Final Project

Final Projects

Final projects will be worth 100 points. They will be cumulative projects that require you to use the skills you developed across the entire semester. This is an applied project. We want you to develop an interesting research question that can be answered using the quantitative data analysis and visualization methods we have learned in this class.

Note - the items listed below should be considered as minimum guidelines and not just boxes to check.  Quality of the work, degree of effort shown, and interpretability and presentation clarity all should be given substantial weight.  Think of these blog as an opportunity to showcase your new skills in programming and data visualization in addition to your analytical and communication skills.

Requirements:

• Collect data from two or more different sources, including but not limited to: data APIs, web scraping, public data portals, FTP servers, or directly from an organization
• Clean, organize, and process the data using Python/pandas into a nice analyzable format
• Conduct a statistical analysis. This could include a series of descriptive statistics or analytical methods such as multiple regression, clustering, or spatial analysis.
• Create four or more static data visualizations, such as scatter plots, bar charts, line graphs, pie charts, etc. Feel free to use matplotlib, bokeh, or any similar tool.
• Create two or more interactive web maps. You can use CartoDB, leaflet, mapbox, or any similar tool, but it should be an interactive set of maps embedded in your blog.
• Describe your research question, its importance, your analysis, and your findings in a 2,000+ word write-up. 

Project proposal: 1-2 page paper explaining your project idea, the motivation, the prospective audience, where you will acquire your data, how you will process it, and how you will visualize it. What sort of findings do you expect? What new insights will they provide? Write up as a PDF and submit via bcourses by October 11. 

The final projects will have the following deliverables: a Wordpress blog post, all the code used to generate results for the project submitted as a Jupyter Notebook, and an in-class presentation.

• Wordpress blog post: compile all of the elements in the requirements section above into a comprehensive blog post covering your entire project. This should be something you can share in the future as part of your data science/urban planning portfolio.
• Jupyter Notebook: Submit via bcourses the code you developed for your project in a Jupyter Notebook, and paste the URL of your blog post as a comment to the submission. 
• In-class presentation: students who opt to make a presentation will present a short summary of their project and its findings in class during RRR week. These are very brief lightning talks, so produce a good set of slides and rehearse your talk so the timing is nailed down!