2024/25 Taught Postgraduate Module Catalogue

OCOM5100M Programming for Data Science

15 creditsClass Size: 100

Module manager: Dr Noorhan Abbas
Email: N.H.Abbas@leeds.ac.uk

Taught: 1 Mar to 30 Apr, 1 Sep to 31 Oct 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.

Objectives

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.

Syllabus

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 type	Number	Length hours	Student hours
On-line Learning	6	1.00	6.00
Group learning	6	2.00	12.00
Independent online learning hours			28.00
Private study hours			104.00
Total Contact hours			18.00
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

Coursework

Assessment type	Notes	% of formal assessment
Computer Exercise	Basic Programming - Program Code	0.00
Computer Exercise	Basic Programming - Program Code (c.100 lines of Python code)	0.00
In-course Assessment	Online test (c. 20 questions)	20.00
Computer Exercise	Data Analysis - Program Code and Report (c.200-300 lines of Python code with text explanations [max 1500 words])	80.00
Total percentage (Assessment Coursework)		100.00

Resit will be by a project assessment similar to summative assessment 2 (worth 100% of the grade)

Reading list

The reading list is available from the Library website

Last updated: 24/05/2024 17:06:29

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