STA 9750 Mini-Project #02: TBD
Due Dates
- Released to Students: 2025-09-30
- Initial Submission: 2025-10-24 11:59pm ET on GitHub and Brightspace
-
Peer Feedback:
- Peer Feedback Assigned: 2025-10-27 on GitHub
- Peer Feedback Due: 2025-11-03 11:59pm ET on GitHub
Estimated Time to Complete: 9 Hours
Estimated Time for Peer Feedback: 1 Hour
Introduction
Student Responsbilities
Recall our basic analytic workflow and table of student responsibilities:
- Data Ingest and Cleaning: Given a single data source, read it into
Rand transform it to a reasonably useful standardized format. - Data Combination and Alignment: Combine multiple data sources to enable insights not possible from a single source.
- Descriptive Statistical Analysis: Take a data table and compute informative summary statistics from both the entire population and relevant subgroups
- Data Visualization: Generate insightful data visualizations to spur insights not attainable from point statistics
- Inferential Statistical Analysis and Modeling: Develop relevant predictive models and statistical analyses to generate insights about the underlying population and not simply the data at hand.
| Ingest and Cleaning | Combination and Alignment | Descriptive Statistical Analysis | Visualization | |
|---|---|---|---|---|
| Mini-Project #01 | ✓ | |||
| Mini-Project #02 | ✓ | ✓ | ½ | |
| Mini-Project #03 | ½ | ✓ | ✓ | ✓ |
| Mini-Project #04 | ✓ | ✓ | ✓ | ✓ |
In this mini-project, you are mainly responsible for data alignment and basic statistical analyses. While not the main focus of this mini-project, you are also expected to provide basic data visualizations to support your findings; the grading rubric, below, emphasizes the dplyr tools used in this project, but reports without any visualization will be penalized severely. Note that data visualization will play a larger role in Mini-Project #03.
As before, I will provide code to automatically download and read in the data used for this project. Also note that, as compared with Mini-Project #01, I am providing significantly less ‘scaffolding’: students are more responsible for directing their own analyses.
Rubric
STA 9750 Mini-Projects are evaluated using peer grading with meta-review by the course staff. Specifically, variants of the following rubric will be used for the mini-projects:
| Course Element | Excellent (9-10) | Great (7-8) | Good (5-6) | Adequate (3-4) | Needs Improvement (1-2) | Extra Credit |
|---|---|---|---|---|---|---|
| Written Communication | Report is well-written and flows naturally. Motivation for key steps is clearly explained to reader without excessive detail. Key findings are highlighted and appropriately given context. | Report has no grammatical or writing issues. Writing is accessible and flows naturally. Key findings are highlighted, but lack suitable motivation and context. | Report has no grammatical or writing issues. Key findings are present but insufficiently highlighted. | Writing is intelligible, but has some grammatical errors. Key findings are obscured. | Report exhibits significant weakness in written communication. Key points are difficult to discern. | Report includes extra context beyond instructor provided information. |
| Project Skeleton | Code completes all instructor-provided tasks correctly. Responses to open-ended tasks are particularly insightful and creative. | Code completes all instructor-provided tasks satisfactorially. | Response to one instructor provided task is skipped, incorrect, or otherwise incomplete. | Responses to two instructor provided tasks are skipped, incorrect, or otherwise incomplete. | Response to three or ore instructor provided tasks are skipped, incorrect, or otherwise incomplete. | Report exhibits particularly creative insights drawn from thorough student-initiated analyses. |
| Formatting & Display |
Tables have well-formatted column names, suitable numbers of digits, and attractive presentation. Figures are ‘publication-quality’, with suitable axis labels, well-chosen structure, attractive color schemes, titles, subtitles, and captions, etc. |
Tables are well-formatted, but still have room for improvement. Figures are above ‘exploratory-quality’, but do not reach full ‘publication-quality’. |
Tables lack significant ‘polish’ and need improvement in substance (filtering and down-selecting of presented data) or style. Figures are suitable to support claims made, but are ‘exploratory-quality’, reflecting minimal effort to customize and ‘polish’ beyond |
Unfiltered ‘data dump’ instead of curated table. Baseline figures that do not fully support claims made. |
Report lacks basic tables; OR report lacks basic figures. | Report includes one or more high-quality graphics (created using R) using tools beyond static basic ggplot2. These can be created using extensions toggplot2 or speciality packages for interactive graphics. |
| Code Quality |
Code is (near) flawless. Code passes all |
Comments give context of the analysis, not simply defining functions used in a particular line. | Code has well-chosen variable names and basic comments. | Code executes properly, but is difficult to read. | Code fails to execute properly. | Code takes advantage of advanced Quarto features to improve presentation of results. |
| Data Preparation | Automatic (10/10). Out of scope for this mini-project | Report modifies instructor-provided import code to use additional columns or data sources in a way that creates novel insights. |
Note that this rubric is designed with copious opportunities for extra credit if students go above and beyond the instructor-provided scaffolding. Students pursuing careers in data analytics are strongly encouraged to go beyond the strict ambit of the mini-projects to i) further refine their skills; ii) learn additional techniques that can be used in the final course project; and iii) develop a more impressive professional portfolio.
Because students are encouraged to use STA 9750 mini-projects as the basis for a professional portfolio, the basic skeleton of each project will be released under a fairly permissive usage license. Take advantage of it!
Submission Instructions
After completing the analysis, write up your findings, showing all of your code, using a dynamic quarto document and post it to your course repository. The qmd file should be named mp02.qmd so the rendered document can be found at docs/mp02.html in the student’s repository and served at the URL:1
https://<GITHUB_ID>.github.io/STA9750-2025-FALL/mp02.html
Once you confirm this website works (substituting <GITHUB_ID> for the actual GitHub username provided to the professor in MP#00 of course), open a new issue at
https://github.com/michaelweylandt/STA9750-2025-FALL/issues/new.
Title the issue STA 9750 <GITHUB_ID> MiniProject #02 and fill in the following text for the issue:
Hi @michaelweylandt!
I've uploaded my work for MiniProject #**02** - check it out!
https://<GITHUB_ID>.github.io/STA9750-2025-FALL/mp02.htmlOnce the submission deadline passes, the instructor will tag classmates for peer feedback in this issue thread.
Additionally, a PDF export of this report should be submitted on Brightspace. To create a PDF from the uploaded report, simply use your browser’s ‘Print to PDF’ functionality.
NB: The analysis outline below specifies key tasks you need to perform within your write up. Your peer evaluators will check that you complete these. You are encouraged to do extra analysis, but the bolded Tasks are mandatory.
NB: Your final submission should look like a report, not simply a list of facts answering questions. Add introductions, conclusions, and your own commentary. You should be practicing both raw coding skills and written communication in all mini-projects. There is little value in data points stated without context or motivation.
Hi @michaelweylandt!
I've uploaded my work for MiniProject #02 - check it out!
https://<GITHUB_ID>.github.io/STA9750-2025-FALL/mp02.htmlMini-Project #02: TBD
Data Acquisition
Incorporate the instructor provided code below into your report. As you do so, make sure to look for possible “keys” on which you will be able to join datasets together.
Extra Credit Opportunities
This work ©2025 by Michael Weylandt is licensed under a Creative Commons BY-NC-SA 4.0 license. 
Footnotes
Throughout this section, replace
<GITHUB_ID>with your GitHub ID from Mini-Project #00, making sure to remove the angle brackets. Note that the automated course infrastructure will be looking for precise formatting, so follow these instructions closely.↩︎