Course Title: Master of Science in Computer Science (Data Science)
The minimum admission requirements for entry to the course are a B.Sc. (Honours) in Computer Science, Mathematics or other suitably numerate discipline with computing as a significant component. The degree should be at the level of Honours 2.1 or better or at Honours 2.2 or better with at least 2 years of relevant work experience. Applicants with other qualifications at Honours 2.1 or better level and relevant experience may also be considered.
Applicants must present a minimum IELTS English proficiency score of 6.5 overall with at least level 6.0 for each component.
Note: Due to the considerable competition for our postgraduate courses satisfying the minimum entry requirement is not a guarantee of a place. Depending on the course of study applications will be assessed based on academic grades and any work/life experience. Applicants may also be required to attend for interview.
The MSc in Computer Science (Data Analytics) programme aims to produce graduates with the knowledge and skills to work with large amounts of raw data and extract meaningful insights from it. Graduates are equipped with deep technical skills (in data management, data mining, probability and statistics, and machine learning), but also with the softer skills (in communications, research and problem solving) required to work effectively within organisations.
Data analytics has been highlighted in a range of recent reports as an area of strategic importance both nationally and internationally. Areas in which opportunities for data analytics practitioners exist include retail, financial services, telecommunications, health, and government organisations. Specific roles include but are not limited to:
- Data Analytics Consultant
- Data Scientist
- Data Analyst
- Data Architect
- Database Administrator
- Data Warehouse Analyst
- Business Intelligence Developer
- Business Intelligence Implementation Consultant
- Business Analyst
- Reporting Analyst
Specialist Core Modules
- Probability & Statistical Inference
- Machine Learning
- Working with Data
- Data Management
- Data Mining
- Data Visualisation
Critical Skills Core Modules
- Research Writing & Scientific Literature
- Research Methods and Proposal Writing
- Research Project & Dissertation
Option Modules (Two required)
- Geographic Information Systems
- Universal Design
- Programming for Big Data
- Problem Solving, Communication and Innovation
- Social Network Analysis
- User Experience Design
- Deep Learning
- Speech & Audio Processing
- Linear & Generalised Regression Models
Students can also take specialist core modules from the Data Science stream as optional modules, subject to availability and schedules.
Teaching will be in the evening with classes starting at 18.00. Some critical skills modules are scheduled on a Saturday. Part-time students can progress through the course at their own pace.
The recommended pathway to complete the part-time course in 2 years requires either taking modules two evenings with Saturdays per week or for three evenings per week in each semester.
TU060 will be delivered in a blended mode with majority of learning activities delivered online with a number of onsite face-to-face touch points in each semester. These touch points include the induction event at the beginning of the academic year and face-to-face lectures and lab in weeks 1, 7 and 13 of each semester. In order to facilitate students who cannot attend, each face-to-face activity will be accompanied by an online version of the event – lectures and labs will be livestreamed from the classroom.