代写ECS784U、代写Python设计程序

日期：2022-03-13 09:31

Coursework 1 specification for 2022

Data Analytics ECS648U/ ECS784U/ ECS784P

Revised on 01/02/2022 by Dr Anthony Constantinou </br>

1. Important Dates

Release date: Thursday 3rd February 2022.

Submission deadline: Monday 14th March 2022 at 10:00 AM.

Late submission deadline (cumulative penalty applies): Within 7 days after deadline.

General information:

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.

2. 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.

viii. Key findings.

ix. Possible extensions or business applications of your project.

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.