AY 25/26, Sem 1
Instructor:
Dr Jonathan Leong
Content (Structure/Organization):
-
Manageability of Workload:
This is one of the NUSC courses with a light workload, around 1 hour on average every week. However, the group project will take more time (~3 hours those two weeks) especially if you're working on the slides or more challenging parts of the report, but it is very manageable!
Ease/Difficulty of Attaining Grades:
Very achievable!
Learning Value/Recommendation:
I would say that the technical skills on excel (and maybe Radiant if this software can be used in future projects) are extremely helpful for beginners!
About the Instructor:
Prof. Jonathan Leong always makes the class very lively with fun references even when the concepts gets boring. Prof is good at covering the key concepts in unique ways, giving examples and new ways of understanding or remembering them. However, unlike other tutorial groups, Prof doesn't have weekly group presentations or individually check your tutorial questions, so you do have to keep yourself accountable (and not pile everything till the last minute!) Despite this, Prof. Jonathan is always open to conference and answering any questions, giving advice, etc.
AY 25/26, Sem 1
Instructor:
Dr Francesca Spagnuolo
Content (Structure/Organization):
I don't think any topic is particularly difficult to grasp, I think it's more of how much they twist the English in the questions (in the final paper) that trips everyone up (e.g. making absolute statements and asking you if it's true/false).
Only NUSC GEA classes had experiments (during tutorials) and data challenges but I didn't see a point in neither. In the experiments, we had to use Radiant to do our data visualisation (but Radiant wasn't tested in our finals so I didn't entirely understand why we need to put in so much time to learn this software). My personal opinion is that the data challenges didn't have much learning value so I didn't feel there was much purpose in completing it.
Manageability of Workload:
Not heavy, just complete lecture (compulsory) and tutorial (optional I think). I did not do my tutorials before class since my prof made us discuss and solve tutorial questions as a group in class.
Ease/Difficulty of Attaining Grades:
Quite difficult from my understanding due to the steep bell curve for finals. But spamming the practice papers does help one to understand the question types that could possibly come out.
Learning Value/Recommendation:
Not much, but it could be a useful general knowledge course I guess. More Excel would better enhance the learning of this course (in my opinion).
About the Instructor:
Prof. Francesca is not teaching here anymore. I think she did try to facilitate class discussions but to not much avail because our class was quite early. She used tutorial time to go through key concepts in the lectures, gave us some questions to try, and addressed misconceptions which I appreciated. I would have appreciated it if she could upload her slides before lessons so I can annotate as she speaks (it doesn't need to be uploaded the day before, just 1h before class is fine too). Would appreciate less time spent on experiments and more time spent on tutorials.
Additional Comments/Word of Advice:
I don't see a point that the NUSC version of these classes should be conducted every week if most of the extra time is spent on experiments that don't add much value to my learning.
I'm unsure if this is NUSC's directive but I did not appreciate being taught Radiant and being informed that radiant was not going to be tested for finals only a few weeks before finals. Many of my classmates shared the same sentiment of "why did we bother to learn Radiant if they were only going to test excel?" My non-NUSC friends mostly did excel in their tutorial and I would've appreciated more excel practice in class (rather than me learning how to do specific excel functions through watching youtube videos before finals).
AY 25/26, Sem 1
Instructor:
Dr Tan Da Yang
Content (Structure/Organization):
In terms of the course's structure, it was fine, but I struggled to understand the objectives of the course. I imagined it to be rather too elementary for, say, math majors. the group project did not seem to add much value to the learning and I thought it strange that we were given alternatives in both excel and radiant to perform the same functions and then abruptly told halfway through the course that only the most rudimentary functions from excel would be tested (referring to things that can easily be accomplished by a jc student on a gc) and radiant would not be tested at all.
Manageability of Workload:
The workload was unreasonably heavy for the amount of value it yielded. "Short" lecture videos added up to hours of viewing each week, especially if one bothered to follow along with the steps in the technical videos. Weekly tutorials were not necessary at all. I would say self-studying the lecture notes and videos (which were very well-written and well-made) would have been more than enough to grasp the content of the course.
Ease/Difficulty of Attaining Grades:
I think it's quite achievable as long as one makes sure to understand all concepts well and drills practice papers.
Learning Value/Recommendation:
Some of the content taught is useful, but as someone who already went through H2 math (and I only mention this because I believe most people have some sort of basic stats background) I don't think I learned a very significant amount, and certainly not enough to justify the time spent on it.
