Welcome to 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 that have not taken more than 2 dedicated mathematics courses in their undergraduate degree.
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 the 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. As such, the course closely follows the
course reader. This document accompanies the students throughout the semester all through the final exam. In fact, a copy of the course reader, free of any annotations and writing, should be brought to the final exam.
In addition to the final exam, the course assessment includes 6 homework assignments. These assignments involve solving problems from the course reader as well as additional problems, some of which need to be carried out computationally using
Wolfram Mathematica. Most Mathematica based problems are taken from this collection of
Mathematica based exercises.
Home assignments are to be submitted individually with each student submitting a unique assignment (copying assignments will not be tolerated). Nevertheless, students are encouraged to collaborate and discuss the homework assignments in an open and constructive manner. Sharing ideas, helping each other and jointly working towards a goal is great.