Mathematics for Data Science 1

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.

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.

The course is coordinated by Yoni Nazarathy (y.nazarathy@uq.edu.au) and the tutor is Maria Kleshnina (m.kleshnina@uq.edu.au). The study format is as follows:

- Follow course materials on-line (see the links to study units 1-10 below). These include video recordings of annotations over the course reader as well as other suggested videos and material. That is, watch the course content at your own time, with a view of keeping up with the home assignment schedule. As you watch the course content - it is recommended that you have paper and pen at hand; pausing for reflection and experimentation is very helpful for the learning process. It is your responsibility to maintain a level of
**active learning**as you watch the videos - don't let the videos drive you into a mode of passive learning.

- There are two face to face meetings with the course staff per week:
- Wednesdays 12-12:50, 67-443. Mathematica and insight session with Yoni Nazarathy.
- Thursdays 11-11:50, 07-302. Tutorial problem solving sessions with Maria Kleshnina.

- Students are encouraged to communicate, both with each other, and with the course staff (as needed) via the course's slack workspace: Please join the slack workspace. Messages from the course staff will also be disseminated via slack.

- Hand in the course assignments on time. Hand in a hard copy to Maria Kleshnina at the
**start**of the tutorial in the week when the assignment is due.

- Look and reflect at feedback on the course assignments, as marked assignments are returned to you.

- Attend a three hour practice exam, to be scheduled during the final week of semester.

- Attend a two hour session following the practice exam for reflection on the exam.

- Attend the final exam (takes place during the examination period, June 2018).

Below are links to Units 1--10 of the course including a course schedule:

- Unit 1 - Sets, number systems, counting and cardinality (Week 1).

- Unit 2 - Relations and functions (Weeks 2 and 3). Assignment 1: Due Week 4. Solution 1.

- Unit 3 - Foundations in logic (Week 4 and 6). Assignment 2: Due Week 7. Solution 2.

- Unit 4 - Sequences, their limits and series (Week 5). Assignment 3: Due Week 9. Solution 3.

- Unit 5 - Real functions: Limits and continuity (Week 7).

- Unit 6 - Elementary Matrix Operations (Weeks 7). Assignment 4: Due Week 10. Solution 4

- Unit 7 - Derivatives, Optimisation and basic ODEs (Week 9).

- Unit 8 - Linear approximations and Taylor series (Week 10). Assignment5: Due Week 11. Solution 5.

- Unit 9 - Integration (Week 11). Assignment 6: Due Week 13. Solution 6.

- Unit 10 - Vectors and partial derivatives (Week 12).