ST3236 / MA3238 Stochastic Processes 1

Module Description
This module introduces the concept of modelling dependence and focuses on discrete-time Markov chains. Major topics: discrete-time Markov chains, examples of discrete-time Markov chains, classification of states, irreducibility, periodicity, first passage times, recurrence and transience, convergence theorems and stationary distributions

Lecture Topics
- Probability
- Random Variables
- Expectation
- Variance
- Conditional Expectations
- Applications of conditional expectation
- Introduction to Markov Chains
- Chapman-Kolmogorov Equations
- Classification of States
- Long time behaviours
- Time Reversibility
- Branching Processes

This will be your first exposure to Markov Chains. It is a very interesting topic and fun to play with.
If you are a statistics major, make sure you pick this module in semester 1. Semester 1 will be taught by statistics department and will not be that rigorous on the proofs. Semester 2 will be taught by mathematics department. More theoretical, more proofs.

Workload
Two Two-hours lecture
Lectures were taught by Dr David Chew. He is generally a good lecturer. Very helpful and patient lecturer.

One two-hours tutorial
Tutorials were held in the classrooms. The tutorials are generally not difficult. Like I said, MC is very interesting and it's a joy to solve the questions.

Assessment 
20% 2 online assignments
20% Term Test
60% Final Exam

The two online assignments are conducted on IVLE. Check your answers with friends.

Final paper will be a 2 hours closed book paper. You will be allowed to carry two pieces of cheatsheet.

Personal
I found the topics are very interesting and maybe that's why I was able to do better. I also found the questions in the final paper do-able. As long as you are careful in your calculation, it should be alright. Double check and make sure everything tallies. Have fun with the transition matrices. :)

Extra notes to readers: I have lecture notes, tutorials and past year papers. Download them from my dropbox.

ST2131 / MA2216 Probability

Module Description
The objective of this module is to give an elementary introduction to probability theory for science (including computing science, social sciences and management sciences) and engineering students with knowledge of elementary calculus. It will cover not only the mathematics of probability theory but will work through many diversified examples to illustrate the wide scope of applicability of probability. Topics covered are: counting methods, sample space and events, exioms of probability, conditional probability, independence, random variables, discrete and continuous distributions, joint and marginal distributions, conditional distribution, independence of random variables, expectation, conditional expectation, moment generating function, central limit theorem, the weak law of large numbers. This module is targeted at students who are interested in Statistics and are able to meet the prerequisite. It is an essential module for Industrial and Systems Engineering students.

Lecture Topic
- Sample Spaces, Probability and Combinations
- Axioms of Probability
- Binomial, Bernoulli, Poisson Random Variables
- Discrete Uniform, Geometric, Hypergeometric, Negative Binomial, Exponential, Gamma, Continuous Uniform and Normal Random Variables
- Distribution of a Function of Random Variables
- Jointly Distribution Function
- Sum of Independent Random Variables
- Multidimensional Change of Random Variables and Bivariate Normal
- Properties of Expectation
- Properties of Expectation, Conditional Expectation
- Moment Generating Functions, Inequalities
- Central Limit Theorem (CLT)

This module is the first core module for a year 2 statistics major. Workload is medium. Concepts are quite alright. Nothing too difficult.

Workload
Two two-hours lecture
My lecturer was a visiting Prof from Israel. He uses handwritten notes which were so messy. The whole module felt so disorganised. He uploads the lecture notes 5-10 minutes before the class which was at 8 am. The shop was not even open for us to print notes.

One two-hours tutorial
Tutorials were held in the computer labs. Most of them were quite do-able, at least to me.

Textbook is compulsory. The Prof expects you to read it before the class.

Lectures were webcasted.

Assessment
5% Online Quizzes
15% Written Assignments
20% 1.5 hrs Midterm (MCQ)
60% 2 hrs Finals

The online quizzes consist of probability questions that you have to complete on IVLE. You can only do it once. There will be practice question to let you know the structure of the questions.

Mid term was very easy. Consist of 20 MCQ questions. Most of them test you on P&C.

Personal
Most people did very well for mid term.

Final paper was very difficult.. It is easily the hardest paper I have taken out of the 4 years worth of exams. There were 6 questions and I couldn't answer any of them fully. It's quite insane. For the first time I felt so helpless and thought I would fail terribly. I passed with average results, so thankful for that.

This module is also taken with the Maths and ISE majors. Good luck with the bell curve.

Not much practice needed. No need to mad mug the textbook. It's either you get it or you don't.

Extra notes to readers: I have lecture notes, tutorials, past year papers, textbook inside my dropbox. Cheers!

MA1102R Calculus

Module Description
This is a course in single-variable calculus. We will introduce precise definitions of limit, continuity, the derivative and the Riemann integral. Students will be exposed to computational techniques and applications of differentiation and integration. This course concludes with an introduction to first order differential equations. Major topics: Functions, precise definitions of limit and continuity. Definition of the derivative, velocities and rates of change, Intermediate Value Theorem, differentiation formulas, chain rule, implicit differentiation, higher derivatives, the Mean Value Theorem, curve sketching. Definition of the Riemann integral, the Fundamental Theorem of Calculus. The elementary transcendental functions and their inverses. Techniques of integration: substitution, integration by parts, trigonometric substitutions, partial fractions. Computation of area, volume and arc length using definite integrals. First order differential equations: separable equations, homogeneous equations, integrating factors, linear first order equations, applications.

Lecture Topics
- Limits
- Continuous Function
- Derivatives
- Applications of Differentiation
- Integrals
- Transcendental Function
- Techniques of Integration
- Applications of Integration
- First Order Differential Functions

This is the first few modules you will take as a freshmen. Most likely you will be taking with MA1101R. This module is not say very difficult. The prof will slowly ease you into university life.

Workload
2 two hours lecture
I was taught by A/Prof Goh Say Song. Prof Goh is a very passionate lecturer. He is very patient and is easily one of the best lecturer you get in year 1. He uses transparency slides and OHP to teach the class.

