Program Overview

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Data Analytics for Sport

Overview

The MSc in Data Analytics for Sport is an advanced programme designed to equip students with the skills and knowledge to analyze and interpret complex data from within the sports industry. This interdisciplinary field combines principles of data science, statistics, and sports science to provide insights that can enhance athletic performance, optimize team strategies, and improve overall organizational efficiency.

Programme Details

TU Code

TU422

NFQ Level

Level 9

Award Type

Major

Award

Master of Science

ECTS Credits

90

Duration

1 Year

Course Type

Postgraduate

Mode of Study

Full Time

Method of Delivery

On-Campus

Commencement Date

September 2025

Location

Grangegorman

Fees

€8,500 Total Fee (EU), €21,750 Total Fee (Non-EU)

Programme Description

The programme covers a range of topics including predictive modeling, machine learning, data visualization, and performance analytics. Students will learn to collect, process, and analyze large datasets using cutting-edge tools and techniques. Practical applications are a key component of the programme, with hands-on projects and collaborations with sports organizations providing real-world experience in applying data analytics to solve practical problems.

Minimum Entry Requirements

• An honours degree at the level of Upper Second Class Honours (2.1) in the area of Computing, Science, Engineering, Mathematics, or related areas with an appropriate numerate component.
• Students with an honours degree at the level of Lower Second Class Honours (2.2) with appropriate work experience may be considered for admission to the programme.
• Other applicants who do not meet these accredited learning criteria but who have substantial experience in the relevant field may be considered through a Recognition of Prior Learning process, in line with University policy.
• English language proficiency at the level of IELTS (Academic Version) English 6.5 overall (or equivalent) with nothing less than 6 in each component.

Course Content

• Fundamentals of Analytics
• Statistics for Analytics
• Data Mining
• Digital Transformation in Sport Ecosystems
• Sport Intelligence Platforms
• Data Analytics Research Project Preparation 1
• Data Analytics Research Project Preparation 2
• Data Analytics Research Project

Conversion Pathway

• Programming for Analytics
• Data Exploration

Computing Background

• Advanced Analytics Programming

Elective Modules

• Machine Learning
• Programming for Big Data
• Deep Learning
• Digital Ethics

Schedule

Teaching takes place from Monday to Friday. As a twelve-month programme, classes will commence in early September with the programme running until the end of August. Classes take place weekly throughout the semester and in blocks between semesters. Students will finish their Data Analytics Research Project over the summer period.