MS4H02 - Programming for Data Analysis 04 Dec 2020 - 31 Aug 2026 | Version 1

Associated Module Information

Module Code: MS4H02
Module Title: Programming for Data Analysis
Faculty: Faculty of Computing, Engineering and Science
Faculty Group: Computing and Mathematics
Faculty Sub Group: Maths
Module Leader: Ieuan Griffiths
Module Team: Stephanie Perkins, Rebecca Peters
First Intended Intake: JAN 2025 Final Year of Intake: 2025
Date Closed:
Credit Value: 10 Credit Level: 7
Language: English
Percentage of Module Taught in Welsh: 0
Equivalent Module:
HECOS codes: 100992 - machine learning 101034 - statistical modelling
HECOS Code Weighting: 75 25

Document Version Information

Version 1
Valid From 04 Dec 2020
Valid To 31 Aug 2026

Module Aims

To introduce the fundamentals of programming and data manipulation tasks utilising a relevant Data/AI programming language.

Content Summary

This module aims to enhance the fundamental computer programming skills required to understand, interpret, research, and manage complex data. In particular, for graduates to apply appropriate techniques utilising a variety of industry focused programming languages to produce solutions to business problems.

Learning and Teaching Methods

Activity Type Hours
Lecture 8
Seminar 8
Tutorial 8
Independent Study 40
Directed Study 36
Total Hours Selected 100

Learning Outcomes

# Learning Outcome
LO1 Be able to design, analyse and implement data manipulation tasks.
LO2 Be able to use relevant packages and tools efficiently and effectively.

Module Requisites

N/A

Assessment Criteria

Assessment Category Assessment Type Description Duration Word Count Weight (%) Best of? Pass Mark
Practical Assessment (CW) Practical Coursework 1 Design, produce and test a computer program to interrogate a data set 0 N/A 100 No 40

Assessment Matrix

Assessment Type Learning Outcomes
LO1 LO2
Practical Coursework 1

Reading List

Grus, J. (2019) Data science from scratch: first principles with Python. O’Reily

Shaw, Z.A. (2013) Learn Python the hard way. Addison Wesley.

Hetland, M.L. (2017) Beginning Python: From Novice to Professional. Apress, Springer-Verlag

Vanderplas, J.T. (2016) Python data science handbook: essential tools for working with data. O’Reily.