One two-hours tutorial
Tutorials are hard. At least for me. Lots of work to do. I spent quite a lot of late nights doing those tutorials.

Supplementary 
Prof Goh will spam A LOT of extra questions and work for you to do...

Assessment 
15% 5 homework assignment
5% online laboratory assessment
20% 85 minutes Term Test
60% 2 hour Final Exam

Prof will provide a helpsheet for the final exam.

Personal
I did most of the tutorials and extra questions. The day before the papers I was pretty confident that I realised I don't have anything else to study. So i guess it really comes down to the day itself. Don't be too tensed. Be confident with your knowledge and go solve those annoying integration and differential equations.  :)

Work load for this module is quite heavy.

Extra notes to readers: I have lecture notes, tutorials, past year papers. You can download them from my dropbox.

ST4231 Computer Intensive Statistical Methods

Module Description

The availability of high-speed computation has led to the development of “modern” statistical methods which are implemented in the form of well-understood computer algorithms. This module introduces students to several computer intensive statistical methods and the topics include: empirical distribution and plug-in principle, general algorithm of bootstrap method, bootstrap estimates of standard deviation and bias, jack-knife method, bootstrap confidence intervals, the empirical likelihood for the mean and parameters defined by simple estimating function, Wilks theorem, and EL confidence intervals, missing data, EM algorithm, Markov Chain Monte Carlo methods. This module is targeted at students who are interested in Statistics and are able to meet the prerequisite.

Lecture Topics
- Review of Basic Probability and Statistics Terminology
- Random Numbers
- Generating Random Variates with Non-Uniform Distributions
- Variance Reduction Techniques
- Markov Chain Monte Carlo
- Bootstrap Methods
- Permutation Tests
- Optimization and Equation Solving

I found this module extremely interesting because it introduced us to a lot of useful algorithms. This is a compulsory core module for statistics major. If you intend to do honours, you will probably need some of the topics learnt in this module. For example, MCMC, Gibbs sampling, bootstrapping, simulation, etc


Workload
Three-hours lecture
Lectures were taught by Dr Wu Zhengxiao. The lecturers changes almost(?) every semester so I'm not sure if this is still relevant to this module. However Dr Wu is alright. We can understand him find and he teaches well.

One two-hours tutorial
Tutorials were held after the 2nd lecture. Tutorials were quite tough for my semester because there are quite a bit of coding. The questions are non-trivial and I often had to spend a lot of them on them. We used R for all the coding.

Textbook is not compulsory. Dr Wu used Computational Statistics, 2nd Edition by Geof H. Givens and Jennifer A. Hoeting.

Lectures were not webcasted.

Assessment (AY11/12 Sem 2)
20% Homework/participation/tutorial
20% Midterm
60% Finals

Dr Wu would ask someone to come out and present one question for every tutorial. So it is a good idea to do the tutorial beforehand.

1 single A4 cheat sheet is allowed for midterm. The midterm wasn't too difficult. He tested on everything in the lecture notes. I made a mistake for the question about changing variables.

2 A4 cheatsheet are allowed for finals. Final paper was significantly more difficult. Most of the questions were about writing pseudo codes.

ST4233 Linear Models

Module Description

Linear statistical models are used to study the way a response variable depends on an unknown, linear combination of explanatory and/or classification variables. This module focuses on the theory of linear models and possible topics include: linear regression model, general linear model, prediction problems, sensitivity analysis, analysis of incomplete data, robust regression, multiple comparisons, introduction to generalised linear models, nonlinear regression. This module is targeted at students who are interested in Statistics and are able to meet the prerequisite.

Lecture Topics
- Matrix and Vector
- Random Vector and Matrix
- Multivariate Normal Distribution
- DF of Quadratic Forms in y
- Simple Linear Regression
- Multiple Regression: Estimation
- Multiple Regression

Core module for statistics major.

Workload
1.5 hours lecture every week
Lectures were taught by Prof Zhou Wang.

One hour tutorial
Tutorials were held after the lecture.

Lectures were not webcasted.

Assessment (AY11/12 Sem 2)
30% Assignment
70% Finals

The assignment consisted of 7-8 questions to be completed in 2 weeks.

Two pieces of cheatsheet is allowed for the final paper. Most of the chapters were tested.

Personal
Waiting for results

This module is like a continuation of ST3131 except that we will learn to handle everything in matrix. 2 out of 6 questions were on proofs.

SSA2215 Biophysical Environment of Singapore

Module Description
The module will focus on the functions of the biophysical environment of the city state of Singapore. The topics include geology, soils, river systems, water supply, natural reserves, green areas, land reclamation and coastal environments. The environmental problems that arise from the development of a large tropical city within a limited area, and the possible solutions for such problems will be examined. The module does not require an extensive science or mathematics background.

Lecture Topics
- Terrestrial Environment: Origin of rocks and minerals, plate tectonics and the rock cycle 
- Terrestrial Environment: Geological evolution of Singapore 
- Terrestrial Environment: Singapore topography 
- Terrestrial Environment: Singapore soils, slope modifications and soil erosion 
- Terrestrial Environment: Singapore climate and weather 
- Marine environment: Singapore tides, sea level and climate changes: past, present and future 
- Transitional Environment: Singapore coasts & beaches . 
- Singapore Inter-tidal Environment: Mangrove 
- Singapore Sub-tidal Environment: Sea grass and coral reef habitat  
- Terrestrial Environment: Singapore forests, parks, green areas and the Garden City 
- Terrestrial Environment: Streams, rivers, swamps and lakes 
- Singapore natural resources: sun, sea, sand and land; landfill, rock and water; alternative power: wind, waves, solar, geothermal 

Tutorial Topics
- Rocks and minerals of Singapore 
- Maps of Singapore II: geology, topography, soil, built environment/landfill changes (past and present), vegetation (past and present)
- Self-paced fieldwork on Kent Ridge Campus: rocks, slopes, soils, vegetation of Kent Ridge
- Singapore land use change and urbanization  

Workload
One two-hours lecture
Lectures were taught by Dr Grahame Oliver and Dr Daniel Friess. They take turns to teach the lectures. Dr Oliver is the funny one. Dr Daniel is the handsome one that makes the girls go ahh and wah. 

