Project proposal due Friday, June 3 at 11:59pm
Draft report due Monday, June 13 at 11:59pm
Peer review due Wednesday, June 15 at 11:59pm
Final report and team repository due Wednesday, June 22 at 9:29am
Presentation on Thursday, June 23 at 9:30am
Peer evaluation due Thursday, June 23 at 12pm
The goal of this project is to demonstrate proficiency in data science techniques by conducting a novel analysis of a dataset of your own choosing or creation. The dataset may already exist, or you may collect your own data using a survey. You will also practice effective oral and written communication of your results.
The final project will be done in groups of 3-4 members. Click here to find your team number and members.
The five deliverables for the final project are
Late projects will not be accepted. As per the syllabus, you and your team must complete all components of the final project to pass the course.
The grade breakdown is as follows:
| Total | 100 pts |
|---|---|
| Project proposal | 5 pts |
| Written report | 50 pts |
| Repository | 5 pts |
| Peer review | 10 pts |
| Slides | 10 pts |
| Presentation | 20 pts |
To perform a successful analysis it is imperative that you choose a manageable dataset that can be analyzed using the tools we have learned in STA 199. This means that the data should be readily accessible, not contain too many missing values, and be large enough so that multiple relationships can be explored. Your dataset must have at least 500 observations and at least ten variables (or has been approved by your instructor). The dataset should include a rich mix of categorical, discrete numeric, and continuous numeric data. If you have any doubts or are having trouble please reach out to me.
All analyses must be done in RStudio and your final written report and analysis must be reproducible. This means that you must create an R Markdown document attached to a GitHub repository that will create your written report exactly upon knitting.
If you are using a dataset that comes in a format that we haven’t encountered in class (for instance, a .DAT file), make sure that you are able to load it into RStudio as this can be tricky depending on the source. If you are having trouble, google first, then ask for help.
Datasets that cannot be used:
Datasets used as examples/homework in this class
Data that have been analyzed in another class
Kaggle
The resources below may be helpful for finding an interesting dataset but feel free to explore on your own.
The proposal serves two purposes:
Choose three substantially different datasets you are interested in analyzing. For each, identify the components below.
glimpse() function to glimpse your data and include the output at the end of your proposalClick here for examples.
Order your proposals from first choice to third choice.
Submit the PDF of your proposal to Gradescope by Friday, June 3 at 11:59pm. I will provide feedback on your proposal and help you decide which dataset you should use for your final project. Project proposals should have no more than one page of text. (With the glimpse() it is likely to go beyond a page total.)
The project proposal will be graded as follows:
| Total | 5 pts |
|---|---|
| Introduction / data | 2 pts |
| Data glimpse | 1 pts |
| Research questions | 2 pts |
Your final report must be written using R Markdown. Before you finalize your report, make sure the printing of code chunks is turned off with the option echo = FALSE.
Your final report must match your GitHub repository exactly. The mandatory components of the report are as follows, but feel free to expand with additional sections as necessary. Your final written report should not exceed ten pages inclusive of all tables and figures.
The written report is worth 50 points, broken down as follows:
| Total | 50 pts |
|---|---|
| Introduction / data | 5 pts |
| Methodology | 12 pts |
| Results | 20 pts |
| Discussion | 10 pts |
| Formatting | 3 pts |
The introduction provides motivation and context for your research. Describe your topic (citing sources) and provide a concise, clear statement of your research question and hypotheses.
Then identify the source of the data, when and how they were collected, relevant context and a general description of relevant variables.
The methodology section should include justification of the choice of statistical method(s) used to answer your research question. You can also include summary statistics or figures used in exploratory data analysis to investigate your research question.
Place figure(s) here to illustrate the main results from your analysis. One beautiful figure is worth more than several poorly formatted figures. You should not use the default gray background ggplot theme.
Showcase how you arrived at answers to your research question using the techniques we have learned in class (and beyond, if you’re feeling adventurous).
Provide only the main results from your analysis. The goal is not to do an exhaustive data analysis (calculate every possible statistic and perform every possible procedure for all variables). Rather, you should demonstrate that you are proficient at asking meaningful questions and answering them using data, that you are skilled in interpreting and presenting results, and that you can accomplish these tasks using R. More is not better.
