Students must attain 90 credits through a combination of core modules (30 credits), elective modules (30 credits) and a dissertation (30 credits).
PART I (60 credits)
Core Modules (30 credits)
CS6405 Datamining (5 credits)
CS6421 Deep Learning (5 credits)
ST6030 Foundations of Statistical Data Analytics (10 credits)
ST6033 Generalised Linear Modelling Techniques (5 credits)
Database Modules
Students who have adequate database experience take:
CS6408 Database Technology (5 credits)
Students who have not studied databases take:
CS6503 Introduction to Relational Databases (5 credits)
Elective Modules (30 credits)
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)
CS6409 Information Storage and Retrieval (5 credits)
CS6420 Topics in Artificial Intelligence (5 credits) Semester 1
CS6426 Data Visualization for Analytics Applications (5 credits)
ST6034 Multivariate Methods for Data Analysis (10 credits)
ST6035 Operations Research (5 credits)
ST6036 Stochastic Decision Science (5 credits)
ST6040 Machine Learning and Statistical Analytics I (5 credits)
ST6041 Machine Learning and Statistical Analytics II (5 credits)
Programming Modules
Students who have adequate programming experience take:
CS6422 Complex Systems Development (5 credits)
CS6423 Scalable Computing for Data Analytics (5 credits)
Students who have not studied programming take:
CS6506 Programming in Python (5 credits)
CS6507 Programming in Python with Data Science Applications (5 credits)
All selections are subject to the approval of the programme coordinator.
PART II (30 credits)
CS6500 Dissertation in Data Analytics (30 credits) or
ST6090 Dissertation in Data Analytics (30 credits)
See the University Calendar (MSc Data Science & Analytics) for further course and module content.
Postgraduate Diploma in Data Science & 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 & Analytics.
Modules
Further details on modules can be found in our Book of Modules. Any modules listed 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.
Course Practicalities
A typical 5-credit module entails:
2 lecture hours per week;
1–2 hours of practicals per week;
and outside of these regular hours, students are required to study independently by reading and by working in the laboratories and on exercises.
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