4 - 5 1 hour practical
My tutorials were taught by the lecturers. We learned to identify minerals in the rocks, and look at Singapore maps. We also walked around NUS to complete some tasks for one of the tutorial.

Textbook is compulsory.

Lectures were not webcasted.

Assessment
4 10% CA (Total 40%) 
Mid-semester test (10 %)
Finals (50 %)

The mid term is 30 mins at end of lecture 7, consisting of multi choice and fill in the blanks types of questions. It is closed book.

The CA tests are given at the end of the tutorials.

Personal
I didn't bother to study this module so results were not so good. I didn't expect this module to be so memory intensive. If I wanted to memorise hard facts and regurgitate them, I wouldn't be in statistics.

Extra notes to readers: I have lecture notes and tutorials. Download them from my Dropbox.

GEK1508/PC1325 Einstein's Universe and Quantum Weirdness

Module Description
This module will give a gentle introduction to two of the most important developments in modern physics: relativity and quantum theory. It would cover topics such as: the concept of absolute and relative space and time, the twin paradox, black holes and wormholes, wave-particle duality of matter, Heisenberg's uncertainty principle, Schrodinger's cat, the ultimate constituents of matter, grand unification and superstrings, and how these theories can contribute to the technology of tomorrow. It is designed for non-physics students, and proceeds mainly by analogy and contrast with the familiar. Concepts will be emphasised, while omitting the technical details.

Lecture Topics
- Einstein Special Relativity
- Einstein General Relativity
- Star Nomenclature
- Black Holes
- Classical Mechanics to Quantum Mechanics
- End of Determinism: Rise of Probabilistic Mother Nature
- What is Real?
- The standard model
- Lepton
- Antimatter

This module is mind blowing.

Workload
Two two-hours lecture
Lectures were taught by Prof Phil Chan. He is VERY PASSIONATE. Easily the best lecturer of the semester.

5 Odd or even week one-hour tutorial
Tutorial will NOT last more than 45 minutes. Do remember to sign attendance. The tutor will go through problems during tutorial. There will be assignments at the last 10-15 minutes of the tutorial. Don't be too worried about them. It's not too difficult.

Textbook is not compulsory. I don't think you need it as well because the lecture notes are really sufficient.

Lectures were webcasted.

Assessment 
5% Attendance
15% In-Class Assignment
5% IVLE discussion
5% Star Gazing
20% MCQ Test 1
20% MCQ Test 2
30% Term Paper

Term Paper is a group project of 2-3 students. We had to either write a story using relativistic or quantum ideas or a critical group review. So those of you who can write very well can choose to write a story.

5% star gazing is an interesting component of this module. You can either go on a short weekend astronomy trip to Punggai / Johor or the NUS football field.

MCQ Test 1 includes Einstein's Relativity. MCQ Test 2 will include the second half of the module. Prof will ask you to take it easy and have fun during the paper. Nevertheless the papers were still mind blowing. This whole module is mind blowing.

Sorry I do not have any past year papers

PL2131 RESEARCH AND STATISTICAL METHODS I

Module Description
This module is aimed at equipping students with the critical thinking and analytical skills necessary as a foundation for evaluating or carrying out empirical research in psychology. It is an essential module for psychology major students. It consists of two sections: the first deals with the design of psychological research; the second covers basic descriptive and inferential statistical techniques. Students will be taught how to design their own empirical study, to carry out appropriate statistical analyses on the data collected so as to draw valid conclusions, and how to write up their findings. Ethical aspects of psychological research are covered.

Lecture Topics
1) Introduction
2) Scientific Methods I: Non-experimental Design
3) Scientific Methods II: Experimental Design
4) Descriptive and Inferential Statistics
5) Hypothesis Testing
6) Effect Size and Statistical Power
7) Sampling Distribution and Means
8) One Sample and Dependent Sample t-test
9) Independent Sample t-test
10) Correlation and Simple Regression
11) Chi-Square Tests
12) Research Ethics and Report Writing

Good module to take if you are good in statistics. This is a core module for psychology students. I feel that the department could have split the arts students and the rest of NUS students into two separate module (like DSC1007 and DSC1007X). I think it is very unfair that they have to compete like this with the rest.

Workload
Two two-hours lecture
Lectures were taught by Prof Mike Cheung. He is very passionate in his teaching. Clear and easy to understand. He will also share jokes at the end of the class.

4 tutorial sessions in the semester
My tutorials were taught by some postgrad student. You will be learning a lot of PSPP. It is a free alternative to SPSS.

Textbook is compulsory. I bought it and found it quite helpful

Lectures were not webcasted.

Assessment (AY12/13 Sem 1)
20% Homework Assignment
30% Closed book mid-term MCQ test
50% Open book final exam

2 homework assignment 10% each. Very easy. Involves using PSPP to deal with the data and find solutions to some questions.

You can find past year papers in the NUS library if i'm not wrong.

Following are the instructions and tips provided by the Prof:
- Open book. Textbooks and other written materials allowed.
- 2 main questions with several parts
- Penalties if answers are irrelevant.
- Remember to bring calculator

Extra notes to readers: I have textbook. Download it from my Dropbox.

DSC1007 / DSC1007X Business Analytics – Models and Decisions

Module Description
This course prepares students with fundamental theory and basic instruments to capture
business insights from data and thus make good managerial decisions. Quantitative
models and tools such as Decision Analysis, Simulation Modeling and Mathematical
Optimization are covered to demonstrate the use of scientific methods in business
decision making. Practical examples and cases with rich data are used to stimulate
students’ interest and understanding in Business Analytics.

