M.Sc. Computer Science - Data Science

Course Description

The M.Sc. in Computer Science has a common set of entry criteria and leads to a Master's degree in Computing, specializing in one of four exciting areas: Data Science, Intelligent Systems, Augmented & Virtual Reality and Future Networked Systems. The course is designed and taught by staff who are leading experts in their fields, and the course content is inspired by their cutting-edge work as well as their contacts with leading industry researchers around the globe. We expect our graduates to be in high demand for high-end research and development positions within leading multi-national companies and start-up companies alike. In some cases our graduates have gone on to take up funded PhD studies at TCD.

Data Science or Big Data has become a hugely important topic in recent years, finding applications in Healthcare, Finance, Transportation, Smart Cities and elsewhere. In this strand, Trinity's leading experts in this field will guide you through how to gather and store data (using IoT and cloud computing technologies), process it (using advanced statistics and techniques such as machine learning) and deliver new insights and knowledge from the data.

The course is taught over a full calendar year, with two 12-week semesters of taught modules, involving attendance at labs and lectures, followed by dedicated research work over the remaining summer months for the MSc Dissertation.

In the first term (September - December), all students gain the necessary skills in a number of Core Modules common to the M.Sc. Programme. These include Research Methods (to enable students to produce their own dissertation), Innovation (to equip students with skills in company formation or innovating within a large company) and Machine Learning (a foundational technique for each of the specializations). In addition, students will make a start on specialist modules in their chosen strand, learning the key techniques of Data Mining & Analysis including classification techniques, neural networks and ensemble methods with practical work in the R language. Additionally, students discover how large data sets might be gathered and manipulated in large cloud computing facilities in the Scalable Computing module.

During the second term (January – March), students begin foundational work on their dissertation, and immerse themselves in further specialist modules of their chosen strand. The module on Optimisation Algorithms for Data Analysis will explore topics such as Convex optimisation, large dimension simulation. Applied Statistical Modelling will deal with many popular techniques such as Markov Chains and Monte Carlo Simulation with an opportunity to apply these techniques to a real data set. Students will learn how to reveal the insights derived from large data sets in the Data Visualisation module and cover essential crypto and security concerns of data in the Security & Privacy module. In addition, you can choose three additional electives (one in Term 1 and two in Term 2) from a pool of modules offered in the other strands of the M.Sc. programme.

The summer term (April – August) will be exclusively focused on the Dissertations, doing experimental work, building prototypes and writing up the work. By April, students will have chosen a Dissertation topic, picked and consulted with their chosen supervisor and be ready to devote substantial time to researching and prototyping your work. We expect that the top projects should deliver publishable quality papers over this period. During the year, all projects will be showcased to an industry audience comprising indigenous, small & medium employers and multinational companies.

Please note that the course content is updated on an annual basis and some changes occur from year to year. Students accepted on the course will be given formal module descriptors before the start of term.