Data Science & Analytics (MSc)
Field of Study
Data Science & Analytics
Type of Course:
Type of Qualification:
Type of Study:
Application Closing Date:
5th September 2018
Full-time: 3 semesters
The entry requirements for the MSc programme are
- 2H1 in a Level 8 Honours degree
- Alternatively, graduates with a 2H2 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 score of IELTS 6.0 is required for entry into postgraduate programmes. Appropriate EFL training courses are offered by CIT 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.
The following is the expected fees schedule for a typical student who begins the programme in September 2018.
- Full-time Mode of Study: EU Students: €6,500
- Non-EU Students: Please click here for information
Union of Students in Ireland (USI) Levy
The USI membership levy of €7.00 has been introduced as a result of a referendum where students opted to affiliate to the national Students’ Union. This levy must be paid before the start of Semester 1.
The need for both data scientists and “data savvy” practitioners has been well-articulated in government policy documents, going back at least as far as the Action Plan for Jobs in 2012. Following on its very successful Higher Diploma Science in Data Science & Analytics, CIT now offers a conversion MSc programme in this area.
The full-time MSc in Data Science & Analytics will run over three semesters, i.e. fifteen months.
In Semester 1, the learner acquires the necessary foundation in Mathematics, Statistics and Computer Science. From Semester 2, the specialisation into the “Big Data” space takes effect, and the learning experience is geared specifically for the realisation of Level 9 programme outcomes.
In Semester 3, the learner undertakes a 30 credit capstone project module, in which they apply the knowledge, skills and competences acquired in the taught modules to the research and development of a Data Science problem, and successfully complete the project in accordance with a project plan.
The graduate of this MSc programme will be of high academic and practical standards, in order to match the needs of the Irish and international IT industry, especially in the “Big Data” space.
- Semester 1 – September 2018 to January 2019
- Semester 2 – January 2019 to June 2019
- Semester 3 – September 2019 to January 2020
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 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 CIT project supervisor.
This course has been designed to address the skills shortage in Data Science and Analytics, by equipping graduates with the scientific, technological, business and interpersonal skills necessary to operate professionally in this rapidly evolving interdisciplinary field.
The graduate of this programme will be of high academic and practical standards, in order to match the needs of the Irish and international IT industry, especially in the “Big Data” space. He/she will be able to ally the transferable skills obtained in his/her Level 8 degree to newly acquired knowledge, skills and competences in Statistics & Mathematics, Computer Science and Data Science, and their application to solving real-life problems. Potential job opportunities not only include that of data scientist/analyst, but also skilled staff who will be required to extract actionable insight from large amounts of raw data in order to enable better decision making within an organisation.
- 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.
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.