Lecture Topics
1) Laws of Probability, Bayes Theorem, Covariance
2) Discrete Probability Distributions
3) Continuous Probability Distributions
4) Normal Distribution and the Central Limit Theorem
5) Decision Tree Model and Analysis
6) General Method Decision Analysis
7) Random Number Generators
8) Using the Sample Data for Analysis
9) Computer Software for Simulation Modeling
10) Formulating Management Problems
10a) Linear Optimization Model
10b) Nonlinear Optimization Model
10c) Discrete Optimization Model
11) Computer Software for Optimization Modeling

I took DSC1007x which is open to all NUS student. DSC1007 is only for Biz students. Since this is known to be a module of a quantitative nature, you can expect to find more people from Maths, Stats, Engineering than other majors.

Workload
One two-hours lecture
Lectures were taught by Prof Wang Tong. He is very experienced in teaching the topics in this module. He is also very clear and easy to understand.

Weekly one-hour tutorial
My tutorials were taught by a PhD student.

Textbook is not compulsory. I didn't buy the textbook because it is so expensive. Buy if you think u have the time to read and do the practices.

Lectures were not webcasted. But there are past year webcasts for you to view.

Assessment (AY12/13 Sem 1)
10% Class Discussion
10 % Assignment
10 % Project Report
70 % Final Exam

The assignment/tutorial is group work. Each team has to submit one entry. The first half of the tutorials are pretty easy if you have basic statistics background. It gets harder at the back when you start to do optimisation.

Project is also group work. One entry. The group has to come up with a hypothetical business problem where you have multiple choices and a decision to make.

Personal
Awaiting results

Extra notes to readers: I have lecture notes and tutorials. I will not be giving out my group project. Download them from my Dropbox.

CS1010 Programming Methodology

Module Description
This module introduces the fundamental concepts of problem solving by computing and programming using an imperative programming language. It is the first and foremost introductory course to computing.  It is also the first part of a three-part series on introductory programming and problem solving by computing, which also includes CS1020 and CS2010. Topics covered include problem solving by computing, writing pseudo-codes, basic problem formulation and problem solving, program development, coding, testing and debugging, fundamental programming constructs (variables, types, expressions, assignments, functions, control structures, etc.), fundamental data structures: arrays, strings and structures, simple file processing, and basic recursion.

Lecture Topics
The latest lecture updates are all here.

Tutorial Topics
All here as well

This module was my first ever encounter to programming but it is also my all-time favourite module. This is a core module for statistics majors to prepare us for programming in other languages and statistical softwares.
You will be using C programming language throughout the module.
The workload is heavy.

Workload
Three-hours lecture
Lectures were taught by Dr Zhou Lifeng. The lecturers changes almost(?) every semester so I'm not sure if this is still relevant to this module. However Dr Zhou teaches very well and replies to email quickly. He is also very patient and approachable so it was easy to ask him questions. I voted him as the best lecturer in the lecturer feedback.

One two-hours tutorial
Tutorials were held in the computer labs. Most of them were quite do-able, at least to me.

Supplementary 
Dr Zhou put up a hell lot of supplementary questions. You can access them here. He releases them bit by bit as the lecture progresses, so you might not see the full list at the start of the semester. There's really no obligation to do any of these.

Take-home lab assignments
There will be 1 trial lab that is ungraded. Subsequently there will be 5 more graded labs. Difficult is moderate(?).

Practical Exams
There were two practical test during my semester. They are quite do-able as well.. just got to think and type fast.

Lectures were not webcasted.

Assessment (AY11/12 Sem 2)
5% Take-home lab assignments
5% Discussion Session Attendance
10% + 25% 2 Practical Exams
15% Term Test
40% Final Exam

Final Exam is open-book. The lecturer will not be out to kill anyone with this paper. There will be easy questions but there will also be a few tougher ones to differentiate the students.

There is also no bell curve for this module. So you will get your A if you really deserve it. This is fair because everyone has different level of programming background.

Personal
Well I was hoping to get an A but i kinda of screwed up my midterms so I knew it wasn't possible anymore. I got like <50% of the total marks for mid terms for some reason. I think I got very high marks for both PE. I also left 1 question almost blank during finals due to the lack of time.

It is very important to have the right attitude for this module. Before I started this module, I was actually very afraid of programming and tried my best to avoid it. I had zero programming experience and I thought I was going to die in this module. But as you start to do more questions, you will slowly gain confidence and hopefully even more motivation due to the satisfaction felt after solving every question :)

I finished all the supplementary question (and I encourage you to do so as well) because those questions were really interesting and challenging. As long as you love problem solving and you are reasonably good at it, I think you will be fine. Keep up with the lecture and discussion too.

Extra notes to readers: I have lecture notes, tutorials, my own supplement questions codes, my own cheatsheets for exams. You can view them here (if they are still relevant) :)

GEM2901 Reporting Statistics in the Media

Module Description
Statistical thinking will one day be as necessary for efficient citizenship as the ability to read and write' (H.G. Wells). In the Information Age every educated person is surrounded by statistical information of all kinds. This information comes frequently through the media from governmental, scientific and commercial worlds. This module, using a minimum of mathematical or statistical prerequisites, aims to make the student statistically literate in reading and understanding such information. The course will be based on real world case studies of issues of current importance and relevance. The students' objectives in this course are as follows: (1) Students will learn to read, critically analyze, write about and present reports about all types of quantitative information. (2) Students will learn the strengths and weaknesses of using quantitative information in different circumstances. (3) Students will study a number of case studies of current interest. They will be able to compare and contrast the statistical treatments from different sources.

Topics
- The Benefits and Risks of Using Statistics
- Reading the News
- Measurements, Mistakes, and Misunderstandings
- How to Get a Good Sample
- Experiments and Observational Studies
- Getting the Big Picture
- Summarising and Displaying Data
- Relationships between Measurement Variables
- Relationships Can Be Deceiving
- Relationships Between Categorical Variables
- Understanding Probability and Long-Term Expectations
- Psychological Influences on Personal Probability
- When Intuition Differs from Relative Frequency

Well honestly, I didn't feel there were much of an advantage to take this module as a statistics major. This module teaches you to think critically about any studies you may come across in the news in a systematic manner. There are very little statistics or mathematics in this module.

