Module code: MAT1042

Module Overview

This module introduces basic mathematical programming in Python and professional skills. The module covers digital skills such as basic data handling, processing and least squares fitting to analyze real-world problems. The professional skills cover employability, teamworking, writing a technical report, and presentation skills. The goal of the module is to equip students with the skills to tackle real-world problems, communicate their results and prepare them for employment after their degree.

Module provider

Mathematics & Physics

Module Leader

ROBERTS James (Maths & Phys)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 4

Module cap (Maximum number of students): 150

Overall student workload

Independent Learning Hours: 30

Lecture Hours: 24

Seminar Hours: 5

Laboratory Hours: 22

Guided Learning: 45

Captured Content: 24

Module Availability

Semester 2

Prerequisites / Co-requisites


Module content

Indicative content includes: 

  • Python programming: variables, in-built functions, data types, file/script writing, data types; insights into the structure of Python such as objects, instances, attributes, functions, classes, definitions, loops, logical statements and so on; the use of packages useful for data science; scientific code structuring; loading of python packages; loading, storing and manipulation of data files; plotting and debugging codes; data pre-processing; data visualisation; and data analysis; simple regression analysis; model validation; making projections.

  • Data Analysis: Introduction; data sets; data visualisation; data security; ethics; understanding trends and anomalies; reproducibility; using Python for data analysis; regression analysis; model validation and projections

  • Overleaf: Setting up LateX documents; Typesetting; Handling Errors; Using Packages; Structuring Documents; Figures and tables; Bibliographies; Further online Resources; report writing in overleaf

Skills Development: How to research; Scientific Report Writing; Critical Thinking/Review of information sources; good coding practices; good report writing practices; project planning and managing projects; group project work; individual project work; presentation skills.

Assessment pattern

Assessment type Unit of assessment Weighting
Coursework Individual Report 35
Coursework Group Project 45
Oral exam or presentation Presentation 20

Alternative Assessment

The alternative assessment for the group project is an individual piece of coursework covering the same learning outcomes.

Assessment Strategy

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

  • Their understanding of the use of Python as a scientific language to solve problems in data science.

  • Their ability to extract valuable information from the results of their data analysis.

  • Their ability to write well-structured, reusable functional code.

  • Their ability to work as part of a group and to communicate mathematical concepts and results.

Thus, the summative assessment for this module consists of:

35% Python individual coursework - covers basic Python e.g. for loops/if statements/etc. Written report and submission of code.

45% group project coursework - technical report based on open ended project. 

20% presentation skills.

Formative assessment

Unassessed Python coursework

Unassessed group project report feedback 


Students receive individual written feedback on the formative unassessed Python coursework and feedback on the draft group project report. The feedback is timed so that feedback from the unassessed Python coursework assists students with preparation for the Python individual coursework. The feedback on the draft project report aids students in preparation of the final report. Students also receive verbal feedback in computer lab sessions and in office hours.

Module aims

  • Equip students with the skills to use Python for problem solving in mathematics.
  • Equip students with the skills to program in Python for data science, data analysis.
  • Equip students with the skills to extract information from large data sets.
  • Introduce students to the fitting and use of basic regression models to analyze large data sets
  • Equip students with the skills to communicate mathematical concepts and results effectively.
  • Give students the practical experience of working effectively in a group. This includes planning work, the allocation of resources to meet deadlines and management of the production of a group submission.

Learning outcomes

Attributes Developed
001 Students will demonstrate the ability of using Python for scientific computing, and data analysis. KPT
002 Students will demonstrate the capability to apply simple data analysis tools and to interpret the results. KCPT
003 Students will be able to fit a linear regression model to a data set and will be able to interpret the results KCPT
004 Students will be able to write a report on their investigation, model fitting and results. PT
005 Students will be able to prepare and deliver a presentation using PowerPoint. PT

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

Methods of Teaching / Learning

The learning and teaching strategy is designed to provide:

  • A comprehensive introduction to Python with a focus on giving students the experience in implementing Python codes for problem solving in mathematics.

  • Experience in programming in Python to tackle problems in data science, to enhance students’ digital capabilities and employability.

  • Practical experience in analysing data to extract valuable information, to enhance student resourcefulness and resilience.

The learning and teaching methods include:

  • 2 x 1 hour laboratories per week x 11 weeks. The lab sessions are used to reinforce material covered in lectures and to give students the opportunity to apply methods through practical programming exercises using Python.

  • The first 8 weeks of labs will cover content from lectures, weeks 9,10,11 labs will be reserved for project help.

  • 3 x 1 hour lectures for weeks 1 – 8, weeks 9,10,11 - drop in sessions/office hours only. Where appropriate the lectures are delivered as seminar or tutorial style sessions.

  • Assessed coursework to give students practical experience of implementing techniques covered in lectures and lab sessions in an extended piece of work.

  • Several pieces of unassessed coursework to give students experience of using techniques introduced in the module and to receive formative feedback.

  • Lectures may be recorded. Lecture recordings are intended to give students the opportunity to review parts of the session that they might not have understood fully and should not be seen as an alternative to attendance at lectures.

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
Upon accessing the reading list, please search for the module using the module code: MAT1042

Other information

The School of Mathematics and Physics is committed to developing graduates with strengths in Digital Capabilities, Employability, Global and Cultural Capabilities, Resourcefulness and

Resilience and Sustainability. This module is designed to allow students to develop knowledge,

skills, and capabilities in the following areas:

Digital Capabilities: Students gain experience in programming with Python.

Employability: The programming and report writing skills developed in the module are valued by employers in many sectors. Further, the group work emulates working styles of many employment types, with the communication and group working skills developed being widely transferable.

Global and Cultural Capabilities: The group work is a key part of MAT1042. Through this work, students gain experience of working with people from a range of cultural backgrounds.

Resourcefulness and Resilience: The demands of the data analysis and Python coding enhance student resourcefulness. Group dynamics and shared responsibilities teach resilience in the face of setbacks. This combination equips learners with valuable skills to adapt, problem-solve, and succeed.

Sustainability: The data analysis skills learned in MAT1042 equip students with the skills to analyse data on resource consumption, emissions, and environmental impact, facilitating the development of sustainable practices. Thus, this module plays a role in creating a more sustainable future.

Programmes this module appears in

Programme Semester Classification Qualifying conditions
Mathematics with Data Science BSc (Hons) 2 Compulsory A weighted aggregate mark of 40% is required to pass the module
Mathematics BSc (Hons) 2 Compulsory A weighted aggregate mark of 40% is required to pass the module
Financial Mathematics BSc (Hons) 2 Compulsory A weighted aggregate mark of 40% is required to pass the module
Mathematics MMath 2 Compulsory A weighted aggregate mark of 40% is required to pass the module
Mathematics and Physics BSc (Hons) 2 Compulsory A weighted aggregate mark of 40% is required to pass the module
Mathematics and Physics MPhys 2 Compulsory A weighted aggregate mark of 40% is required to pass the module
Mathematics and Physics MMath 2 Compulsory A weighted aggregate mark of 40% is required to pass the module

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 2025/6 academic year.