Data Science & Analytics

Course Outline
The MSc in Data Science & Analytics, jointly offered by the Department of Computer Science and the Department of Statistics, provides an education in the key principles of this rapidly expanding area. The combination of sophisticated computing and statistics modules will develop skills in database management, programming, summarisation, modelling and interpretation of data. The programme provides graduates with an opportunity, through 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).

Why Choose This Course
The MSc in Data Science and Analytics is a significant collaboration between the Departments of Computer Science and Statistics; designed to provide graduates with the skills and knowledge required to help companies and public bodies deal with ever increasing and complex data. The programme emphasises the application of Computer Science and Statistics methodologies helping transform data into useful information that can support decision making.

Check out our blog to find out what our graduates have to say about the course at:

Entry requirements

Candidates must have:
1.Obtained either a honours level 8 primary degree (minium 2H1 honours or equivalent) in computer science or mathematical sciences or honours level 8 primary degree (minium 2H1 honours or equivalent) 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 2H1 in appropriate modules.

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

Applicants who do not have a primary degree will only be considered with a minimum of 10 years 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 interview.

Candidates, for whom English is not their primary language, should possess an IELTS score of 6.5, with no individual section lower than 6.0.

English Language Requirements
Applicants that are non-native speakers of the English language must meet the university approved English language requirements available here.

For applicants with qualifications completed outside of Ireland
Applicants must meet the required entry academic grade, equivalent to Irish requirements, please find our grades comparison by country here.

International/non-EU applicants
For full details of the non-EU application procedure please visit our how to apply pages for international students. In UCC, we use the term programme and course interchangeably to describe what a person has registered to study in UCC and its constituent colleges, schools, and departments.

Not all courses are open to international/non-EU applicants, please check the fact file above.

For more information please contact the International Office.

Assessment Info

Full details and regulations governing Examinations for each programme will be contained in the Marks and Standards 2016 Book and for each module in the Book of Modules 2016/2017.

Postgraduate Diploma in Data Science and 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 and Analytics.

Subjects taught

PART 1 (60 credits)
Core Modules (30 credits)
CS6405 Data Mining (5 credits) – Dr Alejandro Arbelaez, Semester 2
CS6421 Deep Learning (5 credits) – Prof Gregory Provan, Semester 2
ST6030 Foundations of Statistical Data Analytics (10 credits) - Dr Michael Cronin & Dr Supratik Roy, Semester 1
ST6033 Generalised Linear Modelling Techniques (5 credits) - Dr Michael Cronin, Semester 2

Database Modules
Students who have adequate database experience take:
CS6408 Database Technology (5 credits) - Mr Humphrey Sorensen, Semester 1

Students who have not studied databases take:
CS6503 Introduction to Relational Databases (5 credits) - Dr Kieran Herley, Semester 1

Elective Modules (30 credits) - All selections are subject to the approval of the programme coordinator

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) - Dr Steve Prestwich, Semester 1
CS6409 Information Storage and Retrieval (5 credits) - Mr Humphrey Sorensen, Semester 2
CS6420 Topics in Artificial Intelligence (5 credits) – Prof Barry O'Sullivan
ST6034 Multivariate Methods for Data Analysis (10 credits) - Dr Michael Cronin & Dr Supratik Roy, Semester 2
ST6035 Operations Research (5 credits) – Dr Brett Houlding, Semester 1
ST6036 Stochastic Decision Science (5 credits) – Dr Kevin Hayes, Semester 2
ST6040 Machine Learning and Statistical Analytics I (5 credits) -Dr Eric Wolsztynski, Semester 1
ST6041 Machine Learning and Statistical Analytics II(5 credits) -Dr Eric Wolsztynski, Semester 2

Programming Modules
Students who have adequate programming experience take:
CS6422 Complex Systems Development (5 credits) – Dr Klaas-Jan Stol, Semester 1
CS6423 Scalable Computing for Data Analytics (5 credits) - Dr Klass-Jan Stol, Semester 2

Students who have not studied programming take:
CS6506 Programming in Python (5 credits) - Dr Kieran Herley, Semester 1
CS6507 Programming in Python with Data Science Applications (5 credits) - Dr Kieran Herley, Semester 2

PART 2 (30 credits)
A student who obtains an aggregate mark of at least 60% across the taught modules, and not less than 40% in the Dissertation in Data Science and Analytics will be eligible for the award of the MSc Data Science and Analytics.

Eligible students select one of the following modules:
CS6500 Dissertation in Data Analytics (30 credits) - Professor Gregory Provan, Semester 3
ST6090 Dissertation in Data Analytics (30 credits) - Dr Michael Cronin, Semester 3

The Book of Modules contains descriptions for all modules listed in the University Calendar. Selection of any modules is governed by the programme requirements outlined in the University Calendar for each programme.

Further details on the modules listed above can be found in our book of modules. Any modules listed above are indicative of the current set of modules for this course but are subject to change from year to year.

University Calendar
You can find the full academic content for the current year of any given course in our University Calendar.


1 year Full Time

Enrolment dates

Start Date 7 September 2020

Post Course Info

Skills and Careers Information
This programme aims to prepare students to manage, analyse and interpret large heterogeneous data sources. 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 Computer Science graduates in 2017-18 include:

Accenture, Aer Lingus, Amazon, Apple, Bank of America Merrill Lynch, Bank of Ireland, BT, Cisco, CiTi-Technology, Cloudreach, Dell, Digital Turbine Asia Pacific, Dell EMC, Enterprise Ireland, Ericsson, First Derivatives, Guidewire, IBM, Intel, Open Text, Paddy Power, Pilz, PWC, SAP Galway Transverse Technologies, Trend Micro, Uniwink, Version 1 (Software), VMware.

Starting Salaries
There is an increasing demand for graduates that can collate, interpret, manage and store large volumes of data. Graduates can be employed as analysts, database administrators, data warehouse consultants, business intelligent consultants to name but a few. Employment agencies report typical salaries ranging from €50,000-80,000 depending on industry and experience. Salaries are in general higher than many other industries.

More details
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  • Qualifications

    Degree - Masters (Level 9 NFQ)

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