MSc in Data Analytics
Course Overview
This Master of Science in Data Analytics is a one-year full-time or two-year part-time Level 9 programme primarily aimed at students with strong numeracy skills who wish to extend their skillset to a more advanced level of theoretical knowledge and practice and to further develop their research capabilities and innovation skills in the area of Data Analytics. The programme takes an integrative approach, focusing on the synthesis of knowledge and practice from specialist areas within the data analytics and computing domains.
The aim of this programme is to produce highly-skilled graduates with expertise that cuts across the core disciplines of Mathematics, Statistics and Computer Science. It emphasises the critical connection from data to information, from information to knowledge, and from knowledge to decision making, encompassed in the Data-Lifecycle.
The educational aim is to develop students' analytical, critical thinking, problem-solving and communication skills and to foster their research capabilities and innovation skills in the area of Data Analytics.
The primary aim is to develop expert knowledge in aspects of data required to become highly-skilled Data Analysts: with modules in Statistics, Data Visualisation, Time Series, Data Architecture, Machine Learning, Statistical Programming and Ethics in Data.
Career Opportunities
These career paths can take place in many different sectors and industries including areas such as research; academia; business; finance; banking; healthcare; pharmaceuticals; commercialism; and technology.
Graduates from MSc in Data Analytics can find Industry roles such as Data Analyst, Data Scientist, Data Engineer, Data Architect, Business Data Analyst, Data Visualisation developer and Statistician.
Course Delivery and Modules
This course is delivered using a mix of lectures, workshops, labs, tutorials and independent reading, continuous assessment projects etc.
Full-time course is delivered over 3 semesters in 1 year (including summer). The part-time course takes place over 2 years.
-
Semester 1
- Data Architecture (part 1 of 2): (10 credits) provides a programming framework that would assist in solving big data problems in a distributed computing environment.
- Statistics (10 credits) is to build on the fundamental of mathematics and statistics needed for the masters whilst learning how to begin to apply these techniques to real data.
- Programming for Data Analytics (10 credits) introduces the programming tools in Python for Data Analysis that would assist students in subsequent modules of this programme.
- Research Process for Data Analytics (5 credits) focuses on developing good research practice and prepare students for the research process required for the dissertation.
-
Semester 2
- Data Architecture (part 2 of 2) (10 credits)
- Data Visualization and Insight (5 credits) and Insight aims to employ statistical tools to analyse data and communicate meaningful information effectively, accurately and efficiently.
- Time Series Analysis (5 credits) focuses on the analysis of data taken at regular time intervals, with real data applications in topics such as economics and finance.
- Machine Learning (10 credits) provides the knowledge and the applications of ML algorithms including Neural Networks.
- Ethics in Data Analytics (5 credits) familiarises students with GDPR and builds awareness of the moral and social responsibility in the field of Data Science.
-
Semester 3
- Dissertation (30 credits) gives the graduate the ultimate learning experience, the student gets the first-hand experience of what is involved in mastering data analysis, by carrying out a project using real-world data from industry or research to solve data-rich problems.
Fees and Funding
EU Fees:
- Full-time (EU): €6,950
- Part-time (EU): n/a
Non-EU Fees:
- Full-time (EU): €12,000
- Part-time (EU): n/a
You may be eligible for financial support for this course through the Student Universal Support Ireland (SUSI) system. Find out more
Fees
€6,950
€12,000
Entry Requirements
Standard Entry Requirements:
The standard minimum entry requirement is a 2.2 Honours Degree (NFQ Level 8) or equivalent in the area of Mathematics, Statistics, Computing, Engineering, Science or equivalent qualification in a cognate discipline, which included a minimum of 15 credits in Mathematics and Statistics and 10 credits of programming centric modules. The Higher Diploma in Science in Data Analytics Level 8 award is seen as an entry route to this programme.
International Student Entry Requirements:
International applicants must meet the minimum entry requirements for the programme in addition to providing evidence of proficiency in English. The English requirements are as follows:
- Grade B2 equivalent on CEFR (Common European Framework of Reference for Languages) OR have achieved IELTS 6.5 (or internationally-recognised equivalent). Where an applicant demonstrates evidence of having attained his/her Bachelor Degree through instruction in the English language, a grade of 6.0 may be accepted for admission to a STEM programme.
Shortlisted candidates may be invited for an interview.
How To Apply
Apply Directly to DkIT
Apply directly to DkIT using our online applications system. Please remember to upload the required documentation (copies of your existing qualification(s), your CV, any other support documents etc) with your online application to expedite your application. To avoid disappointment early registration is recommended.
International Application (non-EU)
International Applicants (not from or living in the EU) can apply through an agent or directly to DkIT to study this course.
Ask us a Question
If you have a question about the MSc in Data Analytics please ask it below and we will get back to you.
Disclaimer: All module titles are subject to change and for indicative purposes only. All courses are delivered subject to demand and timetables are subject to change. Elective Module options will only run subject to student numbers. The relevant Department will determine the viability of each elective module option proceeding depending on the number of students who choose that option. Students will be offered alternative elective modules on their programme should their preferred elective option not be proceeding. Award Options for Common Entry Programmes: The relevant Department will determine the viability of each award option proceeding depending on the number of students who choose either option. If the numbers for one of the Award options exceed available places, students for this option will be selected based on Academic Merit (highest grades).