Workload
Two two-hours lectures 
This module was taught by Prof Chua Tin Chiu. The lectures will be held either very early in the morning (8am) or late in the evening (6pm). Very often, Prof Chua would digress and start talking about some story. They were kind of entertaining lah. I didn't really attend the lectures because most of them were pretty self-explanatory. You just need to copy down the "tutorial" answers at the end of every lecture.

No tutorials
Prof Chua will cover a few questions pertaining to the lecture topic at the end of the lecture. You just need to understand and copy down the answers.

Textbook is not recommended. 
Well the textbook is stated as compulsory but I only used it for the projects. So I do not think it is worth the cash to buy the textbook.

Lectures were not webcasted.

Assessment
20% Project 1
20% Project 2
60% Final

There is no mid term for this module. However there are two projects which are quite time consuming. These two projects are to train you to use the techniques taught to analyse newspaper articles.

The final paper includes about 30 MCQ questions and a few open ended questions. The 30 MCQ are quite manageable. The open ended questions will be similar to the "tutorial" questions covered at the end of every lecture. However the open ended questions can be ambiguous and hard to answer.

Personal
This module is very manageable because the contents are easy. There's nothing much to memorise too. You just need to be very clear of the concepts and you should be fine.

Extra notes to readers: I have the TB and project descriptions! I won't be uploading my project though! Download them from my Dropbox.

GEK1520 / PC1322 Understanding the Universe

Black Hole :P

Module Description
This module presents an introduction to the universe, the scientific methods for observing the universe, and evolution of ideas of the universe. The topics covered are planets, stars, galaxies and cosmology. This module is targeted at all interested students with a willingness to be exposed to new ideas.

Lecture Topics
- Naked-eye Objects
- Telescopes
- Solar System
- Milky Way Galaxy
- The Universe

Tutorial Topics
- Naked-eye Observations
- Telescopes
- Radiation & Spectroscopy
- Stellar Properties
- Relativity, Black Holes & Cosmology

Good module to take if you are open-minded enough and interested in knowing more about the lecture topics. If you are not, steer clear of this module because some may find the contents hard to memorise.

Workload
Two two-hours lecture
Lectures were taught by Dr. Cindy Ng. Her methods of delivery are clear and concise. I could tell she is very passionate about this topic and not many can claim that his/her hobby is star-gazing.

Odd or even week one-hour tutorial
My tutorials were taught by Mr. Jeremy Chong. I think the tutor changes every semester, so it is irrelevant. Tutorials are very easy, requiring very basic mathematics. Arts students should not be afraid to take this module :)

Textbook is not compulsory. I don't think you need it as well because the lecture notes are really sufficient.

Lectures were not webcasted.

Assessment (AY12/13 Sem 1)
15% tutorials
5% Observatory Tutorial
2 Term Tests, each 20%
40% Term Paper (Project)

15% tutorials are easy to get, just make sure you mark your attendances and not make any mistakes in those assignments. Just make sure you double check with your friends. Topics are not difficult to understand.

5% observatory tutorial requires you to visit one of the physics buildings where they store their telescope. The 5% comes from this written assignment that has to be handed in on the spot. No worries, the tutors will tell you all the answers. (lol.)

Term Test 1 is like this module's midterm. It will cover the first half of the syllabus, consisting of MCQ, fill-in-the-blank and short answer questions. It is closed book, so you are required to memorise. Any calculations are omitted.

Term Test 2 will be held right before the semester ends (Week 12/13). It will cover ALL of the syllabus and consist of MCQ and calculation questions. However, this is open book. The trick here is to organise your notes in a neat fashion and turn to them as fast as possible. There should be at least a few questions from each set of lecture notes.

Term Paper is a group project where you have to submit both the experiment and individual report. Project may be about anything as long as it is related to this module. My team borrowed a lux meter to measure the intensity of the light radiating from the light bulb. This is not time consuming if you know what you are doing (half day for experiment, 1-2 days for report)

Extra notes to readers: I have lecture notes and tutorials. I will not give out my group project but I can give you my individual report. Download them from my Dropbox.
If you want to know more about the modules I have taken, you may refer to this list.

LAB1201 Bahasa Indonesia I

Module Description
This module aims to develop language proficiency in an integrated approach. Students will acquire language skills through participation in various communicative tasks. Through the exposure to the language, students will develop a general understanding of the cultures, the sociolinguistic and pragmatic aspects of the language. By the end of the module, students will acquire basic skills of speaking, listening, reading, and writing to maintain communication on common topics.

Topics
- Pronoun
- The usage of -nya
- Sentence structure and noun phrase
- Negation
- The usage of ada, punya, adalah
- Question Words
- Prepositions
- Adverbs
- Verbs

Workload
One Lecture Weekly, 2 hours.
This lecture focuses on the usage of grammar in Bahasa Indonesia. Don’t forget to bring your grammar textbook every lesson.

Taught by Ibu Johanna. She delivers her lessons in a suitable pace and defines every single indonesian words clearly. Lecture starts promptly, so don’t be late for lesson.

Classes are conducted in an interactive manner – students are pair up to practice speaking and writing. Lecturers are approachable and friendly, so if you are in doubt, always clarify. That’s the best way to learn a langauge. Not only you will benefit, but other people in the class will also benefit as well.

Two tutorials weekly
Tutorial A – 2 hours/ Tutorial B – 1 hour
Both tutorials focus more on oral communication skills in bahasa indonesia. It has an introductory blue textbook designed to maximise student engagement in communicative tasks, supporting successful language learning.

Class size is small and it’s the best place to make new friends. Most of the class time is devoted to pair-work or group-work with students interacting primarily with each other.

Taught by Ibu Novi for both tutorials. She is well-liked by the students and delivers her lessons in an interactive way. Furthermore, she will make an effort to remember all of her students’ name.

No webcasts.

