Data Science & Analytics - Cork

The growth in the field of Data Science and Analytics 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 analyses. Consequently, data science and analytics have become a core component of both the public and private sectors that seek to gain a competitive advantage.



The MSc in Data Science and Analytics at MTU is a collaboration between the Department of Mathematics and the Department of Computer Science, which 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 required by employers. The programme will equip graduates with the skills to gather, store and process data using advanced techniques such as machine learning, deep learning and statistical modelling to deliver new insights and knowledge from collected data.



The programme has been designed with industry experts to ensure that students develop core skills in programming, database management, statistical modelling, time series analysis, machine learning, and data visualisation in the first two semesters. 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 synthesise and apply the knowledge, skills and competences acquired in the taught modules to research and development applied to a real-world data science problem.



Award

Master of Science in Data Science & Analytics

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 Statistics and Computer Science, with the signature module DATA9009 Data Science Principles overarching the other five modules of this semester.



Semester Schedules

NB The 2024/25 semester schedules are available here. However, please note that a number of changes have been approved by the Academic Council for 2025/26. The updated schedule is shown below. Please monitor this page for updated module descriptors.



Semester 1



Module Code / Title

DATA9009 Data Science and Analytics (5 ECTS)

STAT8010 Introduction to R for Data Science (5 ECTS)

COMP8060 Scientific Programming in Python (5 ECTS)

DATA9008 Practical Data Management (5 ECTS)

STAT9012 Statistics & Probability (5 ECTS)

STAT9005 Time Series Analysis (5 ECTS)



Semester 2



Module Code / Title

STAT9004 Regression Analysis (5 ECTS)

DATA9005 Data Visualisation & Analytics (5 ECTS)

COMP9060 Applied Machine Learning (5 ECTS)

STAT9014 Multivariate Modelling (5 ECTS)

STAT9013 Data Mining and Statistical Modelling (5 ECTS)

MATH9001 Research Methods (5 ECTS)

In Semester 3, the MSc learner undertakes one capstone project module (DATA9003 Research Project – Data Science) worth 30 credits.



Contact Hours

Semester 1

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.)



Semester 2

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.)



Semester 3

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.



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.



Supporting documents such as transcripts, etc.



Submit a 500 word statement, detailing motivation for applying for the programme and how it will enable them to meet their 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 also includes an interview.



What is RPL?

Recognition of Prior Learning (RPL) is when formal recognition is given for what you already know prior to starting on a programme or module. With recognition of prior learning the focus is on learning and not on experience as such. You can apply for RPL in any MTU accredited programme or module. Programmes which are accredited by professional bodies or any external awarding bodies may have their own procedures for RPL which you should refer to.

1 year full-time.

Career options

Graduates of this programme will be of high academic and practical standards, having gained key knowledge, skills and competencies 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.