Course: NUS Data Science
There will be a lot of mathematics and statistics. It’s a lot more difficult than JC maths and stats. A lot more difficult. That was one of the things that came as a shock for me because I didn’t know it would be so intensive. It’s a hybrid so we take computing as well as maths and stats mods. We take more maths and stats so we are under NUS Faculty of Science (FOS) but next year, FOS will combine with FASS to form the College of Humanities and Sciences (CHS). Thus, I’m not sure if everything I say will apply for the next batch.
I didn’t know what else to do. I wanted to do business but my senior told me not to. She said if I didn’t know what to do, I should not take business as it’s general and may not be very beneficial for my career. I went to read up more on the courses available and I had an interest in coding so I decided to switch to take Data Science. I regret it but I didn’t actually spend hours researching my university course. I just went to the Open House and asked the seniors what the courses were like. I think I regretted taking Data Science in Year 1 but not in Year 2. Year 1 had a lot of maths and stats foundation. It was a very big jump from JC Maths. But from Year 2 onwards, it’s very manageable.
I took PCME but with H2 Chemistry, Mathematics, Economics and H1 Physics. The only related subject is maths. I don’t use chem, econs or physics anymore. In JC maths, we did study a little on statistics but it’s really the most basic level of statistics. Data Science goes way more in-depth and it’s a lot more confusing.
I didn’t have something that I really was passionate about. I wanted to do communications but I wasn’t certain about the job prospects. My priority was being more practical and my decision was based on money rather than passion. I went to see what courses had a high starting pay. Data Science was one of those courses. Another course was Industrial Systems Engineering (ISE) which has the highest starting pay in the engineering faculty. For NUS, I was choosing between Data Science and ISE. I went for the Open House and it was the determining factor in my choice. I was more interested in what Data Science has to offer. Data Science can be applied to a lot more industries and businesses. Career wise, I wanted to do marketing analytics where I take data, analyse and visualise it and generate marketing strategies for the company. For engineering, it has a high starting pay but it didn’t sound very interesting to me.
I mainly chose to study at NUS due to its proximity and the hall experience. From what I heard at the Open House, NUS’s Data Science also has a longer history and is more established.
I’m not on a scholarship but I know a lot of NUS merit scholars. For Data Science, I’m not sure whether there are Data Science scholarships but there are a lot of prestigious tech-related scholarships on BrightSparks.
If you don’t get a scholarship before university, it’s actually still ok. During university, you can still apply for scholarships. I have friends who got their scholarships at the end of Year 1 or Year 2. One of my friends in Year 2 just applied and was offered a government tech-related scholarship that is quite prestigious. I’m not sure what the name of the scholarship is. He has to serve a bond with them.
I think there is a change for the new Data Science course under CHS so I think you can get more information on the website but I will talk about my experience.
Unlike FASS, we have some core modules once you enter the course. We are also not like Business where Year 1 students get pre-allocated modules. Instead, for us, we have a suggested study plan. We follow this plan for the first 2 years. We will take the same core modules but we will bid for them to determine when we can take them so not every Data Science student is doing the exact same combination of modules at the same time.
We also have to complete our General Education (GE) and Unrestricted Electives (UE) modules. Some people want to clear them in Year 1 and Year 2, others in Year 3 and Year 4. It’s up to you to decide how you want to spread it and plan it out.
For Year 1, we have foundation modules and in Year 2, there are a lot more application modules. For Year 3 and Year 4, we get to choose which modules we want from 3 lists. One is a list of compulsory core Data Science modules, one is a list of Computer Science (CS) modules and the last is a list of Maths and Stats modules. We get to choose four modules from the latter two lists and another two from the core modules list.
* There are also Final Year Internship (FYI) for 4th year Data Science and Analytic students
For math, it’s mostly hand-written even during Covid. Pre-Covid, we wrote our answers on paper like in JC and did our examinations in the halls.
For the 2-3 CS mods I took, we had midterms, finals and practical examinations (lab). The midterms and finals are written, pen and paper exams that do not need coding. We also have a practical exam before the finals which purely involves coding.
Most of the assignments are individual. I’ve only had two group projects in my core modules. Due to Covid, a lot of the exams changed to open-book as they just assume we’re going to cheat. They made the exam more difficult but allowed it to be open-book. Pre-COVID, most of the exams are close-book where we are only allowed to bring one piece of double-sided cheat sheet.
