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CSC-10077 - Module Specification
School of Computer Science and Mathematics
Faculty of Natural Sciences

For academic year: 2026/27 Last Updated: 16 September 2026

CSC-10077 - Mathematics and Statistics for Data Science
Coordinator: Amirreza Khodadadian
Lecture Time:
Level: Level 4
Credits: 30
Study Hours: 300
School Office: 01782 733075

Programme/Approved Electives for 2026/27

None

Available as a Free Standing Elective

No

Co-requisites

None

Prerequisites

none

Barred Combinations

none

Description for 2026/27

This module provides a foundation in statistics and mathematics for data science. Students develop the knowledge and skills required to explore, analyse, interpret, and communicate data using appropriate statistical and mathematical techniques. Topics include data visualisation, descriptive statistics, probability, statistical inference, correlation, regression, vectors, matrices, linear algebra, functions, calculus, and complex numbers. Throughout the module, emphasis is placed on applying these concepts to real-world data science problems, developing analytical reasoning, problem-solving skills, and the ability to communicate quantitative findings effectively.

Aims
The module follows the following aims:
1) To provide students with a foundation in calculus necessary and required for data science. It will include topics such as:
a) complex numbers
b) matrices and series
c) differentiation and integration.
2) To understand some of the more common statistical techniques, to encourage good practice, and to highlight common errors and
misconceptions. Key to this module is to provide a differentiated learning framework for apprentices, some of whom may not have had significant
mathematical and statistical education beyond Level 2 whilst others may have a level 3 or higher mathematics background.
Specifically, the module aims to develop:
a) a sound knowledge of mathematical concepts, skills, and techniques important in the use of data science.
b) confidence in applying mathematical and statistical thinking and reasoning in a range of new and unfamiliar contexts to solve real-life problems;
c) competency in interpreting and explaining solutions to problems in context;
d) fluency in procedural skills, common problem-solving skills, and strategies.

Intended Learning Outcomes

Analyse, summarise and present data using appropriate statistical methods, graphical techniques and numerical measures, and explain the findings in context.: 1,2
Apply probability and statistical techniques, including probability distributions, confidence intervals, correlation and simple linear regression, to investigate data and support decision making.: 2
Use fundamental mathematical concepts, including vectors, matrices, linear algebra, eigenvalues, functions, complex numbers, differentiation and integration, to solve problems arising in data science.: 3
Select appropriate mathematical and statistical methods to investigate practical problems, interpret the results, and explain the strengths and limitations of the methods used.: 1,2,3
Communicate mathematical and statistical reasoning clearly by presenting calculations, graphs, interpretations and conclusions in a logical and well-structured manner.: 1,2,3

Study hours

36 hours of teaching (lectures, tutorials and practical sessions)
8 hours of student supervision
236 hours of independent study
20 hours of coursework (preparation and completion of assessments)

School Rules

None

Description of Module Assessment

1: Assignment weighted 20%
The exercise 1
Individual coursework requiring students to analyse, interpret and present a given dataset using appropriate statistical techniques covered during the first part of the module. Students will demonstrate their ability to summarise data, select suitable graphical representations, interpret statistical results, and communicate their findings clearly. The report should not be more than 2000 words.

2: Assignment weighted 30%
The exercise 2
Individual coursework requiring students to undertake a comprehensive statistical investigation of a given dataset. Students will select and apply appropriate statistical techniques, evaluate relationships within the data, interpret the results in context, discuss the reliability and limitations of their analysis, and communicate evidence-based conclusions in a structured report. The report should not be more than 2000 words.

3: Assignment weighted 50%
The exercise 3
Individual coursework based on the application of mathematical concepts to data science scenarios. Students will demonstrate their understanding of vectors, matrices, linear algebra, functions, calculus and related topics by solving practical problems, interpreting the results, and justifying the mathematical methods used. The report should not be more than 3000 words.

The University undertakes a continuous review of its teaching and research provision to ensure programmes are of a high quality and changes may be made to modules where it is necessary and reasonable to do so. Please be aware that minor changes to modules, such as a change to an individual assessment, can occur up until enrolment, although we endeavour to give you as much notice as possible of any changes. Details about the circumstance in which changes are made to programmes can be found in the Student Agreement, available at: https://www.keele.ac.uk/legalgovernancecompliance/governance/actcharterstatutesordinancesandregulations/studenttermsconditions/

Last Updated: 16 September 2026