Textbooks are compulsory.
- Grammar Book (Tata Bahasa Indonesia)
- Two binded blue Bahasa Indonesia book

Assessment
15% Homework Assignments
40% 2 Written Tests
15% Project
15% Oral
15% Attendance and Participation

Project:
There is a cultural night which serves the purpose of allowing LAB1201 students to understand more about the rich culture of Indonesia. Students will have to sign up for one of the traditional groups such as Angklung, Menyanyi, Jumputan or Tari Saman. Students are required to attend few hours of practices outside scheduled curriculum time and perform during the cultural night.

Oral:
The oral is conducted in pairs and consists of 2 parts.
For the first part, students are given a piece of paper describing a scenario and given 10 minutes to construct a dialogue based on it.
For the second part of the assessment, student can either do picture conversation or narration. For the narration, the student will be given something to talk about.
Oral should last around 15-20 minutes.

Personal
Homework Assignments
There are 5 take-home essays which are graded. If you have friends who took bahasa indonesia before, ask him/her to check and correct your essay. Try to score for this component, since this module requires consistent good grades across all components to get an A.

The Two Written Tests
Same thing, if you have friends who took Bahasa Indonesia before, ask him/her for the test paper. The format of the paper is usually around the same.
The killer part is the listening comprehension in test 2. I am quite confident that they have allocated significant marks in this section, If you did well for listening comprehension, the chances of you scoring in test 2 is high. The average for the two tests is around 75.
Score above 80 for both papers and you will increase your chance in getting at least an A-.

Project
A very easy component to score. Attend all practices and ensure that the answers in your submitted quiz are correct (you will be given a quiz to do)

Attendance and Participation
A very easy component to score as well. Attend all lessons and ensure that you participate actively in class. If you have participated enough, your lecturer will remember your name.

Oral
Practice conversing with your partners on the topics and scenarios given in class. I suffered during my oral because I didn’t practice enough. Don’t be nervous and always remember that the two invigilators are there to assist you.

Final note: Take with friends who are interested in learning a new language. Though competition is stiff, if you truly enjoy taking this module, you should be able to achieve a decent grade. Also, you will get to meet new friends from across faculties, so you won’t regret taking this module.

Extra notes to readers: If you want to know more about the modules I have taken, you may refer to this list or the labels to the right! :D

(Credit to Yuhuai)

ST3244 Demographic Methods

Module Description
This course will provide an introduction to the fundamental principles and methods of demography. The role of demographic data in describing the health status of a population, spotting trend and making projection will be highlighted. Topics include sources and interpretation of demographic data, rates, proportions and ratios, standardization, complete and abridged life tables, estimation and projection of fertility, mortality and migration, interrelations among demographic variables, population dynamics and demographic models. This module is targeted at students who are interested in Statistics and are able to meet the prerequisites.

Topics
- Basic Concepts and Measures: Population dynamics, demographic rates, person-years (PY) and population growth
- Age-specific Rates
- Life Table
- Multiple Decrement Life Tables, Associated Single-Decrement Life Table
- Fertility and Reproduction
- Population Projections
- Stable Populations
- Global Burden of Disease

This is a great module to take if you are truly interested in demography. It gives you insights into how population projections are done, how TFR is calculated, etc. However, if you are not truly interested, there are better modules out there for you to expand your knowledge.

Workload
Two lectures (2nd lecture is only 45 minutes)
I was taught by Dr Leontine Alkema. Her delivery may not be all that clear because she tends to beat around the bush and sometimes she appears to be unsure of what she is saying. There were a few slides containing a few life tables and she wasn't able to clarify the doubts in the life tables, causing a huge crowd in front after that lecture. She told us she will clarify the doubts during the next lecture, but that is something that I would say to my H2 Mathematics tutee when I am not sure what is going on.

She is very cheerful and interactive, often asking for answers from the lecture group.

It is notable that she also does not reply to emails on the day before the exams. I was not one of those students who sent her emails but some of my peers did and they weren't too happy about that.

On the last week, she introduced us to a new topic but she said that she was not sure if she wanted to add that topic to the final paper. However, to my knowledge, the final paper has to be submitted to the office many weeks before the actual exam date. So how could she be unsure of that?

Weekly one-hour tutorials
Tutorials require the use of R and excel. You will be guided by the lecturer so no worries about the computational part (lecturer's comments and partial codes). Tutorials are not that easy. It is very important to understand the concepts.

No webcasts.

Textbook is recommended.
It is helpful to read the textbook if the lecturer went on too fast or if you have additional doubts.

Assessment
25% Midterm
50% Final
20% Project
5% Attendance and Participation
Cheat sheets will be provided.

For project, you will work in pairs or alone. The project will ask you to use R to do some population projection and calculate some values like total number of females in 2100. It is not too difficult, just a tad too time-consuming. Project should be around the same for every year.

Personal
I am pretty satisfied with this result because I did badly for midterms (Below average). I did okay for the project so I guess it must be the final exam that pulled me up.

In general, the two papers are not very difficult as long as you understand the concept and read the question properly. It is very important to read the question properly because it is usually lengthy for some reason.

It is very important to know the concepts. Just keep in mind that the midterm and final paper will test you on everything. On top of that, each main topic is not really related. Thus you can save time on figuring out which part of the cheat sheet to use.

Extra notes to readers: I have my project R codes and script. If you are interested, please leave a comment and check back often. I will get back to you as soon as possible. :D
If you want to know more about the modules I have taken, you may refer to this list or the labels to the right! :D

ST3241 Categorical Data Analysis

Module Description
This module introduces methods for analysing response data that are categorical, rather than continuous. Topics include: categorical response data and contingency tables, loglinear and logit models, Poisson regression, framework of generalised linear models, model diagnostics, ordinal data. This module is targeted at students who are interested in Statistics and are able to meet the prerequisite.

Topics
- Two-way Contingency Tables
- Three-way Contingency Tables
- Generalized Linear Models
- Logistic Regression
- Loglinear Models
- Multicategory Logit Models

I found this to be a very informative and helpful module that adds on to your knowledge from ST3131.

Workload
Two two-hours lectures
I was taught by A/P Zhang Jin-Ting. It took awhile for the class to get used to his accent and the way he pronounces certain words like "saturated". All in all, he is a good lecturer, always keen to answer questions and replies very fast to emails.