For math modules, a lot of my math modules had assignments. Throughout the semester (the duration of the mod), we would have at least 3 assignments that we have to submit. We would always have midterms and finals. I have never had a core math mod that does not have midterms. The % of each exam is always different. It depends on the professor. Even if it’s the same module, the % can differ if it’s taught by different professors. Usually, the % does not differ much but I think the profs do have flexibility with the %.
For coding, CS modules have mid-term, finals and practical examination. For the four CS mods, all of them have graded weekly assignments. We are also graded on attendance. One of my modules was quite crazy. We had weekly lab assignments, weekly in-class quizzes, mid-terms, practical examinations and finals. The midterm and finals have a higher weightage. I think the weekly assignments have a low % and add up to 10-20% of the grade. They just want to encourage us to submit our work.
I like all the computing modules as I find them a lot more interesting than the math modules. I’m just not a maths person. For a lot of the math modules, the Teaching Assistants (TAs) are not strict about attendance. For the less disciplined students like myself, I tend to slip up halfway through the semester. If I don’t go for one tutorial, I’ll skip the rest for that module and it gets hard to catch up at the end for finals.
For computing modules, as they give us weekly assignments that are graded, we are forced to attend and have discipline.
I think it’s quite a lot. A lot of my friends are from FASS, Business School and School of Design and Environment (SDE). If I compare my workload to theirs, it’s really quite different. I heard from my friend that FASS and SDE have tutorials, on alternate weeks, which I’ve never heard before. I think the entire science faculty has weekly tutorials. Most of our modules take place two times a week and two hours each which is a lot. And we still have tutorials and assignments.
The tutorials don’t change when we have assignments so sometimes we have assignments and tutorials. Sometimes, the assignments are given at the same time by a few modules so those weeks would be a bit crazy.
For computing modules, I would say yes. You need to finish the weekly assignments as the tutorials would mainly go through that week’s assignment. As long as you finish the assignment, it’s ok.
Lessons are mainly conducted in the form of lectures and tutorials. In lectures, we normally have the whole class. During Covid, one entire class that can go up to 300 over students is in one call l for an online lecture.
For tutorials, we’ll split into smaller classes. If the class for that module is big, it can be up to 30-40 students but it can be smaller. We don’t really have discussions for math modules. I’ve only had one math module that required us to discuss. You would have to complete a set of questions he gave during the tutorial itself so it’s like a mini-quiz. Normally, we’ll discuss as a group and submit the tutorial.
I feel in JC, a lot of teachers will spoon-feed you and give you remedial lessons if you don’t understand anything. But in uni, if you don’t understand a concept, you have to actively seek answers from the professors or your friends and be more self-sufficient. One or two of my math modules in Year 1 had few resources as they don’t give us tutorial answers to encourage us to go for the actual tutorials. But during tutorials, the TA only went through certain questions that he found difficult so I think it’ll be hard for weaker students to understand. This was also a physical class where nothing was recorded. When I was preparing finals, I emailed the Professor and TA for tutorial solutions and they said they could not give any because they didn’t want us to rely on answers. I was very lost in that module and it was quite bad as it was my first semester after JC.
No. We are not like Business Analytics which I know has Marketing and Finance. For Data Science, there are no specialisations that I know of but I’m not super sure. But as I mentioned, in Year 3 and Year 4, we have the lists to choose from. If you want to take the “math track” and wish to be stronger in math, you can take more math and stats modules. If you want to focus more on computing modules, you can take those.
I think if you really hate math and you didn’t like it in JC, you should not enter this course. In JC, I liked maths but I don’t like it now because it’s quite intensive. I’m currently doing an internship and it’s not 100% about Data Science. I’m doing it at an accounting firm where I try to digitise their processes but there’s not much maths and stats. I don’t really see myself applying all the mathematical knowledge that I learnt. So I think it’s still ok. There are still Data Science careers that don’t really use maths.
There are a wide range of jobs. I think there’s a rise in demand for Data Science and Data Analytics jobs which Data Science kids can go into. Usually, it depends on the company. Let’s say I’m a Data Science intern in an accounting firm. I need to pick up accounting basics and understand what the company needs to make their processes more efficient. It’s the same for other firms. I have a friend doing machine learning in a company so she needs to work with real, actual machines. I think there really is quite a wide range of jobs and it depends on the company you are going to. I think one thing that Data Science kids can go into is also UIUX (User Interface, User Experience).
