ESSENTIAL MATHEMATICS FOR DATA SCIENCE - 2027/8

Module Overview

Mathematics is a key tool in Data Science. This module is designed to introduce students to the foundational mathematical techniques that are required to support future data science modules.

Module provider

Mathematics

Module Leader

WOLF Martin (Maths & Phys)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 7

Module cap (Maximum number of students): N/A

Module Availability

Semester 1

Prerequisites / Co-requisites

None

Module content

Indicative content includes:
Linear Algebra
Linear Systems of Equations

• Basic matrix operations including matrix multiplication and determinants, and converting data into matrix format.

• Rank and nullity of matrices including elementary row operations and row-echelon form.

• Setting up and solving linear systems of equations by Gaussian elimination.

Vector Spaces:

• Basic concepts of vector spaces including linear independence of vectors and bases of vector spaces.

• Linear maps between vector spaces and their matrix representations.

• Diagonalisation of matrices including eigenvalues and eigenvectors.

Calculus
Functions:

• Basic concepts of functions including domains and ranges.

• Limits of functions and continuity.

• Graphical methods to examine behaviour of functions.

Differentiation and Integration:

• Basic differentiation rules for functions of one and several variables.

• Optimisation methods in data science including the gradient descent method.

• Basic integration rules for functions of one and several variables.

Assessment pattern

Alternative Assessment

None

Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate:

• Knowledge and understanding of mathematical concepts and rules.

• The ability to identify and use appropriate techniques to solve mathematical and data science problems.

Thus, the summative assessment for this module consists of:

• One in-semester test (50 minutes), run online in an invigilated computer laboratory, worth 20% of the module mark, corresponding to Learning Outcomes 1 to 5.

• A synoptic examination (2 hours), worth 80% of the module mark, corresponding to all Learning Outcomes 1 to 7.

Formative assessment
There are regular formative online unassessed courseworks over an eleven week period, designed to consolidate student learning.
Feedback
Students will receive feedback on the online unassessed courseworks and online in-semester test. This feedback is timed so as to assist students with preparation for the final synoptic examination. Students will also receive verbal feedback at the weekly tutorials, which are designed to promote student engagement with mathematical and data science problems.

Module aims

• Consolidate and extend higher mathematical knowledge in key topics relevant to data science.
• Provide students with an introduction to linear algebra and matrices, functions and limits, differentiation and integration, and discrete mathematics.
• Enable students to apply their mathematical knowledge and skills to problems in data science.

Learning outcomes

Methods of Teaching / Learning

The learning and teaching strategy is designed to:

• Provide students with a range of mathematical backgrounds with the tools to tackle problems in data science.

• Provide students with an understanding of vectors, matrices, systems of equations, functions, differentiation and integration.

• Provide students with experience of mathematical methods used to a range of problems in data science.

The learning and teaching methods include:

• Three one-hour lectures for eleven weeks, with module notes provided to complement the lectures. These lectures provide a structured learning environment and opportunities for students to ask questions and to practice methods taught..

• Eleven tutorials for guided discussion of solutions to problem sheets (provided to students in advance) to reinforce their understanding of mathematical concepts and methods, and enable students to engage in solving mathematical problems relating to data science.

• Formative online unassessed problem sheets designed to provide students with opportunities to consolidate learning. Feedback on these unassessed problem sheets will provide students with guidance on their progress and understanding.

• Lectures will cover core topics. Video recordings of core topics covered in lectures will also be provided. These recordings are intended to give students an opportunity to review parts of lectures which they may not fully have understood and should not be seen as an alternative to attending lectures.

• Additional video recordings of revision topics and extension topics may also be provided. Extension topics will also be covered via problems discussed in tutorials using a flipped learning approach.

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list

https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: MATM074

Other information

The Faculty of Engineering and Physical Sciences is committed to developing graduates with strengths in Digital Capabilities, Employability, Global and Cultural Capabilities, Resourcefullness and Resilience, and Sustainability. This module is designed to allow students to develop knowledge, skills and capabilities in the following areas:
Digital Capabilities: The SurreyLearn page features a dynamic discussion forum where students can pose questions and engage with others using e.g. LaTeX and MathML tools. This enhances their digital competencies while facilitating collaborative learning and information sharing. Students are also introduced to the mathematical tools required to solve problems with big data.
Employability: The module equips students with skills which significantly enhance their employability. The mathematical proficiency gained hones critical thinking and problem-solving abilities. Students learn to analyse real-world problems and apply mathematical techniques to arrive at solutions.
Global and Cultural Capabilities: Students enrolled in the module have a wide range of cultural backgrounds and experiences. Students are encouraged to work together during problem-solving teaching activities in tutorials and lectures, which naturally facilitates the sharing of different cultures.
Resourcefulness and Resilience: This is a module which demands the analytical ability to perform mathematical calculations accurately. Students will gain skills in analysing mathematical and data science problems, and will complete assessments which challenge them and build resilience.

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2027/8 academic year.