This section is a conclusion and discussion. This will require a summary of what you have learned about your research question along with statistical arguments supporting your conclusions. You should critique your own methods and provide suggestions for improving your analysis and future work. Issues pertaining to the reliability and validity of your data and the appropriateness of the statistical analyses should also be discussed. Also include a brief paragraph on what you would do differently if you were able to start over with the project or what you would do next if you were going to continue work on the project.
Your project should be professionally formatted. For example, this means labeling graphs and figures, turning off code chunks, and using tidyverse style.
In addition to your Gradescope submissions, we will be checking your GitHub repository. The repos should be formatted in the same manner as labs with a data folder. This repository should have equal contribution by all team members with regular meaningful commits / pushes and should include
Style and format does count for this assignment, so please take the time to make sure everything looks good and your data and code are properly formatted.
Critically reviewing others’ work is a crucial part of the scientific process, and STA 199 is no exception. You will be assigned a team to review. As part of the review process, you must provide your partner team a copy of your current report by Monday, June 13 at 11:59pm. After giving the report to your partner team, they will have until Wednesday, June 15 at 11:59pm to provide a detailed critique about the written report and data analysis. This feedback is intended to help you create a high quality final project, as well as give you experience reading and constructively critiquing the work of others.
During the peer review process, you will be provided read-only access to your partner team’s GitHub repo. Provide your feedback in the form of GitHub issues to your partner team’s GitHub repo. Click here to find your partner team.
Peer review will be graded on the extent to which it comprehensively and constructively addresses the components of the partner team’s report: the research context and motivation, exploratory data analysis, and any inference, modeling, or conclusions. As you work on the draft, the focus should be on the analysis and less on crafting the final report. Your draft must include a reasonable attempt at each analysis component - exploratory data analysis, inference or modeling, and drawing initial conclusions.
Click here for detailed guidelines on how to create GitHub issues and relevant questions to answer in the peer review process.
For your presentation, you and your team must also create presentation slides that summarize and showcase your project. Introduce your research question and dataset, showcase visualizations, and provide some conclusions. These slides should serve as a brief visual accompaniment to your write-up and will be graded for content and quality.
The slide deck should have no more than 8 content slides + 1 title slide. Here is a suggested outline as you think through the slides; you do not have to use this exact format for the slide deck.
Title Slide
Slide 1: Introduce the topic and motivation
Slide 2: Introduce the data
Slide 3 - 5: Highlights from exploratory data analysis
Slide 5 - 7: Inference / modeling
Slide 8: Conclusions + future work
Ask questions if any of the expectations are unclear.
Code: In your write-up your code should be hidden (echo = FALSE) so that your document is neat and easy to read. However your document should include all your code such that if I re-knit your Rmd file I should be able to obtain the results you presented. Exception: If you want to highlight something specific about a piece of code, you’re welcome to show that portion.
Merge conflicts will happen, issues will arise, and that’s fine! Commit and push often, and ask questions when stuck.
The project is very open ended. For instance, in creating compelling visualization of your data in R, there is no limit on what tools or packages you may use. You do not need to visualize all of the data at once. A single high quality visualization will receive a much higher grade than a large number of poor quality visualizations.
Make sure each team member is contributing, both in terms of quality and quantity of contribution (we will be reviewing commits from different team members).
All team members are expected to contribute equally to the completion of this assignment and peer evaluation will be given at its completion - anyone judged to not have sufficient contributed to the final product will have their grade penalized.
While different teams members may have different backgrounds and abilities, it is the responsibility of every team member to understand how and why all code and approaches in the assignment works.
Pay attention to details in your write-up. Neatness, coherency, and clarity will count.
Write all R code according to the style guidelines discussed in class.
Check here for more tips on formatting and communicating results.
Grading of the project will take into account the following:
A general breakdown of scoring is as follows:
You will be asked to fill out a survey where you rate the contribution and teamwork of each team member by assigning a contribution percentage for each team member with justification. This will contribute to your final project grade. Final survey evaluations will be due Thursday, June 23 at 12pm.
There is no late work accepted on this project! Be sure to turn in your work early to avoid any technological mishaps.