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

JACs code:

Module cap (Maximum number of students): 150

Overall student workload

Independent Learning Hours: 79

Lecture Hours: 22

Seminar Hours: 5

Laboratory Hours: 22

Captured Content: 22

Module Availability

Semester 2

Prerequisites / Co-requisites


Module content

Indicative contents include: 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
Coursework Presentation 20

Alternative Assessment

One piece of coursework

Assessment Strategy

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

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

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

  • Their ability to write well-structured, reusable functional codes

  • Their ability to work and communicate their 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 and feedback:

Unassessed python coursework

Unassessed group project report feedback 

Module aims

  • To equip students with the skills to program in Python for data science, data analysis.
  • To equip students with the skills to extract information out of large data sets.
  • To fit a basic regression model to analyze large data sets.
  • Communicate effectively as a mathematician.
  • How to work effectively in a group, planning work and allocating resources to meet deadlines and manage the production of a group submission.

Learning outcomes

Attributes Developed
001 Demonstrate the ability of using Python for scientific computing, and data analysis KPT
002 Demonstrate the capability to apply simple data analysis tools and to interpret the results CKPT
003 Demonstrate ability to apply linear regression to a data set and interpret the results CKPT
004 Demonstrate how to write a report PT
005 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 programming in Python with the view towards applications in data science, to enhance their digital capabilities and employability.

  • Experience in implementing Python codes for problem solving.

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

The learning and teaching methods include:

  • 2 x 1 hour laboratory per week x 11 weeks. This will include short discussions on the content of the lectures (max 30 min) and their applications through practical programming exercises (about 1.5 hour).

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

  •  3 x 1 hour lectures for weeks 1-6, 2 x 1 hour lectures weeks 7 and 8, no lectures weeks 9,10,11 - drop in sessions/office hours only

  • 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.

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 primary purpose of the course is to enhance digital capabilities of the student in the context of data science. Because applications of data science are becoming increasingly more important in many areas, the course will significantly improve the employability of the students, while enhancing their resourcefulness and resilience.

Programmes this module appears in

Programme Semester Classification Qualifying conditions
Mathematics with Statistics 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
Mathematics with Music 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 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
Mathematics 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 2024/5 academic year.