Computer Science - Data Science
1 year full-time ; 140 places
The new 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, Graphics and Vision Technologies and Future Networked Systems.
The course is designed and taught by staff who are renowned research leaders in their fields. 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 top-end research and development positions within leading multi-national companies and from start-up companies alike. There will also be opportunities to progress to PhD study with many funded positions available locally.
Data Science Strand
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.
Data Science Strand Modules:
Michaelmas Term (Sept-Dec)
• Machine Learning
• Data Analytics
• Research Methods and Innovation
• Scalable Computing
• Option 1
Hilary Term (Jan-March)
• Optimisation Algorithms for Data Analysis
• Applied Statistical Modelling
• Data Visualisation
• Security & Privacy
• Option 2
• Option 3
Summer Term. (April-August)
Options 1, 2 and 3 are elective modules taken from other MSc Strands. More detail on each individual module is available at: https://scss.tcd.ie/modules/
In the first term, 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. During the 2nd term, students begin foundational work on their dissertation, and immerse themselves in further specialist modules of their chosen strand. The Summer term will be exclusively focused on the Dissertations, doing experimental work, building prototypes and writing up the work.
In addition to the core modules in the first term, you will learn the key techniques of Data Mining & Analysis including classification techniques, neural networks and ensemble methods with practical work in the R language. Finally, you will discover how large data sets might be gathered and manipulated in large cloud computing facilities in the Scalable Computing
You will build on this in the 2nd term with a course on Optimisation Algorithms for Data Analysis which will explore topics such as Convex optimisation, large dimension simulation with an opportunity to apply your new-found skills in a project using Python, R or Scala. In Applied Statistical Modelling, you 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. You 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.
By April, you will have chosen your Dissertation topic, picked and consulted with your chosen supervisor and be ready to develop substantial time 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.
Siddharth Sheshadri, (M.Sc. in Computer Science.) - Data Scientist at Optum.
“Having majored in computers and statistics, the field of data science always interested me a lot. First, I heard about the M.Sc. Computer Science programme at Trinity while attending an Irish university fair in India. The one thing that struck me about the course at Trinity, and which separated it from all other universities, was the vibrant modules within the programme. The courses were carefully structured to give students an overall knowledge about their specialisation. On reading further about the brilliant and learned faculty in the university online, I decided to pursue a Master’s programme there.”
1 year full-time
Number of Places140
Closing Date31st July 2020 - As a large number of applications for the M.Sc. in Computer Science - Data Science have already been received, regrettably, we are unable to accept any further applications. However, if places become available, applications will be reopened at the end of March/early April
The new M.Sc/P.Grad.Dip in Computer Science programme aims to produce very high quality graduates that can become leaders in high-tech industry and academic research. It will be intensive, demanding and rewarding.
For entry to the course, we require the following:
A II.1 (60-69%) grade or higher from a reputable university in Computing or strongly related discipline
A standard of English language competence that will allow full participation in coursework, classwork and other activities - this means an IELTS level of 6.5. For further details on this please visit the International Students Entry Requirements website
You need to be able to be fully competent in programming in C, C++ or Java [for Graphics and Vision Technologies, you will need to have or acquire competence in C++]
A strong work ethic and the resolve to strongly engage with the demanding programme. This means, for example, that it will be extremely difficult to do the course while holding part-time employment.