5/18 | Technical writing I | Chartering explained; chartering working session | Technical writing output; project charter |
| 3 | 5/25 | Git for project work | Project working session | Weekly summary report |
| 4 | 6/1 | PD: alum panel | Project working session | Weekly summary; project proposal |
| 5 | 6/8 | Technical writing with Quarto | Data engineering consultations; working session | Technical writing output; weekly summary |
| 6 | 6/15 | PD: companies and networking | Project working session | Weekly summary |
| 7 | 6/22 | Statistics consultations | Project working session | Data summary; weekly summary |
| Week | Date | Block 1 (6:00 to 7:30 PM) | Block 2 (8:00 to 10:00 PM) | Deliverables due end of class unless noted |
|---|---|---|---|---|
| 8 | 6/29 | PD: behavioral interview prep | Project working session | Weekly summary |
| 9 | 7/6 | Machine learning consultations | Project working session | Weekly summary |
| 10 | 7/13 | PD: mock interviews I | Project working session | Weekly summary; poster rough draft |
| 11 | 7/20 | PD: internal mock interviews (Portland) | PD continued | Weekly summary |
| 12 | 7/27 | Peer feedback working session | Project working session | Weekly summary; write-up rough draft |
| 13 | 8/3 | Presentation prep | Peer practice; working session | Weekly summary |
| 14 | 8/10 | Poster presentations (Salem) | Poster presentations (Salem) | Final write-up due 8/17 |