We often receive questions about the required programming experience. We do no require or expect programming experience for our programme. We offer two programming modules in the first semester. The first, Programming in Python, is an introductory module if you have little or no programming experience.
In this module, you will learn the basics of Python programming with an emphasis on the tools you will need in the programme. For those who have programming experience, we offer Topics in Scientific Computing. This will cover more advanced topics for students who have experience in programming. At the beginning of the semester, a meeting with your academic advisor will help you decide.
If you have some programming experience but are perhaps out of practice (and you would like to take the more advanced module), reviewing and practicing Python is a good idea. In addition to programming topics such as: data structures (lists, dictionaries, etc.), different types of loops, functions, it is recommended you review and practice with libraries such as NumPy and SciPy.
Tutorials for Python and NumPy are available online.
The other major question is with respect to mathematical prerequisite knowledge. The three main areas where prior knowledge is expected is:
More detailed topics in each of these areas is listed below. If you have not seen this material before or it has been some time since you have seen it - it is recommended you go over some of these topics. We include three course notes which approximately cover the required material. The course notes contain additional material (particularly for calculus. In the breakdown below, we list which chapters are relevant. Finally, we include three freely available online resources which may be helpful.
A brief summary of the topics more in detail are as follows:
Much of this will be reviewed in Probability and Statistics for Data Analytics, but it will be helpful to review it. The provided course notes are helpful.
Download course notes - Probability and Statistics Notes [PDF 394KB]
One should known how to perform the following computations and be familiar with matrix notation. The provided lecture notes cover the required material (with some additional topics).
Download course notes - Linear Algebra Notes [PDF 601KB]
This is the topic which often has the most questions. Calculus is a large subject with many topics. There are many standard textbooks available such as Calculus by Thomas. The provided course notes cover the required material and much more. The main topics you will need for Data Analytics involve computing derivatives (and occasionally an integral). The majority of the material for the following topics is in Chapters 2 and the beginning of Chapter 3.
Download course notes - Calculus Notes [PDF 5,653KB]