WATCH THIS BEFORE COMING TO NUS BIZ ANALYTICS (SoC)

21/22 Sem 1

IS1103 Ethics in Computing (4MCs)
Assessment:
Un-proctored 10MCQ x 10 weeks (100%)
Workload for this module for this sem was negligible.
10 MCQ per week on Luminus based off a textbook, with unlimited time. Most answers can be found in the text book by Ctrl+F. Some MCQs were tricky and at times it feels like there was no correct answers. Bellcurve is very high, I scored 93% overall and got a A. Lots of students forgot to submit their quizzes on time as well, so do remember to submit yours as the Prof will not entertain any excuses. Made friends in hall and outside by discussing our answers.

GEC1028/GEH1074 Luck (4MCs)
Link:   / gec1028geh1074_luck  

MA2001 Linear Algebra I (4MCs)
In this module, I learned advanced linear algebra topics such as linear systems, matrices, Gaussian elimination, and eigenvalues, among others. These form the bedrock of advanced concepts in my later years such as for Machine Learning topics. Essentially you will see these and use topics time and time again. Therefore this is quite an important topic to spend time on.
The module's assessments included weekly quizzes focusing on fundamental concepts, 5 questions per quiz. The best 10 out of 12 quizzes will be graded. You can collaborate when you do the quizzes.
There was homework assignments every three weeks, I had to discuss and copy my friends homework because I was getting to lost.
I downloaded and printed past year papers to practice, but were NOT an effective gauge of the standards of the finals because previous years did not allow the use of MATLAB, so many questions were computed by hand and the concept was rarely thought. However since our finals were held online, we were allowed the use MATLAB to calculate, an our whole paper more theoretical and difficult in general. (GET AN IPAD FOR UNI)

CS1010S Programming Methodology (4MCs)

1. Intro to python
2. Functional Abstraction
3. Recursion, Iteration & Order of Growth
4. Higher-order Functions
5. Data Abstraction & Debugging
6. Working with Sequences
7. Searching & Sorting
8. Implementing Data Structures
9. Object-Oriented Programming
10. Memoization, Dynamic Programming & Exception Handling
11. Visualising Data
12. Tuples, List, Dictionary, Class and relevant commands
Coursemology is an online platform where students complete tutorials, missions and assignments for EXP. There are 16 missions and 17 side-quests. Tutorials and after lecture practices are also done on the platform. To reach the max, level 50 is required. The level you end with constitutes your grade.
The coursemology missions took forever. I spend an average of 2 to 5 days per mission. I had to discuss pseudocode and real code (not allowed) It takes quite a bit of time and understanding for each mission.
Midterms and Finals were written exam papers so it was more conceptual but still tough. Practical exam was essentially a mission to be done within a time frame. You could practice with similar questions in previous years which was useful.
Thankfully we had a super patient head teaching assistant called Keng Hwee who would spend hours outside of class to clear student’s doubts and go through questions before and after the paper.
Funny story is that i absolutely BOMBED this module and i got a MASSIVE FAT D+, and i was going to retake this module you could not SU a D+, (You can choose to retake this module in the special term called AFAST but if you fail you have to wait one semester to take again) BUT thankfully the NUSSU pushed to change the SU policy (can SU D grades and above) and halfway through winter i sought to quit AFAST immediately. I was so devastated and worried that my entire School Of Computing journey would have been derailed.
It is important to note that many students from every faculty face this. Computer Science to FASS to Science kids. Be it CS1010E or CS1010 or CS1101S. Everyone faces the fear and reality of failing their first ever sem and module and some do even changing course.
If you find yourself in this situation, do not hesitate to seek help in anyway possible.

BT1101 Introduction to Business Analytics (4MCs)
The module was divided into two parts, with the first half taught by Prof Sharon and the second by Dr Desmond. It introduced basic concepts of business analytics, covering a range of topics from hypothesis testing and data mining to time series and statistical measures. The curriculum focused on three main areas: descriptive, prescriptive, and predictive analysis.
Fortunately, in my first (which was also my last) coaching session, a fellow student, Merrick, showed me how to load a CSV file in Rstudio—a task I had found baffling
The lecture notes were presented in various formats, including HTML, PDF, and R code, which required me to frequently copy and paste information to keep up. This approach was confusing and necessitated extra study and revision due to the disorganized nature of the material.