AY15/16
IE2101 Introduction to Systems Thinking
IE2110 Operations Research I
CS1020E Data Structures and Algorithms
ES2331 Communicating Engineering
IE3131 Engineering Statistics
Any tips or personal opinions presented are based only and only on my personal experience.**
Took another 2 ISE modules this semester. Also with CS1020E, completed all the set of foundation modules for ISE.
IE2101 Introduction to Systems Thinking
In this module we are exposed to systems thinking. It basically means trying to making a model to describe a real life system. Systems can be anything from Healthcare systems, transport systems, business supply chains to something smaller, basically anything.
Basic modelling involves first drawing simple causal loop diagrams and stock flow diagrams, then trying to assign quantitative values to variables in the models. Afterwards testing the model and see if its a good representation, otherwise being a iterative process, repeat the process again.
We get to choose to create a model for any system we like. There are many interesting projects chosen and I have no idea how everyone goes about building their models. It feels like another project where things are done mostly for show and there isn't much learning going on.
Basic modelling involves first drawing simple causal loop diagrams and stock flow diagrams, then trying to assign quantitative values to variables in the models. Afterwards testing the model and see if its a good representation, otherwise being a iterative process, repeat the process again.
We get to choose to create a model for any system we like. There are many interesting projects chosen and I have no idea how everyone goes about building their models. It feels like another project where things are done mostly for show and there isn't much learning going on.
A very light and easy module (but may be hard to score - as with any ISE module), and where the project might take up a huge portion.
IE2110 Operations Research I
Introduction to Linear Programming and Optimization. Gives you a glance at OR and you could decide if it is your interest. The focus is on more problem solving and there is little focus on mathematical understanding.
This module introduces us to linear programming, through a study of the famous simplex algorithm for solving linear constraint optimization problems. At the end of the course, some non-linear programming topics are introduced as well. The approach taken is rather application or problem solving based, and the focus is to teach how to solve problems.
This module also introduces algorithms to solve certain network problems. Taking Cs1020E concurrently can be helpful as you can also look at the optimization problems from a computer science perspective, and have a sense of how the algorithms can be possibly implemented.
The algorithms are not difficult and it is simple to apply the algorithms taught as they are similar to step by step instructions. Trying to understand and learn the math and theory behind the topics and algorithms is hard. However, it is not required and unnecessary to score, since the module only asks for problem solving.
Topics:
Simplex Algorithm
Duality
Network Models and algorithms
Convex Programming
Lagrange Multipliers and KKT
CS1020E Data Structures and Algorithms
Part two of the programming course. In this course, there will be a switch to C++. This should not be a concern if you have done programming in C as they are rather similar. This course is essential in introducing various programming concepts. Knowledge of these topics are really important to continue with programming. With what is learnt from this course, you can develop confidence and ability to self learn various topics that are of interests. Personally I do not think that lectures added too much value other than content, but it was much easier to follow the lectures rather than learn through reading.
Most of the learning still comes from practice as it helps to cement the concepts. Heavy focus on implementation initially, but later on there will be exposure to abstraction and less focus on implementation at the most basic level and more on algorithms. Personally had a bad time with the topics before mid term, as they are very detail oriented and requires some strong coding skills to deal with OOP and the implementation details of linked lists. In the later topics the focus is less on programming and more on design and analysis of algorithms and the data structures.
Topics:
Programming in C++
OOP
Array and List
Stack and Queue
Hashing
Sorting Algorithms
ES2331 Communicating Engineering
English Module catered to engineering. Provide some opportunity to develop your communication skills further, not too useful other than the opportunity to practice. Don't really have a choice here if you are an engineering student :) Get over and be done with it.
Got an average B+ for this module.
IE3101 Engineering Statistics
Topics: Review of Statistics and Probability
ANOVA
Regression
Design of Experiments
Not Covered: Statistical Process Control (Chapter 8)
The module follows the textbook Engineering Statistics by Montgomery closely. The first part was a review of everything you have learnt in 1131 and 2131. It gets interesting in the second part when the three main topics are covered. Prof Goh provides very useful interpretation on the materials. From the textbook, it describes very well how the statistical techniques taught are applied to engineering. There is little mathematical detail involved and the focus is on understanding and making correct statistical inferences and probably the only course where all the examples used are engineering examples. Allows you to appreciate the some of the tools taught.
