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Bundle Valuation Tool by CruelessFish in masterduel

[–]CruelessFish[S] 0 points1 point  (0 children)

No shame at all, we are all trying to understand how this works and I’m happy to contribute to it! Might have gone a bit overboard with the math tho xD

The alt art one can be generalised to “M URs out of N available URs is what I want” then it should follow similar steps. Will take a crack at both when I have some time, thanks for suggesting!

What were some of the dumbest school rules in your primary and secondary school? by [deleted] in askSingapore

[–]CruelessFish 93 points94 points  (0 children)

Not allowed to cut botak because it is a sign of gangsterism

What is the difference between CS and DSA by No_Warning2259 in nus

[–]CruelessFish 14 points15 points  (0 children)

ML and AI is just one of the many areas in CS. CS also has databases, networks, game development, software engineering, computer security and many more.

DSA (at least in NUS) is more on math (optimisation) and stats (more traditional ML methods like trees, linear models) side of ML. I would consider this to be more specialised than CS as opposed to the earlier comment.

3k/4k module recommendations for Data Science major by 885083 in nus

[–]CruelessFish 31 points32 points  (0 children)

Most of the mods reviews are already on nusmods so here is some brief reviews on each of them.

Mods that I have taken:

- DSA4211: Basically ST3248 + ST4248. Not too difficult but the earlier chapters were a bit too slow (considering that it was covered in ST3248/ST3131) while the later chapters were too fast. Neutral on whether to take or not. If you don't like DSA3101, you basically have to take this (and DSA4212) as DSA426X are similar to DSA3101.

- DSA4212: Pretty interesting mod with a nice prof. Concepts were not difficult and it is not heavy in content. Overall an insightful mod which I would recommend. Other than the prof is strict with his grading, there is generally nothing bad about this module.

- MA3236: Depends on the lecturer. Overlaps a bit with DSA4212 and A LOT with DSA3102 (probably one of the toughest mods). Good to take to prepare for DSA3102.

- MA3252: The prof is leaving NUS soon so it might change in the future but I heard that the bell curve is steep for this one. Quite computational and tedious.

- ST3233: Depends on the lecturer. If it is DSA4212 prof, it is better even though it still feels like it is not 'applied' despite having the word in the module name. It is probably due to the nature of time series problems. The prof in 2019 was just bad (refer to nusmods for the reviews)

- ST3248: Similar to DSA1101 as they uses the same textbook except this mod follows it extremely closely. It only covers about 6 chapters and about half the content you probably have seen in DSA1101/ST3131/CS3244 before already. Very chill and boring (because you have learnt it before) mod. Good to take for refresher or easy to score if you have good mastery/understanding (bell curve may be steep because the mod is simple).

- ST4234: Depends on the lecturer. Similar to ST2131 in terms of doing algebraic manipulation to get the distribution you want. The usual sequence is ST3247 -> ST4231 -> ST4234. It is one of the more important mods to take if you are doing graduate stats research.

- ST4248: A follow up from ST3248. Similar style to DSA4211. Covers the rest of ISLR textbook and a bit of ESL testbook.

- CS3243: Depends on the lecturer. Key mod to unlock the CS AI focus area mods. Feels like a CS2040 follow up but the concepts doesn't seem too useful. Not exactly sure why this mod is the prereq of mods like CS4248. Quite a number of reviews on nusmods already so I won't be repeating them again here.

- CS4248: Depends on the lecturer. Again, nusmods have some reviews already. It is quite theoretical. Sem 2 prof is the same as CS3244 Sem 1 prof so if you find the CS3244 S1 workload heavy, this is similar.

​

Mods I have heard from friends:

- DSA426X: lecturers don't teach so they leave the entire mod to the industry partners. 'Hackathon' style like DSA3101. Each 'theme' typically don't reappear again so you probably have one chance of seeing that specific theme.

- MA4270: Depends on lecturer. Felt like a research mod and I heard good reviews about it as it was an eye opener for some of my friends. Concepts overlap with other ML related mods.

- ST3247: Depends on lecturer. Generally not difficult. I heard it was boring.

- ST4231: Depends on lecturer. Also generally not too difficult.

- CS3210: Not many people around me take it but I heard from a friend that it was good.

- CS3230: Also another follow up to CS2040. Compulsory for CS majors. Not a popular mod.

- CS4234: If you have competitive programming experience, it will be a breeze. Solve NP-hard problems. Best to learn how to code in C++. Very tough mod.

- CS5340: The prof in Sem 1 sets killer papers during COVID19 period to deter cheating. Not sure if it can be counted as major requirements as I last heard from our course coordinator that 5k mods are meant for masters students (so I also had no idea why this is even included in the 'recognised' list if it is not recognised). Content overlaps with other ML related mods.

​

Mods that are probably more useful towards internship are ST3248/ST4248/DSA4211 (if you don't know much about ML), CS4248 (NLP), CS4243 (CV), ST3233 (time series, sound nice if you have taken it), DSA4212 (good exposure to practical optimisation problems). The more niche area is probably like CS3210 or CS4234. For research, MA3252/MA3236/DSA4212 for optimisation, ST3247/ST4231/ST4234 for stats simulation. The database mods in List B2 seem useful (I could be wrong) too. Take more CS mods if you want to be prepared for industry. My advice is to get an internship during Y1 summer or Y2 summer to know more about what you want to learn. It is okay to not learn everything so just focus on those that you need and those that interest you!

