HONR 393-14: Capstone in Data Analytics, Fall 2025

Welcome! 👋

\(-\) Explore, Learn, and Grow with Data Analytics! 🌟

🗓️ Tentative Course Schedule

Biweekly meetings on Fridays, 1:00 P.M.-3:00 P.M.
- In Week 13, the meeting is scheduled on November 19, Wednesday 12:00 P.M.-2:00 P.M., not on Friday.

Week	Dates	Topic	Notes
1	August 29	Meeting 0 (Course Outline; Research Ideas)
2	Sep 2–5	Prepare Meeting 1 (Research Ideas; Data Collection)	Labor Day (Sep 1)
3	September 12	Meeting 1 (Research Methods, Data Collection; Research Ideas)
4	Sep 15–19	Prepare Meeting 2 (Data Preparation; Prepare the Research Kickoff Report)
5	September 26	Meeting 2 (Research Methods)	Submission of Research Kickoff Report
6	Sep 29–Oct 3	Prepare Meeting 3 (Data Collection; Data Exploration)
7	October 10	Meeting 3 (Research Methods)
8	Oct 15–17	Prepare Meeting 4 (Data Preparation; Prepare the Progress & Insights Report)	Fall Break (Oct 13–14)
9	October 24	Meeting 4 (Research Methods)	Submission of the Progress & Insights Report
10	Oct 27–31	Prepare Meeting 5 (Data Modeling)
11	November 7	Meeting 5 (Research Methods)
12	Nov 10–14	Prepare Meeting 6 (Prepare the Research Synthesis Report)
13	November 19	Meeting 6 (Research Methods)	Submission of Research Synthesis Report
14	Nov 24–25	Prepare Meeting 7 (Prepare the Final Proposal)	Thanksgiving Break (Nov 26–28)
15	December 5	Meeting 7 (Feedback on Final Proposal)
16	Dec 8	Prepare the Final Proposal	Submission of Final Proposal

Each report is meant to serve as a structured progress documentation that balances technical work and personal learning. You should:

Summarize progress
- What was done since the last meeting? (e.g., data collection, preparation, exploration, modeling)
- What challenges were encountered?
Evaluate methods
- Are chosen tools/approaches working well?
- What adjustments are needed?
Document insights
- What new findings emerged from the data?
- What was learned about the research question so far?
Plan ahead
- What are the next concrete steps before the next meeting?
- Identify support/resources needed.
Personal takeaways
- Briefly reflect on skills gained (coding, data wrangling, communication).
- How do these skills connect to the overall capstone or professional goals?

Research Kickoff Report
- Research ideas, data sources, and first challenges
Progress & Insights Report
- What’s been done and early analyses
Research Synthesis Report
- Takeaways, limitations, lessons learned, and next steps for the final proposal

Title & Abstract
- Concise project title.
- 150–250 word abstract summarizing research question, data, methods, and expected contribution.
Introduction & Motivation
- Background on the problem/topic.
- Why it matters (business, economics, or policy relevance).
- Clear statement of research question(s).
Literature & Context (short)
- What have others done in this area?
- How does this project extend or differ?
Data
- Description of the dataset(s): source, size, variables, time period.
- How data were collected (APIs, scraping, public repositories, etc.).
- Any limitations (missing data, measurement error).
Methods / Analytical Approach
- What techniques will be used (e.g., regression, clustering, time-series)?
- Why are these methods appropriate?
- Any expected preprocessing steps (cleaning, transformations).
Preliminary Results / Exploration
- Early exploratory data analysis (EDA).
- Tables, summary statistics, or sample visualizations.
- Evidence the student/team has actually interacted with the dataset.
Timeline & Work Plan
- Breakdown of remaining work (analysis, visualization, writing).
- Roles and responsibilities if a team project.
Expected Contributions
- What new insights do you expect to generate?
- Who benefits from this research (scholars, firms, policymakers, the public)?
References
- Cited literature, data sources, and tools (APA or other standard).
Appendices (Optional)
- Extra figures, code snippets, or data dictionaries.