Get in touch
Dr. Markus Hofmann
Blanchardstown Road North
Dublin – D15 YV78
Phone: +353 (0)1 885 1083
You may find your answer below
Yes, the course fee is the same for all applicants, regardless of your location.
If your Degree is from outside Ireland/UK you must apply to Quality and Qualification Ireland (QQI) for a comparability statement indicating the level of your award on the Irish Framework of Qualifications (NFQ). You can find more information on the QQI website here. Follow the link for Learners / Foreign Qualification Recognition. Online Comparability Statements for qualifications from more than 130 countries can be downloaded from their database.
It depends on the electives you choose. All modules with the exception of Programming for Big Data can be completed without writing code – you can use existing, open source, implementations of algorithms and techniques. You may augment existing implementations with your own code, or do some scripting yourself for data cleaning or pre-processing, but that’s optional. Students who elect to do the Programming for Big Data module will require an understanding of fundamental programming concepts such as Data types, Variables, Methods, Parameters, Data structures (e.g. arrays, lists), Loops, Conditional statement, Algorithms, Hashing and Hash Tables, Classes – OO, Functions.
SQL not taught as part of the course. Prior knowledge of SQL is assumed.
There is a quite a bit of Mathematics on the course as would be expected in an MSc focused on Data Science. Students are expected to understand the mathematics underpinning a variety of data science techniques. This permits correct interpretation of results, and informs optimisations of algorithms used. For example, the Algorithms for Data Science module covers the maths behind 10+ algorithms. Some are based on statistics (e.g. Naïve Bayes, Bayesian Networks, Regression), some are based on AI / Machine Learning (Neural Networks, Decision Trees) and some are based on set theory such as Apriori and FPGrowth. The Statistics module covers statistical techniques in depth.
All practical assessment work will be completed using open source tools. There are a selection of market leading data analytics tools that have either open source versions or academic licence options. These include Rapidminer, R, Talend, Tableau, HortonWorks (Hadoop sandbox), Quantum GIS, postgreSQL, postGIS and Python IDE’s. Students may also use other tools provided they are appropriate for the task and covered by a valid license.
Attendance is not mandatory. All lectures are recorded. Lecture recordings are made available after the lecture (within one working day), and students have access to all recordings for the duration of the MSc programme.
Yes, all project work may be work related. This includes the project module in semester two (data science applications) and the MSc Research Project in semester four. We facilitate non-disclosure agreements.
No, all aspects of coursework and assessments is online. We use an online classroom for live lectures, and Moodle, our virtual learning environment, for lecture recordings and other learning resources. While you are welcome to visit the campus at any time and meet with us, many online students visit campus for the first time on graduation day.
The workload is heavy as you would expect for an MSc programme. Students have reported spending on average 12 hours per week on independent study in addition to 6 hours of lecture time. However, estimates varied quite a lot from student to student, depending on previous relevant experience they had.
There are two modules per semester. Typically, assessment for each module includes 2 or 3 literature reviews and a practical assessment to analyse a dataset(s). So in essence you are submitting work every two or three weeks. Semester 1 tends to be the hardest term as the theory can be challenging to begin with, and students are getting used to managing their time & workload. Semester 2 has a more practical focus as one of the modules is a project module, and by semester 3 students tend to be quicker at writing papers, and are more comfortable with data science topics having covered a lot of the ground work already.