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2024/25 Taught Postgraduate Module Catalogue

COMP5712M Programming for Data Science

15 creditsClass Size: 400

Module manager: Giulia Sindoni

Taught: Semester 1 (Sep to Jan) View Timetable

Year running 2024/25

This module is not approved as an Elective

Module summary

This module is designed to give those with little or no programming experience a firm foundation in programming for data analysis and AI systems, recognising a diversity of backgrounds. The module will also fully stretch those with substantial prior programming experience (e.g., computer scientists) to extend their programming and system-building knowledge through self-learning supported by on-line courseware.


The module introduces the fundamental skills of programming and software system development. It aims to give students the skills and experience to produce simple computer-based applications for a range of sectors. It prepares students to develop and integrate systems using Artificial Intelligence and Data Analytics techniques.

Learning outcomes
By the end of the module, students will be able to:
• Design, build and test computer programs in Python
• Implement applications across a selected domain (e.g., health, finance)
• Build systems that integrate with the internet and databases
• Understand how software is used for data analysis and AI


Computer programming in Python: control structures, datatypes, data structures, functions and classes, importing and using libraries, implementing simple algorithms.
Use of a Python development platform.
Use of specific libraries/APIs providing data access and analysis functionality, such as: accessing information from the web or from databases, statistical analysis, ML algorithms, graphical display of data.
Students will undertake a sequence of programming exercises starting with the fundamentals of programming and building up to a system that performs significant data analysis on real data:
• Basic algorithms for representing and processing information.
• Importing, manipulating and displaying data.
• Use of basic ML algorithms (e.g., k-means, nearest neighbour and linear classification).
• Implementation of a data analysis ‘pipeline’ in which data is extracted from some source, processed, analysed and visualised. The data investigated in this exercise will be drawn from the sector of each student’s masters programme (e.g., finance, health, law).

Teaching methods

Delivery typeNumberLength hoursStudent hours
Drop-in Session101.0010.00
Private study hours120.00
Total Contact hours30.00
Total hours (100hr per 10 credits)150.00

Private study

The student will be expected to complete several programming exercises relating to specialist domains. They will undertake program development and submit their code for evaluation. For some exercises an additional written report may be required.

Methods of assessment

Assessment typeNotes% of formal assessment
In-course AssessmentCoursework 120.00
In-course AssessmentCoursework 260.00
In-course AssessmentOnline Test20.00
Total percentage (Assessment Coursework)100.00

This module will be reassessed by coursework.

Reading list

The reading list is available from the Library website

Last updated: 09/05/2024 14:54:47


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