Group final project

Students must work in small groups on one the following types of final project:

  1. Write an original research paper: Articulate and answer a novel research question in international development. Your project should rely on the original analysis of non-traditional data.  Your goal should be to have the first draft of a short paper that could be published or presented at an academic research venue (here is an example of a successful project a past year). Be ambitious, be practical, and start early: you need to be able to obtain and analyze the required data within the course of a semester.  This project should be done in a group of 2-4.
  2. Build something useful: Build a public-facing website that transforms, merges, and analyzes non-traditional data, and provides output or analytics that are useful to people studying international development. Your tool must fill an unmet need, i.e., your contribution must be different from what is already out there.  Examples of what to strive for: Streetscore, DataChile, … This project should be done in a group of 3-5.
  3. Data journalism: Build a public-facing website that summarizes a recent paper or set of papers, or a recent approach to using big data for development. The goal is to clearly communicates technical concepts to a non-technical audience through compelling and visual data journalism.  Here is a  beautiful example; here is another scrollytelling example, and something simple based on my work. This project should be done in a group of 2-3


There are a few deliverables associated with each project:

  1. Extended Abstract (Due March 10): Submit a 2-3 page summary of your idea. Make sure to include the following in this summary: A title; the names of all group members; a 1-sentence summary of the research question you hope to answer (this sentence should end with a question mark!) or what you hope to achieve; a 1-paragraph abstract; a description of the data you will use; an overview of the methods you will use; and a list of the 3-5 most relevant papers. After reading this extended abstract, it should be clear to the reader that your project is both interesting and feasible (on this latter point, you must succinctly describe how your methods will allow you to answer your question based on your data). Think of this summary as laying out the terms of a contract that defines what you will accomplish by the end of the semester — the contract should be sufficiently well-specified that it will be easy to ascertain whether or not you have delivered on that contract.
  2. Lit review and summary statistics or mock-up (Due March 31):  For all project types: Conduct a thorough literature review of related work (or related projects in the private or public sector). Submit a 1-2 page annotated bibliography that describes the 5-10 most relevant papers.
    • For Research Projects: include a few pages of summary statistics of your primary datasets (this means you have to have your data in hand by this point!). Summary statistics include N, min/median/max/mean/SD of key variables, etc.
    • For Other projects: Include a mock-up, prototype, or wireframe of what your final deliverable will look like.
  3. Final presentation and poster session (Due May 3): On the last class, each group will present/demo their final project. Each team will have 10 minutes to present, with 5 minutes for Q&A.  Submit your slide deck via bCourses no later than midnight on Sunday; I will load this deck onto a single computer. I will be ruthless about cutting you off when your time is up, so please practice in advance. There is no requirement that everyone in the group speaks during this presentation, just do what feels right!
  4. Final report (Due April 30): If you are writing an original research paper, please submit an 8-10 page paper using this PNAS submission template.  If you are building a tool/website, aim for ~5 pages that motivate the need for the tool/website and discuss potential use cases.

What data can you use?

While there are no hard-and-fast requirements, your project should ideally involve at least one source of “big” or non-traditional data, and should pertain to development or other substantive topics covered in this course. A few datasets that you might find useful (see bCourses for other ideas).

Frequently-used development datasets

Satellite/RF Data

Other places to look for ideas: