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1. Study
2. Postgraduate
3. Taught Courses
4. Masters
5. Data Science and Analytics

Course Fact File

Code	MSCDSA
Duration	1 Year Full-time
Teaching Mode	Full-time
Qualifications	MSc
NFQ Level	Level 9
Closing Date	Rolling deadline. Open until all places have been filled. Early application is advised.
Non-EU Closing Date	Friday 6 December 2024.Should more places become available it may reopen in early 2025.
Start Date	8 September 2025

Course Outline

Our MSc in Data Science and Analytics, jointly offered by the School of Computer Science and Information Technology and the School of Mathematical Sciences, provides an education in the key principles of this rapidly expanding area. The combination of sophisticated computing and statistics modules will develop your skills in database management, programming, summarisation, modelling, data visualisation, and interpretation of data. The programme provides graduates with an opportunity, through the development of a research project, to investigate the more applied elements of the disciplines.

Students must attain 90 credits through a combination of core modules (30 credits), elective modules (30 credits) and a dissertation (30 credits). 

PART I (60 credits)

Core Modules (30 credits)

• CS6405 Datamining (5 credits) 
• CS6421 Deep Learning (5 credits) 
• ST6030 Foundations of Statistical Data Analytics (10 credits) 
• ST6033 Generalised Linear Modelling Techniques (5 credits)

Database Modules

Students who have adequate database experience take:

• CS6408 Database Technology (5 credits) 

Students who have not studied databases take:

• CS6503 Introduction to Relational Databases (5 credits) 

Elective Modules (30 credits) 

Students must take at least 10 credits of CS (Computer Science) modules and at least 10 credits of ST (Statistics) modules from those listed below:

• CS6322 Optimisation (5 credits) 
• CS6409 Information Storage and Retrieval (5 credits) 
• CS6420 Topics in Artificial Intelligence (5 credits) Semester 1
• CS6426  Data Visualization for Analytics Applications (5 credits) 
• ST6034 Multivariate Methods for Data Analysis (10 credits)
• ST6035 Operations Research (5 credits)
• ST6036 Stochastic Decision Science (5 credits)
• ST6040 Machine Learning and Statistical Analytics I (5 credits)
• ST6041 Machine Learning and Statistical Analytics II (5 credits)

Programming Modules

Students who have adequate programming experience take:

• CS6422 Complex Systems Development (5 credits) 
• CS6423 Scalable Computing for Data Analytics (5 credits) 

Students who have not studied programming take:

• CS6506 Programming in Python (5 credits) 
• CS6507 Programming in Python with Data Science Applications (5 credits) 

All selections are subject to the approval of the programme coordinator.

PART II (30 credits)

• CS6500 Dissertation in Data Analytics (30 credits) or
• ST6090 Dissertation in Data Analytics (30 credits)

Postgraduate Diploma in Data Science & Analytics
Students who pass each of the taught modules may opt to exit the programme and be conferred with a Postgraduate Diploma in Data Science & Analytics.

Course Practicalities

A typical 5-credit module entails:

• 2 lecture hours per week;
• 1–2 hours of practicals per week;
• and outside of these regular hours, students are required to study independently by reading and by working in the laboratories and on exercises.

Why Choose This Course

This programme entails a significant collaboration between the School of Computer Science and Information Technology, and the Department of Statistics. It is designed to provide graduates with the skills and knowledge required to help companies and public bodies deal with ever-increasing and complex data.  We emphasise the application of Computer Science and Statistics methodologies helping transform data into useful information that can support decision making. 

Skills and Careers Information

Our MSc programme aims to prepare students to manage, analyse and interpret large heterogeneous data sources. Our graduates will design, compare and select appropriate data analytic techniques, using software tools for data storage/management and analysis, machine learning, as well as probabilistic and statistical methods. Such abilities are at the core of companies that constantly face the need to deal with large data sets.

Companies currently seeking graduates with data analytics skills include: firms specialising in analytics, financial services and consulting, or governmental agencies.

Companies actively recruiting our graduates:

Accenture, Aer Lingus, Agility M3, Allied Irish Banks, Altada Technology Solutions Ltd, Amazon, Apple, Bank of America Merrill Lynch, Bank of Ireland, BT, Central Statistics Office, Cisco, CiTi-Technology, Clearstream, Cloudreach, Dell EMC, Deloitte, Deutche Bank, Enterprise Ireland, Ericsson, Ernst & Young, Ervia, Facebook, First Derivatives, Google, Guidewire, Intel, IBM, Janssen, KPMG, Logitech, Microsoft, Open Text, Paddy Power, Pfizer, Pilz, PWC, SAP Galway, Screendragon, Transverse Technologies, Trend Micro, Tyco, Uniwink, Verizon Connect,  Snipp Interactive, Version 1 (Software), Virgin One, VMware and more.

 

Requirements

Candidates must have:

1. Second Class Honours Grade I in a primary honours degree (NFQ, Level 8) in computer science or mathematical sciences or
2. Second Class Honours Grade I in a primary honours degree (NFQ, Level 8) with a strong numerate content (e.g. engineering, finance, physics, biosciences or economics). In such cases, the programme team must be satisfied that the numerate content is sufficient for entry to the programme and that applicants have an aggregate grade of a Second Class Honours Grade II in appropriate modules.

Applicants who do not meet the above standard entry requirements will also be considered under Recognition of Prior Learning (RPL) if they have an undergraduate degree (NFQ, Level 8) and a minimum of 5 years of verifiable relevant industrial experience.

Applicants who do not have a primary degree will only be considered with a minimum of 10 years of verifiable relevant industrial experience.

Candidates from Grandes Écoles Colleges are also eligible to apply if they are studying a cognate discipline in an ENSEA or EFREI Graduate School and are eligible to enter the final year (M2) of their programme.

Shortlisted applicants who do not meet the standard entry requirements will be invited for an interview.

International Applicants

To be considered for an offer, you must provide all the required documents at the time of application:

1. If you are required to present an English language proficiency test, then you must present the certificate that meets the entry requirements at the time of submission for the application to be considered. Applications without a valid English certification will be automatically refused.
2. Check the entry requirements: You must meet the required entry academic grade, equivalent to Irish requirements. For more information see our Qualification Comparison page.
3. Only then start the application by clicking on the ‘Apply Now’ button at the end of the page.

Fees and Costs

How To Apply

The closing date for non-EU applications is Friday 6 December 2024.Should more places become available it may reopen in early 2025.

Apply Now