- Important Dates
- Release date: Thursday 3 rd February 2022.
- Submission deadline: Monday 14 th March 2022 at 10:00 AM.
- Late submission deadline (cumulative penalty applies): Within 7 days after deadline.
i.Some students will sometimes upload their coursework and not hit the submit button.
Make sure you fully complete the submission process.
ii.A penalty will be applied automatically by the system for late submissions.
a.Lecturers cannot remove the penalty!
b.Penalties can only be challenged via submission of an Extenuating Circumstances (EC) form which can be found on your Student Support page.
All the information you need to know is on that page, including how to submit an EC claim along with the deadline dates and full guidelines.
c.If you submit an EC form, your case will be reviewed by a panel. When the panel reaches a decision, they will inform both you and the Module Organiser.
d.If you miss both the submission deadline and the late submission deadline, you will automatically receive a score of 0. Extensions can only be granted through approval of an EC claim.
iii.Submissions via e-mail are not accepted.
iv.It is recommended by the School that we set the deadline during a weekday at 10:00 AM.Do not wait until the very last moment to submit the coursework.
v.For more details on submission regulations, please refer to your relevant handbook.
- Coursework overview and deliverables
Submission involves two files – report (see Deliverable 1) and Jupyter notebook (see Deliverable 2).
The coursework involves a data analytic report.
- You should address a data-related problem in your professional field or a field you are interested in (e.g., healthcare, sports, bioinformatics, gaming, finance, etc). If you are motivated in the subject matter, the project will be more fun for you, and you will likely produce a better report.
- Once you determine the area that interests you the most, you should search for a suitable data set or collate the data set yourself (see Section 6 for some data sources).
- You should apply TWO data analytic techniques of your choice, from those covered between Week 3 and Week 5.
Deliverable 1: Technical report.
- The technical report takes the form of a mini conference paper.
- The report shall have a length between 5 and 7 pages including references. Pages exceeding the page limit will NOT be marked.
- Font size should be not lower than 11, and page margins should be not lower than 2.
- Reports should be written with a technical audience in mind. It should be concise and clear, adopting the same style you would use in writing a scientific report.
- Some of the components your report should include:
i.Problem statement and hypothesis.
ii.Description of your data set and how it was obtained, including a sample of the data you used for your project, along with pointers to your data sources.
iii.Description of any data pre-processing steps you took (if any).
iv.What you have learnt from exploring the data, including visualisations.
v.How you chose which features to use in your analysis.
vi.Details of your modelling process, including how you selected your data analytic methods as well as the model through validation.
vii.Your challenges and successes.
ix.Possible extensions or business applications of your project.3
Deliverable 2: Jupyter notebook:
- You should submit your Jupyter notebook Python code as a separate PDF file. This is needed so that we can quickly refer to your code outputs while marking your report.
Please do not forget to add some comments to your code, similar to those added to the notebooks used in the labs.
a.In Windows, you can generate a PDF file by right clicking and selecting to ‘Print’ the Jupyter notebook loaded in your browser, and then you should be given an option to save it as a PDF file.
b.Do NOT copy-and-paste your notebook’s code into a word document, as this approach will not preserve the notebook’s format.
c.You do NOT need to submit your data set nor the actual .ipynb file. These might be requested at a later stage, if and only if we would like to review your code and/or data.
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