Data Analytics - Athlone
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.
| Award Name | Degree - Masters (Level 9 NFQ) |
|---|---|
| NFQ Classification | Major |
| Awarding Body | Technological University of the Shannon |
| NFQ Level | Level 9 NFQ |
| Award Name | NFQ Classification | Awarding Body | NFQ Level |
|---|---|---|---|
| Degree - Masters (Level 9 NFQ) | Major | Technological University of the Shannon | Level 9 NFQ |
Duration
12 Months full-time.
Entry Requirements
Minimum Entry Requirements:
A Level 8 or equivalent honours degree in Business, Science or Engineering, with a minimum grade of 2.2 (50%), comprising of at least 30 ECTS credits in any combination of maths, computer science or engineering. In line with institute policies, non-native English speakers are required to have an IELTS level of 6.5 or higher.
Careers / Further progression
Career Opportunities
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 Market 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.
Further information
Course Commencement Date: September
FEES: EU full-time €7,000,
€17,500* non-EU
Take the guesswork out of decision making with the TUS Athlone MSc in Data Analytics.
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.
TUS Athlone, has developed an industry-focused, contemporary masters programme that will equip graduates with the skills and aptitudes necessary to excel in the emerging field of Big Data and Data Analytics. 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 programme runs over one calendar year, commencing in September, consisting of three semesters. Semesters 1 and 2 will incorporate three key pillars of data analytics: Data, Tools & Techniques and Analysis. Each pillar overlaps with the other to provide a coherent and unified set of core skills in data analytics. Semester 3 will consist of a substantive research project.
At the core of the discipline is data. In this pillar, students will develop their skills in areas including database technologies, data manipulation languages including SQL and the R programming language. In order to understand the data, a range of techniques will be taught, including programming for Big Data, statistics and probabilities and the interpretation of data. Interwoven within these modules is the use of industry-standard data analytics software tools. The final pillar of the programme is analysis. In these modules, students will develop skills to become data-savvy practitioners, gaining insights into data from which strategic decisions can be made.
Applied Research Project: In Semester 3 of the programme, students will be required to undertake a data analytics project and associated thesis of 20,000 words.
Year 1 – Semester 1 (September)
Relational Databases
Credits: 5
Programming for Data Analytics
Credits: 10
Data Analytics
Credits: 5
Statistics for Data Analysis
Credits: 5
Interpretation of Data
Credits: 5
Year 1 – Semester 2 (January)
Advanced Analytics
Credits: 5
Research Methods
Credits: 5
Advanced Databases
Credits: 10
Data Visualisation
Credits: 10
Year 1 – Semester 3 (May)
Applied Research Project
Credits: 30
Bernard Tao Cui
Head of Department
Email: Tao.Cui@tus.ie