MIT 808: Big Data Science Capstone Project Proposals [20 Credits, Half-Year]
This is a project proposal submission page for MIT 808: Big Data Science Capstone Project. Submissions are only for organisations, not students.

MIT Big Data Science 2nd Year students will be performing a Data Science Capstone project for the first half of the year. They will be following a process similar to the Epicycles of Analysis (https://bookdown.org/rdpeng/artofdatascience/epicycles-of-analysis.html)

Deadlines
  • Submission Deadline: Jan 19 2026 (deadline 2)
You can see past projects here.

If you have any questions, email:

* Prof. Vukosi Marivate vukosi.marivate@cs.up.ac.za
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Organisation *
Contact Person [Name and Surname] *
Email *
Project Information
Project Title *
Who are the agencies/departments that will need to be involved? *
Abstract [Up to 200 words] *
Problem/Challenge Description [Up to 500 words] *
Provide a description of the problem/challenge including the following: What is the problem that the study will be investigating? Why is this a problem? What are the expected outcomes of the study? How does this study address either data science or big data?
Goals - minimum 3 [In order of priority] *
Some examples: What are you trying to predict? What action are you trying to maximise(or minimise)? What are you trying to diagnose or understand etc.?
All about the data
Will students be allowed to publish (academic or popular media) outputs *
What Data do you have internally? *
For each try to answer: What does it contain? What level of granularity? How frequently is it collected/updated after it’s captured? Does it have unique identifiers that can be linked to other data sources? Who’s the internal owner of the data? How is it stored?
What data can you get externally and /or from public sources? *
For each try to answer: What does it contain? What level of granularity? How frequently is it collected/updated after it’s captured? Does it have unique identifiers that can be linked to other data sources? Who’s the internal owner of the data? How is it stored?
Can the student use the data off-site? *
Will we need to obtain Ethical Clearance? *
Will we need a Non-Disclosure Agreement *
Can all or a sample of the data be shared publicly after the project? *
If accepted will data be available by 11 February 2024? *
Supervision and Oversight
Name & Surname of main supervisor *
Email of main supervisor *
Name & Surname of mentor
Can be a research assistant, post doc etc. Will provide faster responses to questions from students and will be main contact point.
Email of mentor
What resources will you make available?
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