Module MATH42615: Introduction to Mathematics for Data Science

Department: Mathematical Sciences

MATH42615: Introduction to Mathematics for Data Science

Prerequisites

• None

Corequisites

• None

Excluded Combination of Modules

• None

Aims

• To introduce the mathematical principles that underpin contemporary Data Science

Content

• Review of basic mathematical principles: functions, graphs, and notation.
• Overview of calculus: limits, differentiation, integration, numerical computation
• Introduction to Probability: independent events, conditional probability, expectation, probability distributions, computing probabilities
• Introduction to Linear Algebra: linear systems, matrices, vector spaces, geometric transformations, eigenvalues and eigenvectors

Learning Outcomes

• By the end of the module students will have a knowledge and understanding of mathematical concepts in the following areas:
• Commonly-used functions, their properties and features
• Basic concepts and techniques of differential and integral calculus
• Fundamental concepts of probability
• Basic concepts of linear algebra, their geometric interpretations, and applications to the analysis of linear systems

• Graphical representation of functions
• Use of calculus techniques for the analysis of functions, including basic optimisation.
• Understanding of likelihood and calculation of probabilities.
• Ability to visualise and understand the representation and transformation of data using linear algebra

• Sufficient mastery of mathematical concepts to enable engagement with introductory statistics and data science.

Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

• This module will be delivered by the Department of Mathematical Sciences.
• Lectures demonstrate what is required to be learned and the application of the theory to concrete examples.
• Workshops describe theory and its application to concrete examples, enable students to test and develop their understanding of the material by applying it to practical problems, and provide feedback and encourage active engagement.
• Surgeries give students the change to pose personalized questions on both theory and practice.
• Online resources support learning and could include: video content, directed reading, reflection through activities, opportunities for self-assessment, and peer-to-peer learning within a tutor-facilitated discussion board.
• Coursework will assess students' ability to implement theoretical concepts covered in the module.

Teaching Methods and Learning Hours

Summative Assessment

Formative Assessment:

None

■ Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University