Welcome to the semester 1, 2021 version of MATH7501. This is a bridging course in the
Masters of Data Science Program at the University of Queensland. The course is designed to bring students up to speed with mathematical concepts from discrete mathematics, calculus and elementary linear algebra - all with a view of data science, statistics and machine learning applications that follow.
The course is recommended for data science students (or similar masters of engineering students) that have not taken more than two dedicated mathematics courses in their undergraduate degree. It can also be taken by students that have had several undergraduate mathematics courses a while back and require a refresher. The prerequisite for the course is to have basic knowledge of high-school mathematics, including algebra, geometry, (basic) trigonometry, working with functions, logarithms and related concepts of a similar level.
The goal of the course is to enable students to speak the "language of mathematics" in a way sufficient for understanding further data science, statistics and machine learning concepts. Since the course material is quite broad, there is less emphasis on the detailed mechanics, and more detail on the concepts at hand. The course closely follows the
course reader. This document accompanies the students throughout the semester and serves as the basis for the course structure. It is composed of 10 units where unit 1 deals with basics of linear algebra, units 2-4 deal mostly with discrete mathematics, and units 5-10 deal with calculus including elementary aspects from multi-variate calculus.
The study units generally cover basic mathematical concepts. In addition each unit closes with an "application" section where a concrete data science application of the underlying mathematics is discussed. Nevertheless, the focus of the course is not the application but rather the underlying mathematical foundations. The applications covered include k-means clustering, the set cover problem, automated reasoning, classification using neural networks, analysis of the number of iterations of algorithms, exploratory data analysis, basic profit optimization, and analysis of probability distributions. All of these are simply presented to illustrate how the "languages" of linear algebra, discrete mathematics and calculus come into play in the world of data science.
Students are required to follow course materials prior to lectures. Lectures then help students to further understand material studied individually. The lectures are also there to shed a light on the applications of the material and to guide students towards individual study in future lectures. The course assessment includes 3 homework assignments, 2 quizzes, and a final exam that is treated from a study perspective as an extended quiz. Some of the homework assignments are to be solved "by hand" while others need to be carried out computationally using
Wolfram Mathematica. Some Mathematica based problems are taken from this extended collection of
Mathematica based exercises created by
Sam Hambleton (whose YouTube channel may also give further insight into Mathematica).
Students may also look at resources from previous years:
2020 |
2019 |
2018. Keep in mind that the unit order has changed over the years.
After this course, Data Science students are encouraged to take
MATH 7502.
