Why take this course?
Do you want to transform the world? Take the guesswork out of decision making with the AIT MSc in Data Analytics.
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.
Athlone Institute of Technology, recently voted Institute of Technology of the year 2018, has developed an industry-focussed, 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.
What will I experience?
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 an industry-led 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.
Minimum Entry Requirements:
A Level 8 or equivalent honours degree in Business, Science or Engineering, with a minimum grade of 2.1 (60%), 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.
All applicants will be subject to an interview.
Semester 1 (September):
Relational Databases in the Era of Big Data (5 credits)
Programming for Big Data (10 credits)
Data Analytics (5 credits)
Statistics for Data Analysis (5 credits)
Data Driven Decision Making (5 credits)
Semester 2 (January):
Advanced Analytics (5 credits)
Research Methods (10 credits)
Databases for Big Data (5 credits)
Data Visualisation (10 credits)
Semester 3 (May):
Industry-led Project (30 credits)
Course Commencement Date: September 2020
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What opportunities might it lead to?
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).
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