Course Code: KCDAT_M_Y5 CA

GD Page 1 of 1 19/01/2022

MSc in Data Science – Data Analytics and Algorithms
Continuous Assessment

Provisional Date Due : Wednesday, 20th, April 2022
Time : 12:00 noon
Value : 100%

INSTRUCTIONS TO CANDIDATES:
• Include your name and student number at the top of YOUR Jupyter (or alternative) notebook and all code segments.
• Your will need to submit your Jupyter (or alternative) notebook etc. electronically (this must be accessible for at least 6 months
after the CA deadline).
• NOTE it is your responsibility to keep a backup of all files for this assessment.
• This is an INDIVIDUAL assessment; students will be assessed and marked individually.
• Any attempt at plagiarism will be referred to the office of the Registrar & VP for Academic Affairs and dealt with
accordingly.
• All usual exam regulations apply.

Project Description

You are required to complete a data analytics/machine learning based research and/or programming project. You are
free to choose any data analytics/machine learning related topic, however, your topic must be approved by your
lecturer. In addition, typically, your research MUST include a significant programming component (such as a
Jupyter notebook or equivalent) and a project report detailing your work (the report should be included in your
Jupyter/other notebook).

You are required to 1) complete this project on as large a data set(s) of your choosing as possible 2) to utilise at least
one advanced data analytics/machine learning algorithm (the number of algorithms used depends on the depth of your
work) 3) to provide appropriate description (including the lifecycle/process model used), analysis and critique of your
work and findings in your Jupyter/other notebook’s project report. Included in your report will be a detailed account
of the work undertaken justifying/rejecting the selection of algorithm(s) used in your project to process your chosen
data set(s), the pupose/reason/objectives for the work undertaken (including the hypotheses or research question or
objectives), the results obtained (including accuracy measures for the algorithms), your analysis and conclusions
based on the results obtained and further/future work. Include images, videos and tables etc. as you see fit.

The data set(s) chosen must be a data set(s) that has not been processed before using the tool(s) that you have chosen,
i.e. is not a sample/tutorial data set. You may use and/or refer to any software that is used in the process as you see fit.

As you develop your research/project you are required to maintain a log book to demonstrate your ideas, thinking, issues,
solutions to issues, etc. that arose as you developed your tutorial. A short recorded presentation (5 mins.) of your work
including your log book, your detailed analysis and conclusions must be submitted as part of your final notebook. This
should demonstrate your knowledge of all of your work and serves to verify authorship (No marks will be awarded for
this assessment without this presentation). This presentation may take the form of narrated PowerPoint slides, a
YouTube video, screencast or any other appropriate format of your choosing. This presentation (including your log/lab
book) will be worth 15% of the marks for the project and is a mandatory part of your final submision.

Your final submission is due by 12 noon on Wednesday, April 20th 2022, and as per all submissions, will be made
electronically and must include your notebook and all resources required for a complete review of your project work.

If you have any queries regarding this assessment, please do not hesitate to contact the lecturer at any time.

E&OE