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 on this project. You can come up with your own teams and use Piazza to find prospective team members. If you can’t find a partner 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 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.
|Tuesday, October 23||Team Registration due|
|Monday, November 17 by 2pm (EST)||Project proposals due|
|Week of November 17-21||Project review meeting with your TF|
|Wednesday, December 10 by 11:59pm (EST)||IPython Process book due|
|Wednesday, December 10 by 11:59pm (EST)||Peer assessment due|
|Friday, December 12 by 11:59pm (EST)||Project webpage and screencast due|
|Tuesday, December 16||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.
You start by filling out this google form to define your teams and a tentative project title. This form should be filled out by October 23, 2014. The title can be change at a later date.
Next, your team will submit this project proposal form by November 17, 2014 by 2pm EST. Each team will only need to submit one form. Based on your proposals we will assign a TF to your team who will guide you through the rest of the project. You will schedule a project review meeting with your TF during regular lecture times of the week marked in the schedule. Make sure all of your team members are present at the meeting. Online students can schedule a Skype meeting with their TF.
iPython Process Book
An important part of your project is your iPython process book. Your process book details 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 process book is 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 in your process book.
Your process book 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 process book is a standalone document that fully describes your process and results. The iPython Process book is due Wednesday December 10, 2014 by 11:59pm EST. For instructions on how to submit, please see Submission Instructions below.
We expect you to write high-quality and readable Python code in your process book. You should strive for doing things the right way and think about aspects such as reusability, error handling, 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?s:
- 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 due Wednesday December 10, 2014 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 iPython process book and data should be linked to the web site as well, either using a zip file, github, bitbucket, or another code hosting site. Also embed your main visualizations and your screencast in your website.
The final project website is due Friday December 12, 2014 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 iPython process book 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 December 12, 2014 by 11:59pm EST. For instructions on how to submit, please see Submission Instructions below.
How to submit the iPython Process Book (due Dec 10)
- Make the data used for the final project accessible somewhere online (google drive, downloadable link, etc).
- Create a folder named lastname_firstinitial_project and place ONLY the iPython notebook in this folder. You may split up the iPython notebook into multiple notebooks if everything does not fit into one notebook. You should submit one folder per team, but make sure the names of all group members are inside the iPython notebook at the top. Do not include the data in this folder as the data may be very large and iSites may not be able to accept it. Inside the iPython notebook at the top, include instructions on where to access the data. Compress the folder (please use .zip compression) and submit it in the appropriate dropbox folder on iSites. If we cannot access your work or links because these directions are not followed correctly, we will not grade your work.
- Email your TF instructions on where to access the data and how to make the notebook become executable. Make sure the TF knows the name of the student who submitted the final project for the team.
How to submit the Peer Assessment (due Dec 10)
Each individual team member needs to fill out this google form for the peer evaluation. Your individual project score will take into account this self and peer assessment.
How to submit the Website and Screencast (due Dec 12)
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.
As stated in the syllabus, the final project is graded in two parts:
- Final Project Part I (worth 10% of total grade in CS 109). This portion represents the Project Proposal which is due Nov 17 by 2pm (EST).
- Final Project Part II (worth 25% of total grade in CS 109). This portion will be split into two sub-portions:
- 80% of the Final Project Part II will be based on your iPython process book. This includes the quality of your data analysis and Python 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 Dec 10 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 Dec 12 by 11:59pm (EST)
Your individual project score will also be determined by your peer evaluations.
CS 109 Final Project Winners 2014
Congratulations to all of our winners and honorable mentions!
- Textual Partisanship: Classifying Partisanship through Analysis of the Congressional Record by Eamon O’Brien, Ty Rocca and Tony Li (team members have requested to not publicly release website and screencast until Spring 2015)
- Predicting Hubway bike/dock availability by Lauren Alexander, Gabriel Goulet-Langlois and Joshua Wolff (Website, Screencast)
- Across the Bay 10K Race by Stacie Kuamoo (Website, Screencast)
Honorable Mentions (in no particular order)
- Ale Augur: Quantifying and Predicting Beer Preference with the Untapped API by Ian Nightingale, Alexander Jaffe and Brian Mendel (Website, Screencast)
- ClimbRec by Amy Skerry (Website, Screencast)
- The Green Canvas by Ahmed Hosny, Jili Huang and Yingyi Wang (Website, Screencast)
- Predicting Citation Counts of arXiv Papers by Marion Dierickx, George Miller, Sam Sinai and Stephen Portillo (Website, Screencast)
- Improving University Energy Efficiency: Building Energy Demand Prediction by Wen Xie, Wette Constant and Bin Yan (Website, Screencast)
- Predicting AirBnb Success by Enrique Dominguez-Meneses, Jameson Rogers, Noah Taylor and Muhammed Yildiz (Website, Screencast)