Year1-Sem1
ST1131 | Introduction to Statistics | A- |
MA1101R | Linear Algebra I | A |
MA1102R | Calculus | A |
EC1301 | Principles of Economics | A |
GEK1508 | Einstein's Universe and Quantum Weirdness | A |
Year1-Sem2
CS1010 | Programming Methodology | A- |
ST2131 | Probability | B |
MA2108 | Mathematical Analysis I | B+ |
EC2102 | Macroeconomic Analysis I | A |
PC1322 | Understanding the Universe | A+ |
Year2-Sem1
ST2132 | Mathematical Statistics | A+ |
ST2137 | Computer Aided Data Analysis | A+ |
EC2101 | Microeconomics Analysis I | A |
LSM1302 | Gene and Society | A+ |
GEK2503 | Remote Sensing for Earth Observation | A- |
Year2-Sem2
ST3131 | Regression Analysis | A |
ST3236 | Stochastic Processes I | B+ |
ST3239 | Survey Methodology | A- |
EC3312 | Game theory & its application in economics | A |
EC3333 | Financial Economics I | A+ |
Year3-Sem1
ST3233 | Applied Time Series Analysis | A |
ST3246 | Statistical Models for Actuarial Science | A |
EC3361 | Labor Economics I | A+ |
EC3383 | Environmental Economics | A- |
GEM2901 | Reporting Statistics in Media | S/U |
Year3-Sem2
ST3247 | Simulation | A |
ST4240 | Data Mining | A- |
EC3304 | Econometrics II | A+ |
EC3101 | Macroeconomic Analysis II | A+ |
SSS1207 | Natural Heritage of Singapore | S/U |
MC | 120 |
CAP | 4.768 |
EC3101
Extension to EC2101. Lay the foundation for many other lvl3000 modules. But as someone who has taken many other lvl3000 EC mod before this, most of the materials taught were actually a toned down version of what I have learned before. Despite that, I still did not do well for my midterm due to my carelessness. Luckily, I managed to save myself during the finals. Both midterms and finals has its fair share of tricky questions.
SNG Tuan Hwee is a decent lecturer and is able to explain concepts clearly. But for some reason, I get really confused by the way he phrased his some of his tutorial/homework questions.
Midterm mean-median: 54.8-55.5/75
EC3304
An extremely important and useful module for all economics majors. Workload is extremely light with only midterm, finals and 1 tutorial every fortnight. There are also tutorial participation points where you need to present your solution at least once for the semester. Although the workload is light, the content is not easy, and as someone who has taken a module on time-series from the stats department before, I think the time-series portion of this module is a total mess. The content on time-series is very flimsy and it does not help when Eric FESSELMEYER rushes through it.
Midterm mean-median: 65.6-67(/100)
ST3247
Simulation is a new module taught by a new lecturer. Vik is extremely dedicated and you can easily tell the large amount of effort he puts in to prepare his lectures and tutorials. Clear explanation of concepts and very well defined learning outcome. The only thing I can complain about is that he spent a little bit too much time dwelling on the basics during the start of the semester.
Content includes generating random variables from various distributions, Monte Carlo methods, and discrete event simulation such as queuing models. While the module may requires a lot of R, especially for discrete event simulation, they will not be tested in the midterms or finals. Emphasis was placed on understanding the algorithms, and perhaps a little bit of memorization was needed. This module will also lay foundation for ST4231.
The workload of this module is not heavy. 5 graded assignments, midterm and finals. The midterm is easy, with questions taken directly from tutorials, assignments and sample paper. Finals is slightly more challenging but manageable.
Midterm mean-median: 76.3-82.5(/100)
ST4240
Data-mining, Big Data, Data analytics are the buzz-word in recent years. Everybody wants a slice of it, even in NUS. The CS and stats are already offering it, the biz has recently started offering it, and last I heard, even the math department want to join the party. Although Big Data Analytics is raging hot in the United States. The market is still at its infancy stage in Asia, but nonetheless, it is steadily picking up its pace (I just read on newspaper today that LTA is tabbing on the power of big data to improve our transport system) and I believe there is huge potential in this field. The demand for data scientist (NOT data analyst) will be strong, but unfortunately, most data scientist roles need at least a master or PhD
The content is rather heavy for this module as Prof Xia Yingcun tried to cramp in a lot of data-mining analytics techniques into the module and the math behind these techniques are also not easy to understand. However, I think the module placed too much emphasis on the theory part and lack hands-on component for us to practice analyzing data using these data-mining techniques. Workload is light with only midterm and finals. Both exams are manageable.
Midterm mean: ~80 /100
SSS1207
Extremely popular module as it is one of the few SS mod that have an open book MCQ midterm and finals. Bell curve is extremely steep with half the cohort scoring above 25 out of 30 for the midterm. If you want to do well in this module, you need to be extremely familiar with the textbook, otherwise, you might find yourself flipping through the textbook in frustration wondering where the hell do I find those answers. There are a few guest lectures which, in my opinion, is totally useless for the exams, so there is no harm skipping them. The online discussions/tutorials and field trips are also optional.
Midterm mean-median: 24.26-25(/30)
If you would like to download the materials such as lecture notes or tutorials for any of the modules that I have taken before, you may access them through this link: NUS Modules.
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