I’m not sure about the starting pay now because I have not graduated but so far, my friends who are taking up Data Science related internships have about 1K to 1.5-1.7K as their first internship salary.
Career wise, I think we face a lot of competition. We’re not just competing with our cohort which is growing rapidly in size, but also working adults who go for Data Science workshops and learn on the job. I think a lot of people do that. My current internship managers all took different degrees. One took Engineering, one took Mathematics, one took Economics–none of them are from Data Science. They just learn on the job.
I think what differentiates NUS Data Science from online workshops outside is the maths and stats which is really quite rigorous. For data science workshops outside which are 3 months or 6 months, they focus mainly on softwares like Tableau or vi. They teach you how to use it and give you projects to do. But for us, we spend one and half years on foundation maths and stats and I only started to do graphs and others in Year 2.
I guess so but after the first year, I also told myself that I could switch careers once I graduated. I think a lot of people do that too. They do something very different.
There is this portal called TeleConnect but I don’t think it’s very useful. I didn’t use that. I used LinkedIn and just googled Data Science Internships. Google would show you a lot of results from different websites. I think it’s good to connect with Data Science people on LinkedIn. That’s how I got my internship–through LinkedIn connections rather than applications. If students want to stand out, even before uni, or during Year 1, they can take part in Data Science hackathons. It’s hard to start but once you do one or two, you can get a hang of it.
Kaggle offers hackathons and projects you can do if I’m not wrong. Shopee also has a lot of hackathons but it’s quite difficult. If you are pre-uni and you want to learn stuff, there’s a lot of resources online like Data Camp and coding courses. But I don’t think it’s a must to know how to code before you go in. During my uni camp, my seniors were very shocked that I had never learnt coding before. They said that usually, the majority of the cohort knew how to code before they got into Computing or Data Science courses. It really scared me but I didn’t die and I really like coding now. There’s no need to learn coding before uni. When you first enter, they teach you coding from scratch. It’s a steep learning curve but it’s manageable as long as you put time and effort into it. One website I recommend is Stack Overflow. Whenever someone comes across a common coding error, people will upload the error on the website and other people will come and help. So whenever I come across an error, I will search the website and find someone else with the same problem.
We don’t have any that is specifically for Data Science but you can apply for the Science Faculty exchange programmes. It’s quite hard to map the modules for Data Science. Some places don’t have both CS and Maths, or some places only have CS or some places only have Maths. it’s just not that easy as compared to other courses like Business.
I chose to stay in Sheares Hall for two years. The workload was ok for me as there were quite a few Business Analytics and Data Science students staying in Sheares. For our first ever coding module, we created a group and so we helped each other by sharing answers. I think it’s very important to have friends for this whole Data Science course in general. I would advise you to make friends through camps.
In Year 1, I was in dance and I was a team manager for Soccer Boys where I was in charge of their admin and welfare. I was also a Publicity IC for Sheares Production which is a theatre CCA. I was also in this environmental club where I did their social media page. In Year 2, I was part of the sub-comm of both a sports management board and cultural management board. In one, I did publicity and in another, I did marketing. I didn’t join any CCAs outside of hall.
I have a group of friends who don’t stay in hall from Data Science. If you invest a lot of your time in hall, you won’t make friends outside the hall.During Year 1, you’re rushing to gain points (to secure your next semester’s stay) so you won’t join NUS CCAs. If you don’t have friends from camp, you won’t really see anybody outside of hall.
My hall mainly has Business and FASS kids so I make friends outside of my course. There are only 3-4 Data Science kids in hall and the rest are from other faculties. I think it’s quite hard for them to make friends outside of Data Science unless they are in any sort of committee or CCA. I think it’s good to join committees and CCAs in Year 1 to make friends.
Also, people don’t really make friends in class because it’s usually 45 minutes and you just leave. Most people would bid for the same tutorial slots and come with their friends from camp, CCA, orientation etc. My personal observation is that people don’t really take the initiative to interact with others in class unless forced to.
If you really really hate maths and stats, don’t come. But if you like math in JC and you think Data Science sounds like something you want to do, it’s quite fun.
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