AY2014/2015, Semester 1
DSC3215 Stochastic Models in Management
I didn't know why I took this module, it was interesting but studying for it throughout the semester was a chore. Prof Zhang is really helpful and knowledgeable but his accent is out of the world, thus most of us students have to always listen a few times before we get what he is trying to say. This module covers most of the important theory in decision science in stochastic format, which means it is absolutely stats-intensive. The first part mainly review simple statistic results like Bayes Rule and all the distribution. The main highlight of this module is the extensive use of the equivalence between poisson arrival frequency and exponential interarrival time, which I did not realize until reading week. The main chunk of the module is all about queueing models which we model using Discrete Markov Chains in the first half and using Continuous Markov Chains in the second half, both based on the concept of transition matrices. Also covered in the module towards the end is Decision Theory using dynamic programming, which is quite abstract if you do no have advanced mathematical background in dynamic optimization. All in all, it is a very specialized and applicable brance of decision science, but the mathematics and statistics involve is pretty demanding. Thankfully, we only need to know how to apply the models but not derived them, which makes the memorizing the formulaes an option if all the equations in the notes look incomprehensible.
Grading Criteria: 10% Seminar Participation, 30% Midterm Test, 60% Final Exam.
Grade: S (Finally invoked the power of the S/U option, originally a B-)
EC3322 Industrial Organization I
This module is an extension of applications encountered in EC3101, and is very mathematical in midterm test and final exam. Content of the module is quite straightforward, rederiving perfect competitions, dominant firm with fringe model, monopolies with horizontal and vertical differentiation in greater detail. Oligopoly interactions are examined using Game Theory, and this includes the standard Cournot, Bertrand and Stackelberg competitions. The last part of the module introduces cartels and their stability, as well as product differentiation under competition. From Prof Fesselmeyer, I have also obtained some of the topics which we did not have time to covered this semester, such as mergers and networks. Empirical studies are covered broadly throughout the whole semester but they are not tested in the final exam. Having read both EC3101 and EC3312 last year, this module appears to be relatively intuitive as concepts are heavily drawn from EC3101 and EC3312. Final note, Prof Fesselmeyer does NOT allow the use of calculators for this module. So be mentally prepared, literally.
Grading Criteria: 10% Tutorial Participation, 40% Midterm Test, 50% Final Exam.
Grade: A
EC3332 Money & Banking I
The first course on money and banking introduces both the microeconomics and macroeconomics of the financial system. The banking lectures follow the microeconomic interactions between depositors and banks, examining the sources of inefficiency in the market that arises from asymmetric information, as well as the regulations in place to tackle these issues. The money lectures focus on the variety of monetary tools available to the central bank and the mechanisms of such policies. Unconventional monetary policies such as quantitative easing was also briefly discussed alongside the conventional monetary tools. Finally the course concludes with analysis of monetary response to shocks in the DAD/DAS framework. Overall, the module is more qualitative than quantitative in nature, but there is still great emphasis on graphical analysis. Hence, it is always advantageous to know the mathematical relationship between the variables to prevent confusion when graphing out shocks and responses.
Grading Criteria: 20% Tutorial Participation, 30% Midterm Test, 50% Final Exam.
Grade: B+
EC4102 Macroeconomic Analysis III
My favourite module of the semester. Unlike EC2102 and EC3102 that placed greater emphasis on graphical representations, this module is very quantitative so be prepared for pages of algebra alongside more complicated phase diagrams. For this semester, there is also a qualitative course project which focus on economic intuitions, thus bringing balance to the study of advanced macroeconomics within this course. Throughout the course, advanced mathematical models constructed based on microeconomic interactions between representative agents served as the framework of analysis for economic growth. The first half of the course models real economic growth using capital, labour and technology while the second half of the course examines the growth path of the economy in the presence of random technology shocks, nominal shocks and expectations. Overall, this module is interesting for me as it knowledge from previous macroeconomic courses are reexamined in a detailed manner, presenting a clearer picture of how the economy functions.
Grading Criteria: 10% Tutorial Participation, 20% Group Project, 30% Midterm Test, 40% Final Exam.
Grade: A
MA3264 Mathematical Modelling
This modules covers many topics from optimization to differential systems to probability models. The topics this semester is the same as previous years up to probability models, and subsequently Prof Liu decided to cover game theory and graph theory instead of mathematical physics. Most topics covered are introductory, focusing on applications rather than delving into the mathematical analysis. One of the easier MA-modules I have encountered thus far.
Grading Criteria: 5% Tutorial Participation, 10% Group Project, 35% Assignments, 50% Final Exam.
Grade: A
ST3131 Regression Analysis
This module was daunting at first glance, but after with some effort it is quite manageable. The course covers topics such as ANOVA, ANCOVA alongside different variations of linear regression. Previous knowledge of confidence intervals, prediction intervals hypothesis testing is required, so I am quite disadvantaged in this aspect because ST2131 did not cover those topics for my semester. Assignments required the use of R programming, but prior knowledge is not necessary. The problems for this module can be very non-intuitive and tedious, and the final exam for this semester was very difficult. Lastly, practice is necessary but not sufficient to do well for this module because of the steep bell curve, every mark counts.
Grading Criteria: 20% Assignments, 20% Midterm Test, 60% Final Exam.
Grade: B+
Ending Note
SAP of 4.62 after exercising the S/U option for DSC3215, which exceeded my expectations. CAP went up to 3.87, one step closer to second uppers. Thankful for everything this semester despite the hectic schedule, juggling SUAD, EHDP work and everything else ranging from social to academic aspects. Three more semester to go and I'm going to do my best for everything I chose to commit for this academic year and next. This ending note shall be short because, Prague, HERE I COME!
