For your final assignment in this course you will work on a month-long data science project. The goal of the project is to go through the complete data science process to answer questions you have about some topic of your own choosing. You will acquire the data, design your visualizations, run statistical analysis, and communicate the results.
You will work closely with other classmates in a team of 3 on this project. You can come up with your own teams and use Canvas to find prospective team members. If you can’t find partners we will team you up randomly. We recognize that individual schedules, different time zones, preferences, and other constraints might limit your ability to work in a team. If this is the case, ask us for permission to work alone. In general, we do not anticipate that the grades for each group member will be different. However, we reserve the right to assign different grades to each group member based on peer assessments (see below).
There are a few milestones for your final project. It is critical to note that no extensions will be given for any of the project due dates for any reason. Late days may not be used. Projects submitted after the final due date will not be graded. If you anticipate any issues (e.g., due to business travel) you need to send an email to the staff mailing list at least one week in advance.
|Friday, April 8 by 11:59pm (EST)||Form a team and submit a project proposal due date|
|Week of April 11-17||Project review meeting with your TA|
|Wednesday, May 4 by 11:59pm (EST)||RMarkdown and compiled HTML due|
|Wednesday, May 4 by 11:59pm (EST)||Peer assessment due|
|Friday, May 6 by 11:59pm (EST)||Project webpage and screencast due|
|Wednesday, May 11||Project presentations and best project prizes|
There are several deliverables for your project that will be graded individually to make up your final project score.
Team Registration and Proposal
You start by filling out this google form to define your teams and project proposal. This form should be filled out by Friday April 8, 2016 by 11:59pm (EST). The title can be changed at a later date. Each team will only need to submit one form. Based on your proposals you will be assigned a TA to your team who will guide you through the rest of the project. You will schedule a project review meeting with your TA the following week (April 11-17, 2016). Make sure all of your team members are present at the meeting. Online students can schedule a Skype meeting with their TA.
RMarkdown and HTML files
An important part of your project is the RMarkdown and HTML files. This will detail your steps in developing your solution, including how you collected the data, alternative solutions you tried, describing statistical methods you used, and the insights you got. Equally important to your final results is how you got there! Your RMarkdown and HTML files are the place you describe and document the space of possibilities you explored at each step of your project. We strongly advise you to include many visualizations.
Your RMarkdown should include the following topics. Depending on your project type the amount of discussion you devote to each of them will vary:
- Overview and Motivation: Provide an overview of the project goals and the motivation for it. Consider that this will be read by people who did not see your project proposal.
- Related Work: Anything that inspired you, such as a paper, a web site, or something we discussed in class.
- Initial Questions: What questions are you trying to answer? How did these questions evolve over the course of the project? What new questions did you consider in the course of your analysis?
- Data: Source, scraping method, cleanup, etc.
- Exploratory Analysis: What visualizations did you use to look at your data in different ways? What are the different statistical methods you considered? Justify the decisions you made, and show any major changes to your ideas. How did you reach these conclusions?
- Final Analysis: What did you learn about the data? How did you answer the questions? How can you justify your answers?
As this will be your only chance to describe your project in detail make sure that your RMarkdown file and compiled HTML file are standalone documents that fully describes your process and results. The RMarkdown and HTML files are due Wednesday, May 4 by 11:59pm (EST). For instructions on how to submit, please see Submission Instructions below.
We expect you to write high-quality and readable R code in your RMarkdown file. You should strive for doing things the right way and think about aspects such as reproducibility, cleaning data, etc. We also expect you to document your code.
It is important to provide positive feedback to people who truly worked hard for the good of the team and to also make suggestions to those you perceived not to be working as effectively on team tasks. We ask you to provide an honest assessment of the contributions of the members of your team, including yourself. The feedback you provide should reflect your judgment of each team member:
- Preparation - were they prepared during team meetings?
- Contribution - did they contribute productively to the team discussion and work?
- Respect for others’ ideas - did they encourage others to contribute their ideas?
