Tuesday, 3 June 2014

Academic Update AY2013/14 Sem 02

Workload is generally light for this semester, nothing much to complaint about. Was kind of surprised by my A- in SSS1207 as I thought I did badly for the finals, but I s/u-ed it anyway. I also participated in a R competition somewhere in march which subsequently earned me an internship with Revolution Analytics.

ST1131 Introduction to Statistics A-
MA1101R Linear Algebra I A
MA1102R Calculus A
EC1301Principles of Economics A
GEK1508Einstein's Universe and Quantum WeirdnessA
CS1010 Programming Methodology A-
ST2131 Probability B
MA2108 Mathematical Analysis I B+
EC2102Macroeconomic Analysis I A
PC1322Understanding the UniverseA+

ST2132Mathematical StatisticsA+
ST2137 Computer Aided Data Analysis A+
EC2101Microeconomics Analysis I A
LSM1302Gene and SocietyA+
GEK2503Remote Sensing for Earth ObservationA-
ST3131Regression AnalysisA
ST3236 Stochastic Processes IB+
ST3239 Survey Methodology A-
EC3312Game theory & its application in economicsA
EC3333Financial Economics IA+

ST3233Applied Time Series Analysis A
ST3246 Statistical Models for Actuarial ScienceA
EC3361Labor Economics IA+
EC3383Environmental EconomicsA-
GEM2901Reporting Statistics in MediaS/U

ST4240 Data MiningA-
EC3304Econometrics IIA+
EC3101Macroeconomic Analysis II A+
SSS1207Natural Heritage of SingaporeS/U


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

 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)

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)

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

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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