Lecture was held in the new LT at Stephen Riady. This is also one of the lousiest LT I had ever visited. There's very little space to move around, air-con doesn't work sometimes and it is pretty dirty considering it is so new.

Weekly one-hour tutorial
The tutorials were not very difficult. Usage of R to do some tests and data modelling will be required. These will be taught during lecture.

Textbook is recommended.
As always, it is always better to revise together with the textbook. However, the lecture notes are quite sufficient for revision. So this is up to you.

Lectures were webcasted.

Assessment
60% Final (True and False, Open-ended)
30% Midterm (True and False, Open-ended)
10% Class Participation (Presenting answers for a few questions)
Cheat sheets allowed.

Extra notes to readers: If you want to know more about the modules I have taken, you may refer to this list or the labels on the right! :D

ST2132 Mathematical Statistics

Module Description
This module introduces students to the theoretical underpinnings of statistical methodology and concentrates on inferential procedures within the framework of parametric models. Topics include: random sample and statistics, method of moments, maximum likelihood estimate, Fisher information, sufficiency and completeness, consistency and unbiasedness, sampling distributions, x2-, t- and F- distributions, confidence intervals, exact and asymptotic pivotal method, concepts of hypothesis testing, likelihood ratio test, Neyman-Pearson lemma. This module is targeted at students who are interested in Statistics and are able to meet the pre-requisite.

Topics
- Normal Distribution and some related distribution
- Survey Sampling
- Parameter Estimation
- The Fisher Information
- Large Sample Theory for MLE
- Efficiency
- Sufficiency
- Hypothesis Testing
- Generalized Likelihood Ratio Test
- Comparing Two Samples

Workload
Two two-hours lectures 
This module was taught by Dr Lim Chinghway. He was clear and concise in his delivery. On top of that, he would actually ask us if he was going too fast or we needed him to repeat. At least for me, I encountered very few lecturers who actually does that so I really appreciated that. Recommended to take this module if he is teaching :D

Weekly one-hour tutorials
Difficulty was progressive (as usual). Starting off with simpler questions to ease us towards the harder and more insightful questions.

Textbook is recommended. 
The textbook is nice to have if you have trouble following the lectures or you don't want to watch the webcast.

Lectures were webcasted.

Assessment
50% Final Exam
20% Midterm Exam
5% Tutorial Attendance
5% Tutorial Presentation (Need to present answers for 1-2 questions)
20% Tutorial Submission
5% (Bonus for forum participation)

Extra notes to readers: If you want to know more about the modules I have taken, you may refer to this list or the labels to the right! :D

GEK1527 / LSM1302 Genes and Society

Module Description
The primary aim of this module is to introduce students to the modern concepts in biology and to enable them to evaluate independently the potential benefits and risk of the biotechnological revolution and its implications for society. The topics taught will cover an understanding of the nature of the genetic material and the passage of information from DNA to protein, as well as the major technologies currently used for genetic engineering. Examples of genetic engineering of micro-organisms, plants and animals will be given in detail, and cloning will also be discussed. The student will become aware of the impact of the various genome projects on their own lives and will also able to recognise difficulties associated with deciding which kinds of genetic engineering are ethically and morally acceptable.

Topics
- Overview: The Past and Present
- Classical Genetics: Genes, Inheritance and Us
- DNA to Proteins: The Central Dogma of Molecular Biology
- Mutation: When the Message Changes
- In Different Operating Systems
- From Detecting and Amplifying DNA to Crime Scene Investigation
- From Cloning and Reading Genes to Health-Risk Assessment
- From Omics and Informatics to Drug Discovery
- Genetic Engineering in Society: Microorganisms, Plants, Animals and Human

H2 biology is not part of the pre-requisite but if you have, you will stand an edge. Most of the topics are not that difficult if you have H2 biology.

Workload
Two two-hours lecture
Lectures were held in LT 32, the lousiest LT in NUS so far due to the dark (thus sleep-inducing) lighting and location. Each semester will be taught by different lecturer and the CA component will be different as well. I was taught by Dr Lam Siew Hong, one of the most passionate lecturer I have ever met. He was highly animated and would share about his works on the Zebrafish. (Despite his interesting lectures, I still fell asleep most of the time)

No tutorials!

Assessment
60% Final (All MCQ)
5% Online Surveys
15% Short Questions
10% Mini Project (I made a brochure on Huntington's Disease)
10% Short Essay (On a certain topic)

I didn't spend a lot of time on each of those CA components. They are usually done within half a day or less if you know what to look for. (Again, basic biology background would be helpful here)

Extra notes to readers: I have important past year papers which helped me A LOT for my final paper. Download them from my Dropbox. (Warning: It's a huge pdf file)
If you want to know more about the module I have taken, you may refer to this list.

ST2137 Computer Aided Data Analysis

Module Description
This module introduces students to the statistical computer packages, with main focus on SAS, Splus and SPSS, that provide the computational tools for performing statistical data analysis using the methodology covered in the prerequisite modules. Topics include data access, transformations, estimation, testing hypotheses, ANOVA, performing resampling methods and simulations. It also equips students with basic computational techniques for maximum likelihood estimation. This module is targeted at students who are interested in Statistics and are able to meet the prerequisite.

Contrary to what was written in the module description, I was taught R instead of Splus. I believe that is the right change because R is already more popular.

Topics
- Introduction to R
- Introduction to SAS
- Introduction to SPSS
- Describing Numerical Data
- Robust Statistics for Location and Scale Parameters
- Analyzing Categorical Data
- One Sample Tests and Two Sample Tests
- One-way Analysis of Variance
- Simulation: An Introduction
- Simulation Studies in Statistics
- Resampling: Bootstrap Method
- Numerical Methods in R
- Regression Analysis

This is an extremely informative module in my honest opinion. As a statistics major, it is always good to know a few statistical softwares especially R and SPSS.

Core for statistics major.