About the Instructor:
Prof. Da Yang was extremely nice and friendly, and seemed very enthusiastic about his field. With all due respect, however, I felt that the tutorials were not an effective use of time. They felt meandering with no clear objective, and took much more time than necessary for the actual amount of content covered.
AY 25/26, Sem 1
Instructor:
Dr Tan Da Yang
Content (Structure/Organization):
There is not much content to cover, but since there is a 3 hour lesson each week, the content is a little more spaced out and lessons are conducted at a slower pace for you to understand concepts. This can be good, but at times lessons were slow to the point of being boring.
About the Instructor:
Prof. Tan Da Yang carries a lot of energy when he teaches, especially to my 9am Monday morning class where no one has the willpower to participate. He is quite clear about the content taught, and since the lessons are 3 hours long, he is able to slow down and clarify doubts whenever necessary.
Manageability of Workload:
Quite light but picks up a little towards the end when you have to submit your group project and study for the finals. The content can be covered in two days as it is just 4 topics. 120 practice questions are also provided for you to study for the finals.
Ease/Difficulty of Attaining Grades:
The bellcurve is against you as you are competing against the entire NUS cohort of GEA students. This makes it such that you have to fight for every point. It is not difficult, but it takes effort.
Learning Value/Recommendation:
Helps you avoid being a victim of misleading data. Gives you basic knowledge to understand how studies are conducted and how you can make inferences based on the data and the setup of the study. You might not apply everything taught, but you will get to learn Simpson's Paradox and reference it in every scenario except for what it actually describes.
Content (Structure/Organization): 3
Too much focus on lectures and not enough practice.
Professor Conduct:
While they did generally teach with passion students had already read the lecture material beforehand so the repetitiveness lent itself to some boredom and disinterest. More focus on practice and correcting misconceptions during tutorials would be much more beneficial.
Manageability of Workload: 3
Just right, not too challenging and not too light.
Ease/Difficulty of Attaining Grades:
The content itself is not extremely challenging but the grade curve can be quite punishing, especially for NUSC students.
Additional Comments about the Instructor:
I liked the activities in tutorials and would like more of it rather than lectures on material that students should have already read.
Learning Value/Recommendation: 4
Nice introduction to statistics that could be useful for some courses later on.
Additional Comments/Word of Advice: Find practice wherever you can.
Instructor:
Professor Philip Johns
Content (Structure/Organization): 3
The topic and assessment are not that challenging, but more of tricky. It seems to be testing my reading comprehension rather than testing my concepts. The tutorial quizzes are rather different from what we learn in class, it feels off to learn from the quiz when quiz should be a check-in.
Professor Conduct:
I believe that Prof Johns did a pretty good job conducting the lessons.
Manageability of Workload: 4
Pretty low compared to NSW and NGN. The finals are challenging in the sense that I got tricked by the question rather than failing to understand the concept.
Ease/Difficulty of Attaining Grades:
I think it's really difficult to get an A in this course as when you make a minor mistake in the finals, it can drop your grade by a lot.
Additional Comments about the Instructor:
I like how the prof decide to disagree with the actual teaching content, it help me build a more insightful knowledge.
Learning Value/Recommendation: 3
There is some content that is useful, like learning to analyze experiments, but the content is quite dry and nothing challenging
Review by Jeremy Hor
Name: Dillon Poh Jiahe
Content (Structure/Organization): 4
Yes. No particularly hard topic.
Professor Conduct:
Yes the class was very engaging with many presentations.
Manageability of Workload: 4
Workload was quite tough, 3h a week with extra time spent outside of lessons.
Ease/Difficulty of Attaining Grades:
Okay.
Additional Comments about the Instructor:
NIL
Learning Value/Recommendation: 4
Very useful.
Instructor:
Professor Edmund Low
Content (Structure/Organization): 2
The course content is nothing to write about, mostly A-level stats plus 1 or 2 more new concepts. The questions are very long and convoluted, i think i did more reading than math. Some level of coding is required, but not too heavy, just need to understand basic operators. The material covered is the same as in GEA1000.
Professor Conduct:
He will try to explain the concepts more in-depth in tutorials, and revise concepts if anyone has questions.
Manageability of Workload: 5
Just 2x speed the lectures and you're good. The content is mostly a rehash of a level math anyway. Even the projects and quizzes don't require that much effort.
Ease/Difficulty of Attaining Grades:
Bell curve will be tough because of the low difficulty of this course, but if its any consolation you are curved against the wider NUS, not just NUSC.
Additional Comments about the Instructor:
The Professor will crack jokes occasionally to make learning more engaging.
