Final Project

Published

December 14, 2026

Complete by: Monday 14 Dec. at 9am

Please note that I cannot accept any work past this deadline.

This Final Project report will bring together all of the elements of the project you’ve been working on since early in the semester: documentation, exploratory and descriptive analysis, predictive analysis, and good communication and writing. Some elements of this report will be revised versions of things you have already done, and some will be new analyses you will perform.

The report should be, roughly, 5-7 written pages (though this is hard to measure in a Jupyter notebook, so consider it a guideline). Think about this report as a “final takeaway” of all the skills you’ve learned in class over the semester. Below is a rough structure of your final written report.

This should be a ready-to-deliver report with clear section headers and interpretations of any statistical or graphical output (like several of our previous projects). You can review the Sample Final Project to get a sense of what you’ll need to accomplish.

Introduction & Data Explanation

  • This section can be a revised version of the writing from your Documentation and Descriptive Analysis projects, but it should be updated to suit what you’re doing in this report.
  • Provide a one-to-three paragraph introduction, professionally written, that gives an overview of the essentials someone needs to know to make sense of the data you show.
  • Consider some of the ethical and logistical challenges that your data presents, and discuss this in your introduction. Address the ethical issues in your project in terms of the ADSA’s four lenses.
  • You must cite and link to your dataset, and you can use Markdown to create contextual links like so: [text here](website).
  • Provide some details describing the data you are working with. What are the observations? The key variables you will be looking at? Are there any particular challenges in the data you will need to work through or be aware of during analysis?
  • Include a data dictionary of the key attributes of the data that you will analyze. You don’t need to include every one of your columns, but you should include all columns that you directly use in the report.
  • Provide and explain the guiding research questions for your report. This can include the research questions you used for the Descriptive Analysis, but you should also add questions that can be answered in the Predictive Analysis section (see below).

Descriptive Analysis and Exploration

  • This section should be a revised and updated version of your Descriptive Analysis project. Like before, the exact structure and presentation of the data is up to you.
  • You should add at least one more polished visualization and one more statistical analysis, for a total of 5 visualizations and 6 univariate or bivariate analyses. No more than two of these should be the same visualization type. Write text that describes the data and what the visuals tell you about your data or decisions you will need to make for the analysis.
  • The conclusions you come to in this section should be connected to what you are doing in the Predictive Analysis section.

Predictive Analysis and Interpretation

  • Provide at least two distinct statistical approaches (for example: linear regression and hypothesis testing; naive bayes classification and Kmeans clustering; KNN regression and random forest classification; etc.) that you interpret correctly and fully in the text. These can be whatever you choose, but you should explain why you chose the model you did, and why they fit the data. It’s recommended that you use two different approaches (i.e. not just two methods for regression or two methods for classification).
  • These approaches should be in response to research questions you’ve stated in your introduction. What have you learned about the data through running these tests or models?
  • Provide at least three polished visuals that specifically support and validate the model(s) you have developed (e.g., residual and regression line/scatter, histogram showing normality of data or residuals, confusion matrix, etc.), or help to communicate your main result. Visuals should have captions and be referred to clearly in your text, and they should not all be the same (e.g., not three scatterplots). You’ll likely need more than three to complete the usual validation steps.
  • Text should fully explain what you show and your findings, to someone who is unfamiliar with your data, code, and models, in terms of the data and in plain language.

Conclusions

  • Provide one or two paragraphs concluding about the data: what does it tell us, what are the limitations to this data/model, and what is one future direction you could envision for future data analysts or data collectors?
  • Include citations for the secondary references that you used in your Descriptive Analysis project, and add at least one more relevant source for your Predictive Analysis. Explain how each pertains to your conclusions and insights. You may cite a reference by linking directly to it in your markdown [text here](link here), and listing the full citation below the conclusions section as a Bibliography. Please ask me if you aren’t sure how to cite references.