Workload
Two two-hours lecture
I was taught by Dr David Chew. In general, I think he is alright at teaching but he is pretty boring. He provides different versions of the lecture notes (slide or pdf) for you to print out.

Weekly one-hour tutorial
All tutorials try to familiarise you with the usage of R, SAS and SPSS. Each tutorial will require you to head down to the computer laboratories because SAS and SPSS are not free for students to download (R is free). This will be annoying for students who are not staying in campus. Tutorials are not difficult but may be time-consuming.

Textbooks are not needed
Lecture notes are sufficient. Textbooks are available for R, SPSS and SAS but they are all pretty expensive. No real need to buy.

Lectures were not webcasted

Assessment (AY12/13 Sem 1)
20% Project (Consist of 2 parts, may form groups of 7-8. It is advisable to split the part 1 and 2 to different people to save time)
10% Laboratory Test (Not very difficult as long as you study. All softwares might tested but only 1 will be tested (random))
20% Midterm (Open-ended)
50% Final (Open-ended)
Cheat sheets allowed

Extra notes for readers: I have the lecture notes, sample paper and my project. I won't give out my project but I don't mind helping anyone check their project. If anyone is interested, please leave a comment and check back often. I will get back to you as soon as I can :D
If you want to know more about the modules I have taken, you may refer to this list or the labels on the right! :D

ST3131 Regression Analysis

Module Description
This module focuses on data analysis using multiple regression models. Topics include simple linear regression, multiple regression, model building and regression diagnostics. One and two factor analysis of variance, analysis of covariance, linear model as special case of generalized linear model. This module is targeted at students who are interested in Statistics and are able to meet the pre-requisites.

This is one of the most important core modules for a statistics major. Take it as soon as you can because it is the pre-requisite for many level 3000 modules. :)

Topics
- Introduction to regression
- Simple linear regression
- Multiple linear regression
- Introduction to generalized linear models

You will need to have basic knowledge of linear algebra and statistics for this module. The lecturer will use R during lecture as well as in tutorial, so you will need to remember ST2137.

Workload
Two two-hours lecture
This module was covered by Dr Yu Tao during my semester. Lecture notes are relatively straightforward but there are some difficult concepts. Dr Yu would go through a lot of proofs on the contents but I feel that these are mostly for understanding purposes. I can understand Dr Yu fairly well despite his chinese accent.

It was covered by Dr Chan Yiu Man in semester two of AY12/13. He introduced a project in this semester. I did not take ST3131 under him so I did not have to do the project.

Weekly one-hour tutorial
As usual, we have tutorials every single week. Homework are due every two weeks, so it's between doing them diligently or copying them from your friend. Every homework submission is counted to your CA. However, incomplete homework will be penalised.
Some of the tutorial questions require the use of R.

Textbook is recommended
Although it is not compulsory, I felt that the textbook helped a lot in clarifying my concepts.

Lectures were not webcasted

Assessment
10% Homework (Homework submission fortnightly)
10% Class Participation (Presentation of tutorial answers)
30% Midterm (True and false as well as open-ended)
50% Final (True and false as well as open-ended)
Cheatsheets are allowed for both midterm and final papers.

Extra notes to readers: I have the lecture notes. You can get all the files from my dropbox. Cheers!

MA1101R Linear Algebra 1

Module Description
This module is a first course in linear algebra. Fundamental concepts of linear algebra will be introduced and investigated in the context of the Euclidean spaces R^n. Proofs of results will be presented in the concrete setting. Students are expected to acquire computational facitilies and geometric intuition with regard to vectors and matrices. Some applications will be presented. Major topics: Systems of linear equations, matrices, determinants, Euclidean spaces, linear combinations and linear span, subspaces, linear independence, bases and dimension, rank of a matrix, inner products, eigenvalues and eigenvectors, diagonalization, linear transformations between Euclidean spaces, applications.

Topics

- Linear Systems and Gaussian Elimination

- Matrices

- Vector Spaces
- Vector Spaces Associated with Matrices

- Orthogonality

- Diagonalization

- Linear Transformation

This is actually one of the most important modules to take if you are a mathematics (higher level linear algebra mods), statistics (linear regression, etc) or computer science student. Please study this module as well as you can.

First few chapters will be quite simple, but it gets abstract very fast from then on. Beware!

Workload

Two two-hours lecture

This module is covered by different lecturers every semester. I took it under Dr Ng Kah Loon in semester one. Usually this module will be taught in a huge LT like LT27 due to the number of students.  I suggest sitting nearer to the centre because it is hard to hear the lecturer with so many people chit-chatting at the back. Dr Ng is pretty persistent when it comes to keeping the LT quiet, so he will refuse to start the class if it is still too noisy.

Lectures are webcasted. 

This is usually the case when the number of students taking a certain module is high enough. You will most likely need the webcast whether you attend the lectures or not because this module has a high learning curve. You will probably not catch everything during the lecture.

Book is compulsory. 

This book was written by Dr Ng Kah Loon himself and two other people. You can literally use it as your lecture notes because the slides will follow extremely close to it. I don't think it's a must to buy the newest edition. 

Weekly one-hour tutorial

Tutorials are taken directly from the textbook (another reason to get the book). Tutorials are not easy so prepare to spend more time on it. :/

Two or three lab sessions? (I am not too sure of the lab frequencies but it is not a lot)

You will learn basic Matlab. There is a lab component in CA so try to learn this well.

Assessment (AY11/12 Sem 1)

10% Lab (Using Matlab to solve certain questions.)

20% Mid terms (Open-ended) 

70% Finals (Open-ended)

Personal

I obtained 27/40 for mid terms which is considered above average since the mean is like 23-24 i think. I think I didn't do well enough for finals.

If you pay enough attention and keep up with the lecture notes and tutorials, this module should not be too difficult. In finals, most of the questions are considered do-able except for a few proving questions.

This module will appear intimating at first but please do not give up.  

Extra notes to readers: I have past year papers and lecture notes. If anyone is interested, download the files from my Dropbox.

If you want to know more about the modules I have taken, you may refer to this list.