COM7025 - Programming for Data Science and Artificial Intelligence

Objectives:

On successful completion of the module, students will be able to:
Demonstrate an understanding of programming concepts and techniques essential for building applications in AI and data science
Apply programming skills to manipulate and analyse data using popular libraries and frameworks
Select and apply suitable AI and machine learning algorithms to solving data problems
Apply best software practices, including modularisation, code documentation and version control, to ensure code sustainability and collaboration in AI and data science.

Content:

This module provides an introduction to programming for artificial intelligence (AI) and data science applications. Students will learn the fundamentals of programming languages and their application to AI and data science problems. The module will cover topics such as data representation, data manipulation and cleaning, data visualisation and algorithms. Students will learn how to apply these concepts using popular programming languages such as Python, R and MATLAB.

Throughout the course, students will develop their programming skills and gain hands-on experience through practical exercises and assignments. They will learn how to write sustainable and efficient code and optimise algorithms for different size datasets. They will also explore software best practices and secure development principles such as modularity, documentation, version control and collaboration tools in AI and data science.

Upon completion, students will have a solid foundation in programming for AI and data science and will be equipped with the skills to solve real-world problems using programming languages and tools commonly used in the field.

Learning and Teaching Information:

Workshops may take different approaches relevant to the content. Students may be initially presented with the fundamental key concepts to enable them to understand and analyse the material. These will be interactive in nature. If possible, there will be some guest visits undertaken by academics and industry practitioners. This may then be followed up by an opportunity for students to apply techniques to given scenarios.

Workshops
Hours: 30
Intended Group Size: 50

Guided independent study
Hours: 120

Further details relating to assessment
Software Artefact: students are presented with a real-world problem for which they must find an AI algorithmic solution and implement that solution in code. The problem will require the application of programming techniques to manipulate and manage data, statistics and mathematics algorithms to scale and optimise the solution. Students will also need to apply software development best practices such as DevOps and MLOps when they implement their solution.

Students are required to provide the following:
- coding scripts, dataset/s, test cases, features, etc
- project documentation, workability, and interpretation of results.

In this module, formative assessment will be used to support the skills that contribute to the assessment. Formative assessment will take the form of an e-portfolio where students will be able to evidence progression on the module through the development of computer programs composed of polished, documented and extended versions of lab exercises started during the sessions. Formative feedback will be an ongoing process within class sessions.

Students should refer to the Module Handbook for further details on the module learning, teaching, and assessment strategies.

Assessment:

Fact File

Module Coordinator - PRS_CODE=
Level - 7
Credit Value - 15
Pre-Requisites - NONE
Semester(s) Offered - 7T1

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