Course Code: CKR49 Full-time
Course Title: Data Science and Analytics
College: Science, Engineering and Food Science
Duration: 1 year Full Time
Teaching Mode: Full-time
NFQ Level: Level 9
Costs: 2017/2018 Irish/EU €7,000
2017 Entry Requirements: See detailed entry requirements
Closing Date: See details in application procedure section below
Next Intake: 11th September 2017
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.
For further information see our brochure - MSc Data Science & Analytics Brochure
Students must attain 90 credits through a combination of:
- core modules (30 credits)
- elective modules (30 credits)
- dissertation (30 credits)
Students take 90 credits as follows:
PART 1 (60 credits)
CORE MODULES (30 credits) - All selections are subject to approval of the programme coordinator
- CS6405 Data Mining (5 credits) - Dr Marc Van Dongen, Semester 2
- ST6030 Foundations of Statistical Data Analytics (10 credits) - Dr Michael Cronin, Semester 1
- ST6033 Generalised Linear Modelling Techniques (5 credits) - Dr Michael Cronin, Semester 2
Students who have adequate database experience take:
- CS6408 Database Technology (5 credits) - Mr Humphrey Sorensen, Semester 1
- CS6409 Information Storage and Retrieval (5 credits) - Mr Humphrey Sorensen, Semester 2
Students who have not studied databases take:
- CS6503 Introduction to Relational Databases (5 credits) - Dr Kieran Herley, Semester 1
- CS6505 Database Design and Administration (5 credits) - Mr Humphrey Sorensen, Semester 2
ELECTIVE MODULES (30 credits) - All selections are subject to 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
- CS6323 Analysis of Networks and Complex Systems (5 credits) - Professor Gregory Provan, Semester 2
- CS6509 Internet Computing for Data Science (5 credits) - Dr David Stynes, Semester 1
- ST6032 Stochastic Modelling Techniques (5 credits) - Professor Finbarr O'Sullivan, Semester 1
- ST6034 Multivariate Methods for Data Analysis (10 credits) - Dr Michael Cronin, Semester 2
- ST6035 Operations Research (5 credits) - Professor Finbarr O'Sullivan, Semester 1
- ST6036 Stochastic Decision Science (5 credits) - Professor Finbarr O'Sullivan, Semester 2
Students who have adequate programming experience take:
- CS6406 Large-Scale Application Development and Integration I (5 credits) - Prof. Gregory Provan, Semester 1
- CS6407 Large-Scale Application Development and Integration II (5 credits) - Prof. Gregory Provan, 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)
Students select one of the following modules, which is undertaken after Semester 2 examination results are known:
Candidates must have:
- obtained either a second class honours level 8 primary degree (or equivalent) in computer science or mathematical sciences or
- a second class honours level 8 primary degree (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 2H2 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 7 or > 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.
Shortlisted applicants who do not meet the standard entry requirements will be invited for interview.
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.
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.
If you are applying with Qualifications obtained outside Ireland and you wish to verify if you meet the minimum academic and English language requirements for this programme please click here to view the grades comparison table by country and for details of recognised English language tests.
Application for this programme is on-line at www.pac.ie/ucc. Places on this programme are offered in rounds. The closing dates for each round can be found here. For full details of the application procedure click How to Apply.
All required documentation must be either uploaded to your online application, or sent in hard copy to The Postgraduate Applications Centre, 1, Courthouse Square, Galway, immediately after an application is made.
A typical 5 credit module:
- 2 lecture hours per week
- 1–2 hours of practicals per week
- Outside these regular hours students are required to study independently by reading and by working in the laboratories and on exercises.
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.
|CS6406||Dr. Steve Prestwich||Semester 1|
|CS6323||Professor Gregory Provan||
|CS6405||Dr. Marc Van Dongen||
|CS6406||Professor Gregory Provan||Semester 1|
|CS6407||Professor Gregory Provan||Semester 2|
|CS6408||Mr. Humphrey Sorensen||Semester 1|
|CS6409||Mr. Humphrey Sorensen||Semester 2|
|CS6500||Professor Gregory Provan||Semester 3|
|CS6503||Dr. Kieran Herley||Semester 1|
|CS6505||Mr. Humphrey Sorensen
|CS6506||Dr. Kieran Herley||Semester 1|
|CS6507||Dr. Kieran Herley||Semester 2|
|CS6509||Dr David Stynes||Semester 1|