The module and topics are presented in a high level overview style. Many topics are covered and it can be really difficult to appreciate all the concept taught without any personal experience or opportunity for hands on application. It leaves you with alot more knowledge on statistical applications but does not build up your statistical foundations. Provides a good gauge of whether you have interest in statistics.
Recommended to go for lectures especially for second part. Read the textbook as much as possible and look up things which you do not really understand as the book gives a quick introduction to too many topics without being able to explain in depth.
Finally I got B. Exam was surprisingly difficult. I thought I had done ample preparation but I found it very difficult to answer the questions. There was little calculation involved and the focus is heavily on applications to engineering and on explaining some of the concepts clearly.
Overview of Semester
This semester exposed me to some higher level technical modules, which made me realize areas which I have had to learn more, especially probability and statistics and linear algebra. I am also becoming more interested in studying technical modules which I had previously thought I would not enjoy.
This module introduces us to linear programming, through a study of the famous simplex algorithm for solving linear constraint optimization problems. At the end of the course, some non-linear programming topics are introduced as well. The approach taken is rather application or problem solving based, and the focus is to teach how to solve problems.
This module also introduces algorithms to solve certain network problems. Taking Cs1020E concurrently can be helpful as you can also look at the optimization problems from a computer science perspective, and have a sense of how the algorithms can be possibly implemented.
The algorithms are not difficult and it is simple to apply the algorithms taught as they are similar to step by step instructions. Trying to understand and learn the math and theory behind the topics and algorithms is hard. However, it is not required and unnecessary to score, since the module only asks for problem solving.
Simplex Algorithm
Duality
Network Models and algorithms
Convex Programming
Lagrange Multipliers and KKT
CS1020E Data Structures and Algorithms
Part two of the programming course. In this course, there will be a switch to C++. This should not be a concern if you have done programming in C as they are rather similar. This course is essential in introducing various programming concepts. Knowledge of these topics are really important to continue with programming. With what is learnt from this course, you can develop confidence and ability to self learn various topics that are of interests. Personally I do not think that lectures added too much value other than content, but it was much easier to follow the lectures rather than learn through reading.
Most of the learning still comes from practice as it helps to cement the concepts. Heavy focus on implementation initially, but later on there will be exposure to abstraction and less focus on implementation at the most basic level and more on algorithms. Personally had a bad time with the topics before mid term, as they are very detail oriented and requires some strong coding skills to deal with OOP and the implementation details of linked lists. In the later topics the focus is less on programming and more on design and analysis of algorithms and the data structures.
Topics:
Programming in C++
OOP
Array and List
Stack and Queue
Hashing
Sorting Algorithms
ES2331 Communicating Engineering
English Module catered to engineering. Provide some opportunity to develop your communication skills further, not too useful other than the opportunity to practice. Don't really have a choice here if you are an engineering student :) Get over and be done with it.
Got an average B+ for this module.
IE3101 Engineering Statistics
Topics: Review of Statistics and Probability
ANOVA
Regression
Design of Experiments
Not Covered: Statistical Process Control (Chapter 8)
The module follows the textbook Engineering Statistics by Montgomery closely. The first part was a review of everything you have learnt in 1131 and 2131. It gets interesting in the second part when the three main topics are covered. Prof Goh provides very useful interpretation on the materials. From the textbook, it describes very well how the statistical techniques taught are applied to engineering. There is little mathematical detail involved and the focus is on understanding and making correct statistical inferences and probably the only course where all the examples used are engineering examples. Allows you to appreciate the some of the tools taught.
The module and topics are presented in a high level overview style. Many topics are covered and it can be really difficult to appreciate all the concept taught without any personal experience or opportunity for hands on application. It leaves you with alot more knowledge on statistical applications but does not build up your statistical foundations. Provides a good gauge of whether you have interest in statistics.
Recommended to go for lectures especially for second part. Read the textbook as much as possible and look up things which you do not really understand as the book gives a quick introduction to too many topics without being able to explain in depth.
Finally I got B. Exam was surprisingly difficult. I thought I had done ample preparation but I found it very difficult to answer the questions. There was little calculation involved and the focus is heavily on applications to engineering and on explaining some of the concepts clearly.
Overview of Semester
This semester exposed me to some higher level technical modules, which made me realize areas which I have had to learn more, especially probability and statistics and linear algebra. I am also becoming more interested in studying technical modules which I had previously thought I would not enjoy.
