Objectives
The objectives of this programme are:
- To produce students who are equipped for present and future jobs in fields such as Data Science, Data Analytics, Machine Learning, Big Data, Business Intelligence and Operations Research.
- To better integrate theory and practice through classroom presentations of theory followed by laboratory exercises and projects.
- To produce graduates with an in-depth knowledge of Data Analytics.
- To produce graduates with knowledge of Process Optimization and Operations Research.
- To produce graduates capable of pursuing advanced research in Data Analytics.
- To produce graduates who can formulate models for real-world problems and solve them.
- To produce graduates with an entrepreneurial spirit.
Entry Requirements
The minimum requirement for admission shall be a minimum GPA of 2.5 or a Lower Second Class Honours degree or its equivalent in Computer Science, Statistics or a related field, unless the Campus Committee for Graduate Studies and Research in any particular case otherwise decides.
The applicant should also have a basic knowledge of the following areas:
(a) Computer Programming
(b) Introductory Statistics
(c) Introductory Linear Algebra.
Candidates without the above background who have at least an Upper Second Class Honours degree from UWI, or its equivalent, or significant work experience in the area, are still encouraged to apply. They will be considered for entry upon successful completion of qualifying courses. The programme coordinator will choose qualifying courses for each candidate based on their academic and work backgrounds.
Course of Study
The MSc in Data Science will consist of a set of core courses, elective courses and a research project. The core courses will cover material that is essential for any Data Science graduate while the elective courses will be chosen from presently offered MSc programmes. Students will also be required to take a course on research methods that will help them with their research project. The research project will be a major component of the degree. Each student must take a total of 39 credits consisting of 18 core course credits, 12 elective course credits and a 9-credit research project.
Core Courses
Six 3-Credit Courses + 9-Credit Project = 27 Credits
The core courses have been chosen to satisfy the data science needs of the country while at the same time providing sufficient theoretical content for those wishing to pursue more advanced degrees.
- Full-time students will have to take 5 courses per semester and do their research project during the summer following their second semester.
- Part-time students can take 2-3 courses per semester and start their project once their courses are complete.
SEMESTER 1
COMP 6501 Research Methods, Entrepreneurship and Intellectual Property
COMP 6925 Applied Operations Research
STAT 6105 Probability and Statistical Methods for Data Analytics
SEMESTER 2
STAT 6106 Statistical Inference for Data Analytics
COMP 6930 Machine Learning and Data Mining
COMP 6940 Big Data and Visual Analytics
SUMMER
Elective Courses
Four 3-Credit Courses = 12 Credits
Four elective courses are required. The student must choose these from the following with the condition that at least one must be chosen from each of the disciplines (COMP and STAT).
COMP 6300 Advanced Internet Technologies
COMP 6802 Distributed and Parallel Database Systems
STAT 6170 Multivariate Analysis
