Mathematics for Data Science

Module title	Mathematics for Data Science
Module code	CSC2028
Academic year	2024/5
Credits	15
Module staff

Duration: Term	1	2	3
Duration: Weeks	11

Number students taking module (anticipated)	30

Module description

This module provides a grounding in the core mathematical skills essential for progression onto the With Proficiency in Data Science pathway. It builds a strong foundation in topics from pure maths and statistics, with a focus on developing confidence and competence in the understanding and application of fundamental concepts. Throughout the module students will be exposed to and become familiar with the terminology and notation that will be required for subsequent modules in the pathway. Classroom sessions will be a combination of lectures, and guided practical exercises to consolidate learning and provide the foundations for later study.

Module aims - intentions of the module

The module aims to develop confidence and competence in pure mathematics and statistics, which will provide the necessary foundation for the With Proficiency in Data Science Pathway or quantitative elements of other degree programmes. Relevant topics have been carefully selected to focus on the knowledge and skills necessary for students to subsequently engage with both the theoretical and practical elements of more extensive data science training. Integrated lectures and practical sessions will be used to introduce the material, demonstrate how it can be applied to a range of problems and provide opportunities for students to practice applying the techniques. While most of the practical exercises will include simple examples that students can complete by hand, they will additionally be exposed to using software to do the computations to highlight why data science is dependent upon a strong foundation in programming.

Intended Learning Outcomes (ILOs)

ILO: Module-specific skills

On successfully completing the module you will be able to...

1. Perform rudimentary operations using vectors and matrices, such as addition and multiplication.
2. Recognise a function and use graphs to characterise its form.
3. Select an appropriate probability distribution for a given context.
4. Describe the process and apply the language of statistical hypothesis testing.

ILO: Discipline-specific skills

On successfully completing the module you will be able to...

5. Demonstrate a basic knowledge and understanding of fundamental concepts necessary for progression to further studies in data science.
6. Formulate questions as mathematical/statistical problems.
7. Use mathematical terminology and notation with confidence.

ILO: Personal and key skills

On successfully completing the module you will be able to...

8. Apply skills learned in critical thinking to formulate, tackle and solve problems for a variety of real-world scenarios.

Syllabus plan

Topics will include

Linear Algebra & Functions
- Formulating and solving linear and quadratic equations.
- Introduction to functions and graphical representation of these.
- Introduction to scalars, vectors and matrices and common manipulations of these.
Probability
- Laws of probability
- Random variables and their properties
- Probability distributions to include both continuous and discrete examples
Statistics
- Principles and methods behind sampling from populations
- Using graphs to summarise and characterise data (link back to probability distributions)
- Summary statistics for different types of data
- Hypothesis testing

Learning activities and teaching methods (given in hours of study time)

Scheduled Learning and Teaching Activities	Guided independent study	Placement / study abroad
33	117	0

Details of learning activities and teaching methods

Category	Hours of study time	Description
Scheduled learning and teaching activities	33	Workshops (2 x 1.5 hrs per week) consisting of some lecture materials to introduce each topic and demonstrating how this knowledge can be applied to examples, followed by exercises to consolidate learning.
Guided independent study	117	Completing any remaining tasks from the workshops, some additional worksheets to facilitate further practice/revision.

Formative assessment

Form of assessment	Size of the assessment (eg length / duration)	ILOs assessed	Feedback method
Workshop exercises	1.5 hours per week	All	Model solutions and verbal feedback
Online MCQ	5 questions per week	All	Online software

Summative assessment (% of credit)

Coursework	Written exams	Practical exams
20	80	0

Details of summative assessment

Form of assessment	% of credit	Size of the assessment (eg length / duration)	ILOs assessed	Feedback method
MCQ Exam	80	2 hours	All	Written
Continuous Assessment	20	11 worksheets with 5 questions	All	Written

Details of re-assessment (where required by referral or deferral)

Original form of assessment	Form of re-assessment	ILOs re-assessed	Timescale for re-assessment
MCQ Exam	MCQ Exam (80%)	All	Summer assessment period
Continuous Assessment	1 Worksheet (10 questions) with selection of questions from across the weeks (20%)	All	Summer assessment period

Re-assessment notes

Reassessment will be by MCQ exam in the failed or deferred element only. For referred candidates, the module mark will be capped at 40%. For deferred candidates, the module mark will be uncapped.

Indicative learning resources - Basic reading

Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong. 2019. Mathematics for Machine Learning (Part 1). Cambridge and University Press.
Bruce, A. and Bruce, P. 2020. Practical Statistics for Data Scientists. O’Reilly.

Indicative learning resources - Web based and electronic resources

•ELE – https://ele.exeter.ac.uk/

Key words search

Mathematics; Algebra; Probability; Statistics

Credit value	15
Module ECTS	7.5
Module pre-requisites	None
Module co-requisites	None
NQF level (module)	5
Available as distance learning?	No
Origin date	01/03/2024