Data Analytics

Overview

This Master of Science in Data Analytics has been designed to meet the growing need for graduates with data science skills in the light of increasing applications of new and existing technologies and techniques such as statistical analysis, machine learning and data visualisation across many industries throughout the global economy. Given the rapid growth in internet data usage, the shift to cloud computing, and the rate at which Irish businesses integrate data and analytics into their daily operations, Data Analytics is an identifiable discipline with a breadth and depth of content that encompasses many of the subfields (e.g. software development, machine learning, data visualisation that form the modern AI ecosystem).



The programme aims to respond to the ever-growing demand across industries for data analytics specialists involving skills in technology, programming for data analytics, data analysis with related context, graph technology considered to be effective means to empower the development of sophisticated AI applications. Additionally, the programme learning outcomes focus on the learner’s ability to meet requirements and deliver against business intelligence goals of organisations they will work in and allow for all of these outcomes to be demonstrated from an academic perspective but also to have a portfolio piece of work that can be shared with current or prospective employers.



Either full time or part time, the programme is designed to facilitate learners with computer science/data science/ technology/general science/ mathematics/statistics or related background who wish to upskill in this new and emerging area of Data Analytics. It will also be of interest to learners who have completed their undergraduate degree and wish to specialise in this area.



The specific programme aims are as follows:

• Enable learners to develop expert knowledge of current and developing analytics skills related to the development and use of analysis and statistics.

• Provide learners with a deep and systematic knowledge of business and technical strategies for data analytics and the subsequent skills to implement solutions in these areas.

• Facilitate the learner’s ability to develop applied data analytics skills relevant to the workplace.

• Identify and develop autonomous learning skills appropriate to develop specialisms in the field of analytics.

• Develop in learners a deep and systematic understanding of current issues of research and analysis relating to the field of data analytics.

• Empower learners as they identify, develop and apply detailed analytical, creative, problem solving in research scenarios relating to data analytics.

• Provide the learner with a comprehensive platform for career development, innovation and further study related to the field of data analytics.

• Respond ethically and informatively to address any emerging trends and wicked problems that may arise due to the growing needs of industry.

• Apply advanced theoretical and methodological knowledge to address a problem domain related to the field of data analytics creating an applied solution.



Structure

The full-time programme will be delivered across three semesters comprised of twelve weeks each across nine months. In the part-time mode, the schedule consists of four semesters of twelve weeks across eighteen months.



Semester one lays the groundwork for the programme and encompasses modules that focus on providing a solid and comprehensive understanding of the relevant concepts, a proficiency in the use of programming skills to gather, analyse, process and visualise data, statistics for data analytics and the application of pattern recognition in machine learning.



Semester two builds on this by covering advanced modules in which the knowledge, understanding and skills acquired in the first semester can be employed. Semester two modules offer advanced and applied skills in topics such as data and network mining, databases and data storage, graph, data visualisation including foundations in linguistics, statistical analysis and applications.



Semester three includes an Applied Research Project.



In addition, the programme aims to incorporate advanced transversal skills in each module for the professional development of learners to enhance their employability options. This will enable the learner to integrate seamlessly into an organisation by addressing skills such as leadership, problem solving, teamwork, time management and academic writing that are essential for a Level 9 graduate.

Semester 1

• Programming for Data Analysis

• Statistics for Data Analytics

• Machine Learning & Pattern Recognition



Semester 2

• Advanced Data and Network Mining

• Data Storage Solutions for Data Analytics

• Data Visualisation

• Applied Research Methods



Semester 3

• Applied Research Project

Entry Requirements

The minimum entry requirements for the MSc in Data Analytics are:

• A Level 8 honours bachelor's degree with a minimum second-class second-division (2.2) award or above in a cognate area. Cognate subjects include computer science, data science, technology, networking, information systems, engineering, general science, mathematics, statistics, data analytics or related discipline.

• A Level 8 Higher Diploma within a minimum 2.2 or above in a cognate area. Cognate subjects include computer science, data science, technology, networking, information systems, engineering, general science, mathematics, statistics, data analytics or related discipline.

• A Level 8 honours bachelor's degree with a minimum 2.2 or above in a non-cognate area plus 3-5 years' professional experience in a related field. Learners can also access this programme through RPL. Such applicants will be assessed on a case-by-case basis.



Due to the mathematical nature of the content, candidates will be required to show sufficient competency in mathematics, based on prior learning or professional experience. This can be further defined as a module of mathematics/mathematics and statistics equivalent to a minimum of 10 ECTS in their primary degree. This knowledge/qualification should be acquired within the last ten years from the date of qualification.



In addition, for applicants whose first language is not English and who have not undertaken their undergraduate degree through English the following is required:



The minimum requirement for a non-native English speaker is greater or equal to B2+ in the Common European Framework of Reference for Languages for Admission. Non-EU applicants, residents outside Ireland/EU, must apply directly to the International Admissions Office at DBS.

We are now accepting applications for programmes taking place in 2025.

Full-time: 1 year

Part-time: 2 years



Delivery

This is a multimodal programme. Learners are therefore timetabled to be in class or online. They will also have mandatory on demand content as contact hours that they can do in their own time and place.

Next Intake dates:

January 2025

April 2025

September 2025

Career Opportunities

The rise in employment opportunities in the area of data science is a global trend. Data Science is set up as a priority area of focus in the discipline of ICT for 2018-2023 in particular in automation and digitalisation including Data Analytics, Management, Robotics and Artificial Intelligence (including Machine Learning), Digital Platforms, Content and Applications etc.



Graduates from Master of Science in Data Analytics programme have the potential to excel in roles such as Data Analyst, Data Scientist, AI Manager, IT Manager, Data Analytics Managers in a wide variety of industries.



Within the Data Analytics space, there is a diversity of jobs requiring various levels of expertise:

Core industry roles include:

• Data Analyst

• Business Analyst

• Data Engineer

• BI Analyst



Advanced roles include specialists in:

• Data Science

• Software / Python Developer