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Data Analytics ECS784U
i. 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. Deadline extensions can only be granted through approval of an EC claim
d. 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.
e. If you miss both the submission deadline and the late submission deadline, you
will automatically receive a score of 0.
iii. Submissions via e-mail are not accepted.
iv. The School requires that we set the deadline during a weekday at 10:00 AM.
v. For more details on submission regulations, please refer to your relevant student handbook.
2. Coursework overview
Coursework 2 involves applying causal structure learning to a data set of your choice. You will have to complete a series of tasks, and then answer a set of questions.
This coursework is based on the lecture material covered between Weeks 6 and 12, and on the lab material covered between Weeks 9 and 12.
The coursework must be completed individually.
Submission should be a single file (Word or PDF) containing your answers to each of the questions.
o Ensure you clearly indicate which answer corresponds to what question.
o Data sets and other relevant files are not needed, but do save them in case we
ask to have a look at them.
To complete the coursework, follow the tasks below and answer ALL questions enumerated in Section 3. It is recommended that you read this document in full before you start completing Task 1.
You can start working on your answers as early as you want, but keep in mind that you need to go through up to Week’s 12 material to gain the knowledge needed to answer all the questions.
TASK 1: Set up and reading
b) Download the Bayesys user manual.
c) Set up the NetBeans project by following the steps in Section 1 of the manual.
d) Read Sections 2, 3, 4 and 5 of the manual.
e) Skip Section 6.
f) Read Section 7 and repeat the example.
i. Skip subsections 7.3 and 7.4.
g) Read Section 8 and repeat the example.
h) Skip Sections 9, 10, 11 and 12.
i) Read Section 13.
i. Skip subsection 13.6.
TASK 2: Determine research area and prepare data set
You are free to choose or collate your own data set. As with Coursework 1, we recommend that you address a problem you are interested in or related to your professional field. If you are motivated by the subject matter, the project will be more fun for you, and you will likely perform better.
Data requirements:
Size of data: The data set must contain at least 8 variables (yes, penalty applies for using <8 variables). There is no upper-bound restriction on the number of the variables. However, we recommend using <50 variables for the purposes of the coursework to make it much easier for you to visualise the causal graph, and to save computational runtime. While the vast majority of submissions typically rely on relatively small data sets that take a few seconds to ‘learn’, keep in mind some algorithms might take hours to complete learning when given more than 100 variables!
i. You do not need to use a special technique for feature selection – it is up to you to decide which variables to keep. We will not be assessing feature selection decisions.
ii. There is no sample-size restriction and you are free to use a part of the samples. For example, your data set may contain millions of rows and you may want to use fewer to speed-up learning.