I didn't know why I took this module, it was interesting but studying for it throughout the semester was a chore. Prof Zhang is really helpful and knowledgeable but his accent is out of the world, thus most of us students have to always listen a few times before we get what he is trying to say. This module covers most of the important theory in decision science in stochastic format, which means it is absolutely stats-intensive. The first part mainly review simple statistic results like Bayes Rule and all the distribution. The main highlight of this module is the extensive use of the equivalence between poisson arrival frequency and exponential interarrival time, which I did not realize until reading week. The main chunk of the module is all about queueing models which we model using Discrete Markov Chains in the first half and using Continuous Markov Chains in the second half, both based on the concept of transition matrices. Also covered in the module towards the end is Decision Theory using dynamic programming, which is quite abstract if you do no have advanced mathematical background in dynamic optimization. All in all, it is a very specialized and applicable brance of decision science, but the mathematics and statistics involve is pretty demanding. Thankfully, we only need to know how to apply the models but not derived them, which makes the memorizing the formulaes an option if all the equations in the notes look incomprehensible.
Grading Criteria: 10% Seminar Participation, 30% Midterm Test, 60% Final Exam.
Grade: S (Finally invoked the power of the S/U option, originally a B-)
EC3322 Industrial Organization I
This module is an extension of applications encountered in EC3101, and is very mathematical in midterm test and final exam. Content of the module is quite straightforward, rederiving perfect competitions, dominant firm with fringe model, monopolies with horizontal and vertical differentiation in greater detail. Oligopoly interactions are examined using Game Theory, and this includes the standard Cournot, Bertrand and Stackelberg competitions. The last part of the module introduces cartels and their stability, as well as product differentiation under competition. From Prof Fesselmeyer, I have also obtained some of the topics which we did not have time to covered this semester, such as mergers and networks. Empirical studies are covered broadly throughout the whole semester but they are not tested in the final exam. Having read both EC3101 and EC3312 last year, this module appears to be relatively intuitive as concepts are heavily drawn from EC3101 and EC3312. Final note, Prof Fesselmeyer does NOT allow the use of calculators for this module. So be mentally prepared, literally.
Grading Criteria: 10% Tutorial Participation, 40% Midterm Test, 50% Final Exam.
Grade: A
EC3332 Money & Banking I
The first course on money and banking introduces both the microeconomics and macroeconomics of the financial system. The banking lectures follow the microeconomic interactions between depositors and banks, examining the sources of inefficiency in the market that arises from asymmetric information, as well as the regulations in place to tackle these issues. The money lectures focus on the variety of monetary tools available to the central bank and the mechanisms of such policies. Unconventional monetary policies such as quantitative easing was also briefly discussed alongside the conventional monetary tools. Finally the course concludes with analysis of monetary response to shocks in the DAD/DAS framework. Overall, the module is more qualitative than quantitative in nature, but there is still great emphasis on graphical analysis. Hence, it is always advantageous to know the mathematical relationship between the variables to prevent confusion when graphing out shocks and responses.
Grading Criteria: 20% Tutorial Participation, 30% Midterm Test, 50% Final Exam.
Grade: B+
EC4102 Macroeconomic Analysis III
My favourite module of the semester. Unlike EC2102 and EC3102 that placed greater emphasis on graphical representations, this module is very quantitative so be prepared for pages of algebra alongside more complicated phase diagrams. For this semester, there is also a qualitative course project which focus on economic intuitions, thus bringing balance to the study of advanced macroeconomics within this course. Throughout the course, advanced mathematical models constructed based on microeconomic interactions between representative agents served as the framework of analysis for economic growth. The first half of the course models real economic growth using capital, labour and technology while the second half of the course examines the growth path of the economy in the presence of random technology shocks, nominal shocks and expectations. Overall, this module is interesting for me as it knowledge from previous macroeconomic courses are reexamined in a detailed manner, presenting a clearer picture of how the economy functions.
Grading Criteria: 10% Tutorial Participation, 20% Group Project, 30% Midterm Test, 40% Final Exam.
Grade: A
MA3264 Mathematical Modelling
This modules covers many topics from optimization to differential systems to probability models. The topics this semester is the same as previous years up to probability models, and subsequently Prof Liu decided to cover game theory and graph theory instead of mathematical physics. Most topics covered are introductory, focusing on applications rather than delving into the mathematical analysis. One of the easier MA-modules I have encountered thus far.
Grading Criteria: 5% Tutorial Participation, 10% Group Project, 35% Assignments, 50% Final Exam.
Grade: A
ST3131 Regression Analysis
This module was daunting at first glance, but after with some effort it is quite manageable. The course covers topics such as ANOVA, ANCOVA alongside different variations of linear regression. Previous knowledge of confidence intervals, prediction intervals hypothesis testing is required, so I am quite disadvantaged in this aspect because ST2131 did not cover those topics for my semester. Assignments required the use of R programming, but prior knowledge is not necessary. The problems for this module can be very non-intuitive and tedious, and the final exam for this semester was very difficult. Lastly, practice is necessary but not sufficient to do well for this module because of the steep bell curve, every mark counts.
Grading Criteria: 20% Assignments, 20% Midterm Test, 60% Final Exam.
Grade: B+
Ending Note
SAP of 4.62 after exercising the S/U option for DSC3215, which exceeded my expectations. CAP went up to 3.87, one step closer to second uppers. Thankful for everything this semester despite the hectic schedule, juggling SUAD, EHDP work and everything else ranging from social to academic aspects. Three more semester to go and I'm going to do my best for everything I chose to commit for this academic year and next. This ending note shall be short because, Prague, HERE I COME!