Data Science and Analytics

Overview

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 has become a core component of both the public and private sectors who 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 course will equip graduates with the skills to gather, store and process data using advanced techniques such as machine, 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 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.

Duration

Three semesters

• Semester 1 – September 2023 to January 2024
• Semester 2 – January 2024 to June 2024
• Semester 3 – June 2024 to September 2024 OR September 2024 to January 2025

Award       

Master of Science in Data Science & Analytics

What will I study?

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. 

Semester 1

Module Code / Title

• DATA8001 Data Science and Analytics
• MATH8009 Mathematical Methods and Modelling
• DATA8002 Data Management Systems
• STAT8010 Introduction to R for Data Science
• COMP8060 Scientific Programming in Python
• STAT8006 Applied Stats & Probability

Semester 2

Module Code / Title

• STAT9004 Statistical Data Analysis
• DATA9005 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.

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. 

Entry Requirements

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.

Fees

The following is the expected fees schedule for a typical student who begins the programme in September 2024.

• Full-time Mode of Study: EU Students: €6,500
• Non-EU Students: €15,000 

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. International students will be required to pay a deposit of €1,000. 

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.

Union of Students in Ireland (USI) Levy

The USI membership levy of €7 has been introduced as a result of a referendum where students opted to affiliate with the national Students’ Union. This levy must be paid before the start of Semester 1.

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.