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2022/23 Taught Postgraduate Module Catalogue

OCOM5100M Programming for Data Science

15 creditsClass Size: 100

Module manager: Dr Noorhan Abbas

Taught: 1 Mar to 30 Apr, 1 Mar to 30 Apr (2mth)(adv yr), 1 Sep to 31 Oct, 1 Sep to 31 Oct (adv yr) View Timetable

Year running 2022/23

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 Analysis techniques.

Learning outcomes
On completion of this module students should be able to:

1. Design, build and test computer programs in Python.
2. Implement applications relating to data analysis in specific domains (e.g. text analysis, health, finance, geography).
3. Build systems that integrate with the internet and external data.
4. Be able to apply general principles and techniques for the use of software in data analysis and AI.


Indicative content for this module includes:

- Computer programming in Python: control structures, data-types, 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 example AI and ML algorithms.
- Implementation of a data analysis ‘pipeline’ in which data is extracted from some source, processed, analysed and visualised.

Teaching methods

Delivery typeNumberLength hoursStudent hours
On-line Learning61.006.00
Group learning62.0012.00
Independent online learning hours28.00
Private study hours104.00
Total Contact hours18.00
Total hours (100hr per 10 credits)150.00

Private study

Private study will include directed reading and exercises and self-directed research in support of learning activities, as well as in preparation for assessments.

Independent online learning involves non-facilitated directed learning. Students will work through bespoke interactive learning resources and activities in the VLE.

Opportunities for Formative Feedback

Online learning materials will provide regular opportunity for students to check their understanding (for example through formative MCQs with automated feedback). Regular group activity embedded into learning will allow self and peer assessment providing opportunities for formative feedback from peers and tutors.

Two early formative assessments have been designed to enable students to develop and test basic programming skills (LO1 ) that they learn at the start of the module. This will build student confidence in basic programming concepts and in validating their code.

Methods of assessment

Assessment typeNotes% of formal assessment
In-course AssessmentIn-course online test20.00
Computer ExerciseBasic Programming – Program Code0.00
Computer ExerciseBasic Programming – Program Code0.00
Computer ExerciseAlgorithms and Data – Program Code20.00
Computer ExerciseData Analysis – Program Code and Report60.00
Total percentage (Assessment Coursework)100.00

.Resit will be by a project assessment similar to Assessment 2.

Reading list

The reading list is available from the Library website

Last updated: 14/02/2023


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