Skip to main content

Course Search

Course Search

Course Search

Data Analytics – MSc

  • Campus: Moylish, Limerick City

  • years: 1

Course Overview

Data Analytics is the process of examining vast quantities of data, often referred to as Big Data, in order to draw conclusions and insights about the information they contain. Some examples of Data Analytics applications include real-time fraud detection, complex competitive commercial analysis, website optimisation, intelligent air, road and other traffic management and consumer spending patterns.

Big Data presents three primary problems: there’s too much data to handle easily; the speed of data flowing in and out makes it difficult to analyse; and the range and type of data sources are too great to assimilate. With the right analytics and techniques, these big data can deliver hidden and unhidden insights, patterns and relationships from multiple sources using Data Analytics techniques. This programme will ensure that you will be able to understand the data context, apply appropriate techniques and utilise the most relevant tools to generate insights into such data.

The Expert Group on Future Skills Needs report identified Data Analytics as an area of skills deficit. Given the wide range of industries in which Data Analytics can be utilised, the demand for Data Analytics graduates continues to soar. According to IBM, this demand is to increase by 28% by the year 2020 (Forbes, 2017). The average salary for Data Analysts in the US is $69,949 (PwC, 2017), in Ireland, the average salary is €44,758 (indeed ie, 2017).

Contact Details

Business and Humanities

Faculty Office


What are the entry requirements?

A Level 8 or equivalent honours degree in Business, Science or
Engineering. Minimum grade of 2.1 (60%), comprising of at least 30 ECTS credits in any combination of maths, computer science or engineering. English Language: Equivalent of IELTS 6.0 and above.

Course Modules

  • Data Analytics

    Credits: 5

    Data analytics is an area of increasing importance and interest to organisations. Data analytics techniques offer huge potential in the creation of new knowledge products and services in addition to the enhancement of existing products and services. This module addresses the application of data analytics techniques to real-world business problems and the preparation of data for such scenarios.

  • Interpretation of Data

    Credits: 5

    This module will teach students the importance of data pre-processing and data exploration. Students will prepare data for analysis using a range of advanced data processing techniques and software tools.

  • Programming for Data Analytics

    Credits: 5

    Students taking this module will acquire the computer programming skills necessary to analyse and manipulate data sets. This module will introduce key programming concepts using programming languages designed specifically for data analytics.

  • Relational Databases

    Credits: 5

    This module provides the student with the skills in areas such as entity modelling, normalisation and database design. In addition, significant time will be devoted to utilising the SQL language to operationalise the output of the design process, manage data and manage security in a modern relational database management system.

  • Statistics for Data Analytics

    Credits: 5

    This module will introduce students to the use and role of probability models and statistical inference in data analytics. Laboratory work will give the student experience in applying probability and statistical models to real-world data.

  • Advanced Analytics

    Credits: 5

    This module will build on the content covered in the Data Analytics module, focusing on analytics techniques and how these can be applied to specific business problems. The purpose of this module is to expand the student’s understanding of techniques employed in data analytics by exposing them to authentic case studies of approaches that organisations have taken to implement solutions to real problems in the field or based on scenarios which have no obvious solutions. This will allow afford students the opportunity to articulate their own interpretation in a collaborative, peer-assessed environment. Another goal of this module will be to expose students to the varied uses of data analytics across a range of diverse industries.

  • Advanced Databases

    Credits: 10

    Advanced Databases will build on the Relational Databases module from Semester 1 and give students the opportunity to acquire a thorough understanding of stored procedures in the context of an enterprise database environment. A database programming language will be taught and students will study the key language constructs such as variables, conditional structures, loops, in addition to other more advanced topics such as cursors, functions, and packages. Another key strand in this module is growing importance of NoSQL and other emerging database technologies, students will get an in depth appreciation of those technologies, and their role in an era of massive data storage and processing challenges. In particular, the challenges around distributed databases, transaction processing and data consistency will contrast strongly with some of the topics covered in Semester 1. Finally, the legal, social, and ethical issues and responsibilities for managers and users of databases will be a key backdrop for data analytics students and complement what they are studying in several other modules on the programme.

  • Data Visualisation

    Credits: 10

    This module develops student skills in data visualisation by introducing various data visualisation techniques. Students will learn how to explain the insights obtained from large data sets using data visualization techniques. One of the essential skills in data analytics is the ability to tell a story, visualizing data and findings. Students will learn how to use various techniques to present data visually, obtain a better understanding of the data, and make more effective decisions.

  • Research Methods

    Credits: 5

    This module aims to introduce students to the key concepts involved in research and to develop their understanding of the uses and relevance of the major methodologies employed. The material covered in this module will form the basis for the research dissertation element of the MSc in Data Analytics programme, with one of the key outcomes of this module being a valid and robust research proposal for research in the area of Data Analytics.

  • Applied Research Project

    Credits: 30

    The project builds on the Research Methods module, where the research proposal will have been developed and submitted. The dissertation will consist of 20,000 words, excluding appendices. As the capstone component of the programme, this element will help integrate the curriculum content, and working in conjunction with an approved industry partner, deliver a significant body of work that will contribute to the body of knowledge in the field of data analytics. The dissertation will draw on analytical and evaluative competences based upon knowledge and skills developed during the programme. The exercise also provides an opportunity for students to develop their interests in a particular area of Data Analytics, while working with the industry partner and to demonstrate an ability to undertake individual research in an ethical and methodologically sound manner.

What can you do after this programme?

As Data Analytics is a relatively new and emerging field, the application of analytics spans a vast range of industries including finance, marketing, healthcare and biopharma. Career opportunities for graduates of this programme include:

  • Data Analyst
  • Data Scientist
  • Performance and Analytics Analyst
  • Data Operations Analyst
  • Financial Marker Analyst
  • Business Intelligence Analyst
  • Customer Insight Analyst

Upon successful completion of this programme, graduates have the opportunity to complete Level 9/10 programmes here at TUS or elsewhere.