Course Title: Data Science and Analytics
College: Science, Engineering and Food Science
Duration: 1 year Full Time or 2 years Part Time
Teaching Mode: Full-time, Part-Time
NFQ Level: Level 9
Costs: 2015/2016 Irish/EU €7,000
Entry Requirements: See detailed entry requirements
Course Code: CKR49 full time. CKR50 part time
Closing Date: See detailed application procedure section below.
Next Intake: 7th September 2015
This programme of the Department of Computer Science and the School of Mathematical Sciences provides an education in the key principles of the rapidly-developing area of Data Science and Analytics. In addition to the basic computational underpinnings of this field, a sequence of courses in probability and statistics will develop skills in analysis, summarisation and modelling of data.
The programme also allows graduates an opportunity, through development of a research portfolio, to investigate the more applied elements of the discipline. At all times the programme stresses the importance of data science, statistics and probability theory as key tools in the analysis of large-scale heterogeneous data.
Companies are currently seeking graduates with data analytics skills to fill a range of positions including firms specialising in analytics, financial services and consulting, and governmental agencies.
Below please find link to brochure for further information see our brochure at http://www.ucc.ie/en/media/academic/computerscience/documents/coursedescriptions/MScDataScienceAnalytics..pdf
NFQ Level 9, Major Award
The MSc in Data Science and Analytics is a full-time programme running for 12 months or part-time over 24 months from the date of first registration for the programme.
Students taking the course on a part-time basis will need to complete the 30 core credits plus 15 elective credits (of their choice) in year 1 and the remaining 15 credits of electives plus a 30 credit dissertation in year 2.
Students take 90 credits as follows:
Part 1 (60 credits)
Core Modules (30 credits)
If students have adequate prior programming experience, they are required to take a more advanced programming sequence (CS6406 and CS6407) in place of the introductory programming modules (CS6503 and CS6505). Similarly, if students have adequate prior database experience, they are required to take a more advanced database sequence (CS6408 and CS6409) in place of the introductory database modules (CS6503 and CS6505). All selections subject to approval of the programme coordinator.
Both CS6408 Database Technology (5 credits) and CS6409 Information Storage and Retrieval (5 credits)
Both CS6503 Introduction to Relational Databases (5 credits) and CS6505 Database Design and Administration (5 credits)
CS6405 Data Mining (5 credits)
ST6030 Foundations of Statistical Data Analytics (10 credits)
ST6033 Generalised Linear Modelling Techniques (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. Students can also choose an elective programming course (CS6501 or CS6406 plus CS6407) based on their prior training. All selections subject to approval of the programme coordinator.
CS6322 Optimisation (5 credits)
CS6323 Analysis of Networks and Complex Systems (5 credits)
CS6506 Programming in Python (5 credits) or CS6406 Large-Scale Application Development and Integration 1 (5 credits)
CS6507 Programming in Python with Data Science Applications (5 credits) or CS6407 Large-Scale Application Development and Integration 2 (5 credits)
CS6509 Internet Computing for Data Science (5 credits)
ST6032 Stochastic Modelling Techniques (5 credits)
ST6034 Multivariate Methods for Data Analysis (10 credits)
ST6035 Operations Research (5 credits)
ST6036 Stochastic Decision Science (5 credits)
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, for whom English is not their primary language, should possess an IELTS score of 6.5, with no individual section lower than 6.0.
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 2015 Book and for each module in the Book of Modules 2014/2015.
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 Professor Gregory Provan
CS6407 Professor Gregory Provan
CS6503 Dr. Kieran Herley
CS6505 Mr. Humphrey Sorensen
CS6408 Mr. Humphrey Sorensen
CS6409 Mr. Humphrey Sorensen
CS6405 Dr. Ken Brown
CS6501 Dr. Kieran Herley
CS6322 Dr. Steve Prestwich
CS6323 Professor Gregory Provan
CS6506 Dr. Kieran Herley
CS6507 Professor Barry O'Sullivan
CS6509 Mr. Adrian O'Riordan