Learning Value/Recommendation: 1
There is some content that is useful, like learning to analyze experiments, but the content is quite dry and nothing challenging
Review by Jeremy Hor
Instructor:
Dr Michelle Lee
Content (Structure/Organization): 3
Content was fairly well structured, but some of what was covered specific to NUSC seemed tangentially related to what was assessed and required in the end. I am unsure if it was part of the learning objectives at all, even though the segue way into R coding was interesting.
Accessibility and Assessment: -
Prof Michelle was engaging and effective. Content was clear and well explained, although she tended to move abit too fast during the posit cloud demonstrations and R coding.
Manageability of Workload: 4
Minimal assignments apart from short tutorials and the lectures+ quiz. The ICQs were manageable as long as I watched the technical videos or listened in class. The lecture quiz was the most time consuming component. In all I spent about 3h per week apart from the 3h contact time.
Ease/Difficulty of Attaining Grades:
A is achievable. I participated actively in tutorials, helped tank the project with a few others, did my tutorials, watched lectures and cleared most of the practice questions to prepare for the exam. The actual content is not difficult as it mostly a rehash of H2 Math.
Learning Value/Recommendation: 3
Useful and interesting, but not exactly novel. It was a good overview and introduction to statistics and data.
About the Instructor:
Prof Michelle was engaging and effective. Content was clear and well explained, although she tended to move abit too fast during the posit cloud demonstrations and r coding.
Additional Comments/Word of Advice:
GEA is a very doable module as long as you can sit through the 3h tutorials. An engaging prof like Prof Michelle makes it more bearable.
Instructor:
Dr Phillip John
Content (Structure/Organization): 2
Bad, the content is easy to find on Canvas and clearly segregated but some topics reappear in multiple chapters when they can be grouped together.
Accessibility and Assessment: -
My prof was just talking about his own stories with other classmates, little about content itself. This mod has pretty little content so I was okay with it.
Manageability of Workload: 5
Spent no time on weekly basis, but for final assignment quite some time is needed for discussion and analysis, because the lessons doesnt really help.
Ease/Difficulty of Attaining Grades:
Very achievable especially if you are science student as you are competing with art students.
Learning Value/Recommendation: 3
I've learnt it before! The fallacies were fun to learn.
About the Instructor:
Prof was very approachable but not very knowledgable on topic
Instructor:
Prof Edmund Low
Content (Structure/Organization): 3
The jumping between NUSC and non-NUSC stuff is quite jarring, not well linked, which renders it not well structured. It is only somewhat effective at achieving its learning objectives since we learn like all that theory but we dont absorb it which is difference (because it is boring most of the time we are zoning out). No its not challenging at all.
Accessibility and Assessment: -
He just rambles on and on and like it is boring. More room could be made for discussions. Key concepts were conveyed accessibly.
Manageability of Workload: 5
Very light, like maybe 1h per week max of prep time. No instructor specific assignments
Ease/Difficulty of Attaining Grades:
If one has A-Level math background, one could get A without studying. Not sure about how lenient prof is due to final exam though.
Learning Value/Recommendation: 1
Not useful for people with A-Level math background.
About the Instructor:
He is really really dry and monotonous and it makes me wanna fall asleep. I think more discussions are needed.
Additional Comments/Word of Advice:
its super easy
Name: Kailash (@kailashgautham)
Instructor:
Prof Chan Chi Wang
Content (Structure/Organization): -
The alternate week R sessions are kind of useless. That isn't the profs fault but rather the fault of the teaching team that decides the GEA curriculum. Even the content covered with the rest of the NUS is quite slow and boring. Prof does his best to make classes interesting and fun.
Accessibility and Assessment: 4
Manageability of Workload: 5
barely any workload. try to get a good team that doesn't slack off for the group project. Project takes a couple weeks tops if you have a good team. Class time is 3 hours a week. other than that there's an easy multiple attempts quiz every 2 weeks.
Ease/Difficulty of Attaining Grades:
Quite hard surprisingly, because the paper tends to be so easy that any mistakes you make can mess up your chance of getting an A and even one question wrong could drop you from an A to an A- or an A- to a B+. Prof grades quite leniently but fairly for the group project I think, which is quite nice.
Learning Value/Recommendation: 2
not much learning value. studied pretty much just to do well on the exam. did learn quite a lot while working on the project tho, and prof explains things related to the project very well.
About the Instructor:
Very knowledgable, explains well. not much he can do regd. how boring the content was. he tries his best to make it fun
Additional Comments/Word of Advice:
nothing much else. definitely try to get prof ccw, you won't regret it. he's very nice
Instructor:
Prof Loo Yoke Leng
Content (Structure/Organization): -
This module can be split into 4 portions: 1. Online lectures and quizzes 2. Odd week tutorials 3. Even week tutorials 4. Group Project. Online lectures and quizzes cover general GEA1000 content which comprises of 4 chapters and they are very manageable. Odd week tutorials also cover GEA1000 content. Even week tutorials cover additional non-examinable material that comprises of coding.
