Module Reviews: Y4S1

DSA4262: Sense-making Case Analysis: Health and Medicine

Lecturer: Young Lee

Assessment:
10% Homework/Exercise
30% Midterm
20% Project Part I
40% Project Part II

Overview
The module is basically some concepts you might have known being recalled such as regression techniques, classification techniques, Bayesian methods, neural networks, language models, and VAEs; and then along the way comes the project that feels just like DSA3101.

Module Difficulty
The module itself feels like DSA3101 practically and DSA4212 theoretically, so it's just somewhere around the middle of the difficulty line.

The workload below is measured weekly.
Lectures - 4h
While the timetable says 4 hours of lecture, Prof Young usually just goes through the lecture in an hour or two and then dismisses the class. The lectures were however not recorded but the slides were given anyways. There were a week of two where the lectures were a guest lecture, so that's pretty refreshing considering we're learning from the ones directly into the industry.

The Project
The project consists of two parts: creating a neural network to predict some given data (which its result would be compared amongst other project teams, allegedly), and exploring AI/ML problems in healthcare/medicine (create a proposal, make a technical report, and present it).

The logistics for the course felt a little rushed especially on the project, still couldn't get over how the details of the first part of the project was given AFTER the second part of the project despite the second part was the larger portion of the project, though both parts were independent. This is probably due to the constant change in the lecturer so one has to always start designing the course from scratch.

Personal Opinion
Overall, the module could've been done better but some aspects were okay here and there. Really wished there was more feedback given to the project along the way. My team had no idea how well we've done for the project beyond one or two simple questions about how our project works, which gave us the impression that the project was okay. It could also be the weird bell curve since the only partial grade we had was the midterm grade.

Expected grade: B+

DSA4299: Applied Project in Data Science and Analytics

Assessment:
10% Fortnightly Logs
40% Performance Assessment by supervisor
20% Internship Presentation
30% Internship Report

Overview
This module was my final year internship (FYI) module, which was one of my requirements for graduation (otherwise would be DSA4199 which is FYP). TLDR, you simply do an internship during the semester, get allocated a staff assessor/advisor to keep track of your internship progress, and then present your progress in the form of one presentation and one final report.

Module Difficulty
Depends on your internship, how you can describe the process of how things work there to a certain extent. All deliverables other than the Fortnightly logs are due at the end of the semester. There will be a staff visit within the semester but they're mainly just chill one-or-two-hour sessions.

Personal Opinion
Two tips that I would like to share are to have a good communication with both your company supervisor and your staff assessors/advisors, and to try relate your internship as best as you can with the modules you have taken in your entire DSA course, especially when writing it on the internship report and presentation.

One irk, probably major, is the lack of grade feedback that's given throughout the course. You will probably get some verbal feedbacks from your staff assessor, but that's about it. Since almost all professors in the department were staff assessors of this course, I didn't really know how standardised the gradings are, let alone the bell curve since there's no partial grades.

Regardless, if you're not doing FYP and you're in my batch I guess you had no choice. The next batches have a different set of final year graudation requirement.

Expected grade: B