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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 and 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
)
Deadline
* Early
15 December 2022
- Feedback can be given for improvements.
* Late
15 January 2023
You can see past projects here
*
https://up-mitc-ds.github.io/808exhibition
If you have any questions email:
* Prof. Vukosi Marivate
vukosi.marivate@cs.up.ac.za
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* Required
Organisation
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Your answer
Contact Person [Name and Surname]
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Your answer
Email
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Your answer
Project Information
Project Title
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Your answer
Who are the agencies/departments that will need to be involved?
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Your answer
Abstract [Up to 200 words]
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Your answer
Problem/Challenge Description [Up to 500 words]
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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?
Your answer
Goals - minimum 3 [In order of priority]
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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.?
Your answer
All about the data
Will students be allowed to publish (academic or popular media) outputs
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Yes
No
Maybe
Other:
What Data do you have internally?
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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?
Your answer
What data can you get externally and /or from public sources?
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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?
Your answer
Can the student use the data off-site?
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Yes
No
Other:
Will we need to obtain Ethical Clearance?
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Yes
No
Maybe
Other:
Will we need a Non-Disclosure Agreement
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Yes
No
Maybe
Other:
Can all or a sample of the data be shared publicly after the project?
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Yes
No
Maybe
Other:
If accepted will data be available by 11 February 2022?
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Yes
No
Other:
Supervision and Oversight
Name & Surname of main supervisor
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Your answer
Email of main supervisor
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Your answer
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.
Your answer
Email of mentor
Your answer
What resources will you make available?
Your answer
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