Accessibility and Assessment: 5
This mod is very accessible for a student with limited to no experience with statistics as the concepts converted are very basic and deal mainly with graphical presentation.
Manageability of Workload: 5
Generally you can expect to spend 1-2 hours on this module every week.
Ease/Difficulty of Attaining Grades:
Because most people have had prior knowledge with the content being taught, the bell curve is likely to be very steep and you have to be very careful with the online/ in-class quizzes and mid terms and finals. The median for my mid terms was an 8/10. Also, do try to put in consistent hours for the group project as I feel that that is honestly what will determine your grade for this mod because everyone’s score is going to be pretty similar for the other components
Learning Value/Recommendation: 2
Honestly, I don’t find that this mod has added much value to my learning because it is merely a repackaging of what is being taught in JC/ secondary school.
About the Instructor:
Prof is very patient, nurturing and approachable. She would offer us consults during recess/reading week or when it is near the project due date for us to clarify our doubts. Her classes are also well-paced and she tries her best to help us understand the content covered.
Additional Comments/Word of Advice:
Honestly I did not really enjoy this mod but Prof Loo made the experience a lot better. If you choose Prof Loo as your tutor, you’ll definitely be in good hands!
Instructor:
Prof Loo Yoke Leng
Content (Structure/Organization): -
Structure: biweekly online lecture videos (OTOT), with accompanying online quizzes and tutorials. The tutorial questions are then covered in odd-week tutorials, and even-week tutorials are used for additional (non-graded) content including coding in R and more practice of concepts covered in odd-week tutorials. There is also a group project, midterms and finals.
As the syllabus mainly follows the NUS-wide GEA1000, the module has a clear syllabus outline, though the content covered in even-week tutorials can vary slightly across classes.
Accessibility and Assessment: 3
The content covered are data collection methods and fundamental statistics concepts which should be quite basic for many, but for those without background, the explanation through the lecture videos might not be very well done.
As for the tools used for data analysis (Excel and Radiant), I think the technical videos are quite straightforward in showing how to navigate these tools.
Manageability of Workload: 5
This was definitely the least workload amongst my NUSC modules. The pre-tutorial work that only had to be done every 2 weeks (for odd-week tutorials) involved watching the lecture videos, doing the quizzes, and tutorial questions, which can be done in around 2h. No preparation is required for even-week tutorials.
The group project would require more time outside of class, but if the group puts in consistent effort throughout the second half of the sem, it would be much easier to manage as compared to doing the project days before the deadline.
Ease/Difficulty of attaining Grades:
We are graded against the entire NUS cohort that does GEA1000, hence the bellcurve is quite steep. However, I would say it is probably a more achievable A compared to most other NUSC modules. As long as you are meticulous during midterms and finals (the questions can be quite tricky), and put in effort for the group project, it is not difficult to get a decent grade.
Learning Value/Recommendation: 2
I find that the content of the module isn’t very useful due to the repetitive nature from pre-uni stats. The R programming part is very brief and hence not very useful either.
About the Instructor:
Prof Loo knows the subject well, hence she is able to clarify queries quite clearly. She also explains the important concepts again during tutorials, which is a great addition to the content from lecture videos.
She is a very nice prof who will organise extra consultation sessions if you need to clarify any doubts about the content, and overall she makes the tutorials more enjoyable. I would definitely recommend doing the mod under her.
Instructor:
Prof Edmund Low
Content (Structure/Organization): -
The structure is fine, except for the NUSC part in which case there is more structure in the distribution of matter in the universe than that
Accessibility and Assessment: -
Its as difficult as being thrown into the Bornean forest and being told to track down and kill a Bornean tiger with nothing but a stick as an intern at UBS.
Manageability of Workload:
not much tbh
Ease/Difficulty of Attaining Grades:
Rather achievable honestly
Learning Value/Recommendation:
it is less useful than a toy karambit knife made of playdough in a robbery
About the Instructor:
Drier than my mouth after being stuck in the Sahara for 35 days but he is passionate tbh just that there is a fundamental disconnect between his wishes and the wishes of his target audience and the fact that he really cannot teach
Additional Comments/Word of Advice:
To be very frank, the fact that it is every bloody week make pouring napalm on myself and setting myself on fire seem like a desirable way to go out.