PART 1 (60 credits)
Core Modules (30 credits)
CS6405 Data Mining (5 credits) – Dr Alejandro Arbelaez, Semester 2
CS6421 Deep Learning (5 credits) – Prof Gregory Provan, Semester 2
ST6030 Foundations of Statistical Data Analytics (10 credits) - Dr Michael Cronin & Dr Supratik Roy, Semester 1
ST6033 Generalised Linear Modelling Techniques (5 credits) - Dr Michael Cronin, Semester 2
Database Modules
Students who have adequate database experience take:
CS6408 Database Technology (5 credits) - Mr Humphrey Sorensen, Semester 1
Students who have not studied databases take:
CS6503 Introduction to Relational Databases (5 credits) - Dr Kieran Herley, Semester 1
Elective Modules (30 credits) - All selections are subject to the approval of the programme coordinator
Students must take at least 10 credits of CS (Computer Science) modules and at least 10 credits of ST (Statistics) modules from those listed below:
CS6322 Optimisation (5 credits) - Dr Steve Prestwich, Semester 1
CS6409 Information Storage and Retrieval (5 credits) - Mr Humphrey Sorensen, Semester 2
CS6420 Topics in Artificial Intelligence (5 credits) – Prof Barry O'Sullivan
ST6034 Multivariate Methods for Data Analysis (10 credits) - Dr Michael Cronin & Dr Supratik Roy, Semester 2
ST6035 Operations Research (5 credits) – Dr Brett Houlding, Semester 1
ST6036 Stochastic Decision Science (5 credits) – Dr Kevin Hayes, Semester 2
ST6040 Machine Learning and Statistical Analytics I (5 credits) -Dr Eric Wolsztynski, Semester 1
ST6041 Machine Learning and Statistical Analytics II(5 credits) -Dr Eric Wolsztynski, Semester 2
Programming Modules
Students who have adequate programming experience take:
CS6422 Complex Systems Development (5 credits) – Dr Klaas-Jan Stol, Semester 1
CS6423 Scalable Computing for Data Analytics (5 credits) - Dr Klass-Jan Stol, Semester 2
Students who have not studied programming take:
CS6506 Programming in Python (5 credits) - Dr Kieran Herley, Semester 1
CS6507 Programming in Python with Data Science Applications (5 credits) - Dr Kieran Herley, Semester 2
PART 2 (30 credits)
A student who obtains an aggregate mark of at least 60% across the taught modules, and not less than 40% in the Dissertation in Data Science and Analytics will be eligible for the award of the MSc Data Science and Analytics.
Eligible students select one of the following modules:
CS6500 Dissertation in Data Analytics (30 credits) - Professor Gregory Provan, Semester 3
ST6090 Dissertation in Data Analytics (30 credits) - Dr Michael Cronin, Semester 3
The Book of Modules contains descriptions for all modules listed in the University Calendar. Selection of any modules is governed by the programme requirements outlined in the University Calendar for each programme.
Modules
Further details on the modules listed above can be found in our book of modules. Any modules listed above are indicative of the current set of modules for this course but are subject to change from year to year.
University Calendar
You can find the full academic content for the current year of any given course in our University Calendar.
Comment
Course Practicalities
A typical 5 credit module:
2 lecture hours per week
1–2 hours of practicals per week
Outside these regular hours students are required to study independently by reading and by working in the laboratories and on exercises.
Full details and regulations governing Examinations for each programme will be contained in the Marks and Standards 2017 Book and for each module in the Book of Modules.
Postgraduate Diploma in Data Science and Analytics
Students who pass each of the taught modules may opt to exit the programme and be conferred with a Postgraduate Diploma in Data Science and Analytics.