- Flexibility - were they flexible when disagreements occurred?
Your teammate’s assessment of your contributions and the accuracy of your self-assessment will be considered as part of your overall project score. The peer assessment is due Wednesday, May 4 by 11:59pm (EST). For instructions on how to submit, please see Submission Instructions below.
You will create a public website for your project using Google Sites or Github Pages or any other web hosting service of your choice. The web site should effectively summarize the main results of your project and tell a story. Consider your audience (the site is public) and keep the level of discussion at the appropriate level. Your RMarkdown file, HTML file and data should be linked from your GitHub Repository (see below) to the web site as well. Also embed your main visualizations and your screencast in your website.
The final project website is due Friday, May 6 by 11:59pm (EST). For instructions on how to submit, please see Submission Instructions below.
Each team will create a two minute screencast with narration showing a demo of your project and/or some slides. Information about how to prepare these screencasts can be found here. Please make sure that the sound quality of your video is good - it may be worthwhile to invest in an external USB microphone. Upload the video to an online video-platform such as YouTube or Vimeo and embed it into your project web page. We will show the best videos in class.
We will strictly enforce the two minute time limit for the video, so please make sure you are not running longer. Use principles of good storytelling and presentations to get your key points across. Focus the majority of your screencast on your main contributions rather than on technical details. What do you feel is the best part of your project? What insights did you gain? What is the single most important thing you would like your audience to take away? Make sure it is upfront and center rather than at the end.
The final project screen cast is due Friday, May 6 by 11:59pm (EST). For instructions on how to submit, please see Submission Instructions below.
How to submit the RMarkdown and HTML files (due May 4)
- Create a GitHub repository which should include the data used for the final project, the RMarkdown file and the compiled HTML file. If the data are too big to fit in the repository, make the data accessible somewhere online (google drive, downloadable link, etc). Inside the RMarkdown file at the top, include instructions on where to access the data. If we cannot access your work or links because these directions are not followed correctly, we will not grade your work.
- You should only have one GitHub repository per team, but make sure the names of all group members are inside the RMarkdown file at the top.
- Email your TA instructions on where to access the data and the location of your GitHub repository.
How to submit the Peer Assessment (due May 4)
Each individual team member needs to fill out this google form for the peer evaluation. Your individual project score will take into account your self and peer assessment.
How to submit the Website and Screencast (due May 6)
Fill out this google form to submit the links to the website and screen cast. If we cannot access the website or screencast, we cannot grade it.
The final project is graded in two parts:
- Final Project Part I (worth 10% of total grade in BIO 260 / CSCI E107). This portion represents the Team Registration and Final Project Proposal which is due April 8 by 11:59pm (EST).
- Final Project Part II (worth 25% of total grade in BIO 260 / CSCI E107). This portion will be split into two sub-portions:
- 80% of the Final Project Part II will be based on your RMarkdown and HTML files in your GitHub repository. This includes the quality of your data analysis and R code, the complexity and level of difficulty of your project, completeness and overall functionality of your analysis. This sub-portion (and peer assessment) is due May 4 by 11:59pm (EST)
- 20% of the Final Project Part II will be based on your web site and screencast and the quality of their storytelling aspects. This sub-portion is due May 6 by 11:59pm (EST)
Your individual project score will also be determined by your peer evaluations.
Example Final Projects
Here are some examples of successful final projects. Note: These projects came from another course we taught on Data Science similar to this one except the previous course used Python, not R.
- Predicting Hubway bike/dock availability (Website, Screencast)
- Across the Bay 10K Race (Website, Screencast)
- Ale Augur: Quantifying and Predicting Beer Preference with the Untapped API (Website, screencast no longer available)
- The Green Canvas (Website, Screencast)
- Predicting Citation Counts of arXiv Papers (Website, Screencast)
- Improving University Energy Efficiency: Building Energy Demand Prediction (Website, Screencast)
- Predicting AirBnb Success (Website, Screencast)