The purpose of these courses is to prepare students for the Core Courses that will follow. Since not all students accepted into the program will have an undergraduate education in mathematics and statistics, the Foundations Courses will introduce them to the essential elements of Mathematics, Statistical theory and Finance. For students who do join the program with some undergraduate training, the Foundations Courses will help to review concepts that they may have forgotten.
Foundation courses will cover four subjects:
1) Mathematics
In this course students will learn:
- Linear algebra,
- calculus,
- graphs,
- Optimisation,
- numerical methods.
Learning outcomes
- Familiarize students with undergraduate to postgraduate mathematics
- Learn how mathematics is useful in analytics.
- Ability to solve mathematical problems
- Preparedness for the Core Course
2) Statistics
Data Scientists work with data drawn from the real world, and students are required to become proficient with the basic tools of data analysis. In this course students will learn:
- What a random variable is, and how to describe it using the concept of probability
- How to work with common probability distribution functions such as the Binomial, Poisson and Normal distributions
- How to compute the expected value and variance of a random variable
- How to work with jointly distributed random variables
- How to formulate a statistical hypothesis, and how to test it
- How to compute confidence intervals
- Markov Chain methods.
Learning outcomes
The purpose of this course is:
- to familiarize students with undergrad to post graduate statistics.
- Applicationsof statistics into business and data analytics, economics and finance, and variance fields of studies.
3) Programming
- Principles of programming,
- variables and data structure,
- functions and modules,
- I/O file,
- handling large projects.
Learning outcomes
The purpose of this course is:
- to familiarize students with imperative programming style.
- To construct algorithms and then program these algorithms in python programming language
- To familiarise students with a wide range of real finance problems with actual data sets.
4) Finance
- Financial Accounting, Double-entry bookkeeping
- Time value of money
- Risk & risk management