Capstone Project Proposals for Spring 2022 are now open! You can access the applications at the links below.

Partners may choose to submit a capstone project(s) to the MDS Vancouver program (general data science) and/or the MDS Computational Linguistics program (language-related data science). If you’re unsure about which program to submit your project to, read more here.

Proposal forms can be found here:

Due to COVID-19, it is unclear whether 2022 Capstone Projects will be conducted in-person or remotely. However, after a very successful “remote Capstone” in 2020 and 2021, MDS is committed to facilitating Capstone Projects again in 2022.

Should I submit my proposal to MDS Vancouver or MDS Computational Linguistics?

The MDS Vancouver (MDS-V) program covers all aspects of data science, including topics of data wrangling, vizualisation, dashboards, statistics and machine learning, amongst others. You can read more about the program here and can see the capstone page on this website to learn more about the type of projects MDS-V addresses in capstone.

The MDS Computational Linguistics (MDS-CL) program covers similar topics to MDS Vancouver but with a focus on analyzing language/text-related data and building models that can extract insights from this data. Particular areas of expertise include deep learning, sentiment analysis, and multi-lingual methods such as machine translation. You can read more about the program here and can see the MDS-CL capstone page to learn more about the type of projects MDS-CL addresses in capstone.

Instructions for filling out the capstone proposal form

Detailed instructions for filling out the capstone proposal form can be found below.

About your organization

Briefly introduce your organization

Brief description of the problem/question

Just a brief description of the problem. If you’d like, you can suggest data science approaches that the students can take to address the problem, but this is not necessary.

Available data

Describe the data that you will make available to the students:

  • How much data is there?
  • What type of features are available?
  • How clean is the data?
  • In what form will the data be available to the students?

Data Product

What product(s) would you like to receive from our students, and what (in general) should it communicate or have the ability to do? Examples:

  • A dashboard, such as a Shiny or Dash app, to explore an aspect of your data
  • An R or Python package with documentation to simplify an analysis
  • A data pipeline that includes some data science model
  • A report outlining student findings

If your project requires confidentiality and IP assignment, please read our legal page on how we handle these before submitting your captstone proposal. During the proposal submission, we will ask you which types of agreements are necessary for working on the project. For non-UBC Capstone partners whose projects require confidentiality and IP assignment, we strongly recommend that partners show the UBC template documents to their legal counsel and get their agreement to use these documents before submitting the capstone proposal. We cannot sign alternate agreements, nor amend our agreements in any way.

We understand that you may require some restrictions to be put in place, but we also would like for our students to have some freedom to talk about the work they’ve done when applying for jobs. We want our students to know about these restrictions up-front so that they can make an informed decision about the project. In the proposal, please be as concrete as possible: do you anticipate students will be able to open-source the code they write? Publish a blog post about their work? Discuss it in a private job interview?

This section should also include any other requirements of students participating in the project, like background checks, etc.

Conflicts of interest

Declare any conflicts of interest. For example, if a current MDS student or family member is involved with your organization on a professional or personal level, this should be declared along with a short explanation. These situations are generally not problematic, but we prefer to disclose them to the students before they rank the projects.