Data Science & Analytics - Cork Campus
The growth in the field of Data Science has been enormous in recent years with a growing demand for practitioners in a variety of industries. With the ever increasing growth in data generation and collection, industries now rely heavily on appropriate analysis. Consequently, data science and analytics has become a core component of both the public and private sectors who seek a competitive advantage.
The MSc in Data Science and Analytics at MTU Cork, a collaboration between the Department of Mathematics and the Department of Computer Science, aims to develop highly skilled and competent graduates in the rapidly expanding field of Data Science. This collaborative programme provides graduates with a thorough grounding in the theory of data science, while allowing graduates to become highly proficient in the technical and practical skills of a modern data scientist. The course will equip graduates with the skills to gather and store data, process data using advanced statistics and techniques such as machine and deep learning and deliver new insights and knowledge from the collected data.
The programme has been designed with industry experts to ensure that in the first two semesters graduates develop core skills in programming, database management, statistical modelling, time series analysis, machine learning, and data visualisation. Students will also learn how to interpret these results to improve business performance. The capstone research project in Semester 3 is a key component of this MSc programme. The project allows the learner to apply the knowledge, skills and competences acquired in the taught modules to research and development applied to a real-world data science problem.
The full-time MSc in Data Science & Analytics will run over three semesters, i.e. fifteen months. Each semester accrues 30 credits.
The Semester 1 schedule consists of six taught modules (each 5 credits). The learner acquires a foundation in Mathematics, Statistics and Computer Science, with the signature module DATA8001 Data Science & Analytics overarching the other five modules of this semester.
Module Code Module Title
DATA8001 Data Science and Analytics
MATH8009 Mathematical Methods and Modelling
DATA8002 Data Management Systems
DATA8003 Unstructured Data & Visualisation
COMP8042 Analytical and Scientific Programming
STAT8006 Applied Stats & Probability
It is during the second semester that the specialisation into the "Big Data" space takes effect, with modules in Statistics for Big Data, Data Visualisation, Distributed Data Management, Time Series, and Machine Learning.
Module Code Module Title
STAT9004 Statistical Data Analysis
DATA9001 Data Visualisation & Analytics
DATA9002 Distributed Data Management
STAT9005 Time Series & Factor Analysis
COMP9060 Applied Machine Learning
MATH9001 Research Methods
In Semester 3, the MSc learner undertakes one capstone project module (DATA9003 Research Project – Data Science) worth 30 credits.
24 contact hours per week. The learner should also expect to invest at least 18 hours per week in independent learning (independent study, preparation of coursework, etc.)
23 contact hours per week. The learner should also expect to invest at least 19 hours per week in independent learning (independent study, preparation of coursework, etc.)
The learner will work full-time on the 30 credit project. Typically, this involves a workload of 42 hours per week, to include meeting with MTU project supervisor.
The entry requirements for the MSc programme are
2.1 in a Level 8 Honours degree
Alternatively, graduates with a 2.2 Honours degree will be considered, subject to having three years relevant experience. See rpl for further details.
The language of academic instruction as well as administration is English. Non-native speakers of English require a minimum IELTS score of 6.0 to be considered for entry into this postgraduate programme.
Appropriate EFL training courses are offered by MTU Cork to applicants who meet the academic programme entry requirements but who need to increase their proficiency in the English language.
Applicants are also required to
Provide an up to date CV with their application
Submit a 500 word statement, detailing motivation for applying for the programme and how it will enable them to meet their career goals
The application process also includes an interview.
Application Closing Date:1st September
Please note that applicants will be required to pay an acceptance fee of €757 online if a place on a course is offered. This fee is deductible from the overall course fee.
Note: You will not be charged for applying for the programme by clicking the 'apply now' button, you are only asked to pay an acceptance fee if a place is offered to you and you wish to accept it.
Please upload the following documents when making your application
A current Curriculum Vitae
Supporting documents such as transcripts, etc.
Personal Statement - please include a 500 word personal statement detailing what has motivated you to apply the programme and how the programme will enable you to meet your career goals
Any further information which you think would support your application, for example, information in regard to Higher Level grades in Leaving Certificate Mathematics, Leaving Certificate Applied Mathematics, Leaving Certificate Physics
The application process may also include an interview.
M.Sc: 90 ECTS
Full-time: 3 semesters
Semester 1 – September 2021 to January 2022
Semester 2 – January 2021 to June 2022
Semester 3 – September 2022 to January 2023
EU Applicants: €6,500
Non-EU Applicants: €12,000
Contact the college for the next start date.
Post Course Info
Graduates of this programme will be of high academic and practical standards having gained key knowledge, skills and competences in Data Science, Machine Learning, Statistics, and Computer Science. Graduates will experience first hand the application of their newly developed skills to solving real-life problems.
Our alumni have gone onto a wide variety of roles as data scientists, as well as other technical and leadership roles in high tech industries such as financial services, pharmaceuticals, IT, brewing, consulting, official statistics, retail analytics, data science banking, and many more.