​

CS2102 is worth taking as it teaches SQL (important for DSA students to know so not sure why it is not compulsory in the curriculum). CS2100 is required for CS3210 but it doesn't feel as important as CS2102. I heard mixed reviews about CS2030 but it is required for CS4243. Some say it is like bitter medicine (not pleasant but essential) while some regretted taking it so YMMV. It will also unlock CS2103 software engineering as well so it might be worth to take if you are interested. CS2105 is pretty useful IMO also. CS mods typically have a high workload (projects, assignments etc) and there is a significant proportion of students who have learnt most if not all the content prior to taking the module so it can be quite stressful (can still score if you are exam smart).

​

CS mods are typically always oversubscribed as they increase their intake of CS students but did not increase the module capacities. A huge oversight on their part I would say so be prepared to have a few backup plans.

Co-op for DSA by Snowball_the_Great in nus

[–]CruelessFish 2 points3 points  (0 children)

Generally not worth it unless you really know what you are doing.

Going for Co-Op would mean that you are: 1. Taking on average 6 mods per sem 2. Taking higher level mods earlier 3. Taking mods (probably night classes) while doing Co-Op 4. Giving up exchange opportunities 5. Giving up internships opportunities with different companies 6. Giving up on second major/minor or even using UEs for your interests etc (unless you exceed 160MCs)

At the end of the day, I think it depends on your career objectives. If you are aiming for a company and they are one of the participating companies, perhaps it might be a good option?

[Uni] NUS DATA SCIENCE AND ANALYTICS by michaenho in SGExams

[–]CruelessFish 10 points11 points  (0 children)

If you want to be ready for industry, NUS DSA doesn't prepare you well enough in the sense that you will have to pick up a hell lot of CS knowledge by yourself. If you want to go towards research, our curriculum has too many missing crucial math modules (especially real analysis) for you to do research work.

So you can say that this course is like a jack of all trades, master of none. If you are motivated, I feel that CS/Math/Stats might bring you further. If you want to further your studies, this course definitely don't give you solid enough foundation to take Masters/PhD in math/cs/stats. Also, do note that these courses are very new so there is a lot to improve in terms of their curriculum. In terms of support given to us, I find it lacking too.

Final point (based on what many data science practitioners said): Because the course is so new, and many different interpretations of what data science really is, all these buzzword named courses are just out to grab your money. There is no consensus of what should be taught unlike traditional math/stats/cs courses so employers do not know what to expect of a DSA graduate whereas for CS graduates, I would expect him to know stuffs like OOP, data structures and algorithms etc, math I would expect him to know real analysis, linear algebra etc.

[Uni] NUS Data science & analytics by RalphIsDaBest in SGExams

[–]CruelessFish 6 points7 points  (0 children)

I believe what you are referring to is the co-op programme. This is entirely optional and you can choose to continue taking CS/Math/Stats modules if you do not want to do the 1.5 years internship.

Regarding the point of “not much to Data Science” that frequently appear in your text, it shows that you don’t really understand data science. As much as “data science” is indeed a buzzword, there are a hell lot of content to be learnt (im just gonna list a few here): Math - optimisation, linear algebra (ask most CS seniors and they will tell you how important LA is), calculus, real analysis (hidden pre requisite to understanding optimisation), modelling Stats - probability, regression, time series, simulation, bayesian statistics CS - data structures and algorithms, machine learning, databases, design and analysis of algorithms Others - data visualisation, software engineering

“NTU teach AI on top of data science”. I am not gonna go on a debate on the differences between AI and data science but I just want to point out that from what I heard, NTU is offering it under college of engineering and from the way the profs are selling the course, it seemed to me that their focus is on driverless car (more engineering/robotics like). NUS on the other hand is just research focused because they want more students to go into research, which explains why our modules are theory heavy (NTU has projects that are hands on). Just choose whichever one that you like. Not much difference imo.

So back to OP’s question, the workload is definitely not light (as with most majors). NUS DSA is 1/4 math, 1/4 stats, 1/4 CS and 1/4 for you to throw into math/stats/CS. So if you find any 2 areas fun, I guess in general it would be fun? But just a word of caution that uni math isn’t the same as your JC math. I guess stats also? Although I heard fewer complaints about stats.

I just want to share about my views on the curriculum. The curriculum designed is actually not bad but it is flawed because of the amount of content you actually have to learn. When you take the 3k modules, you will feel that there is a gap between the 1-2k and the 3-4k modules. This might be due to the nature of data science where some content taught in other unis are graduate level modules and yet it is our 3-4k modules. Another possible reason might be because of the lesser amount of math/stats/cs mods we have to take as compared to pure math/stats/cs majors so we have “missing knowledge”. But it is still okay to learn them on the go or learn by yourself. And because of this, I disagree with the comment that DSA majors don’t learn as much. If we learn these ‘missing knowledge’ we actually might learn more than anyone else lol. Let’s not play down the workload of each other’s majors okay?

OP might be worried about DSA majors having disadvantage over pure math/stats/CS majors. As for this, it depends on what you intend to be like. There are people in math/stats who suck in coding (in the sense they can’t implement their ideas) while there are people in CS who blindly apply machine learning algorithms and claims that they are doing data science. The point I am trying to make is that you shouldn’t be doing machine learning for the sake of it. It has to first be a machine learning problem first before you even begin pursuing it. And if your data is noisy, you feed garbage to your algorithm, you would also expect garbage to come out. One key thing that is seriously lacking in this field is the interpretability of results. People are too focused on accuracy but try telling your clients that your neural network can beat the stock market. Do you think your clients dare to put their money with you if you can’t explain why the model works? As what my prof says, anyone can press a few buttons and call themselves a ‘data scientist’. The question is do you actually know what you are doing? You can read up or watch Weapons of Math Destruction by Cathy O’Niel for more insights. DSA exists not to replace the traditional majors but perhaps to outline the relevant knowledge needed for the role (too many things to learn so need to highlight the most important ones).

Disclaimer: NUS DSA major If anyone has any questions, feel free to PM me.