Overview
Building High-Demand Skills and New Capabilities for IR 4.0
The programme aims to produce graduates to meet the growing demand for data science professionals who are capable of making decisions based on the availability of comprehensive data. It prepares graduates to apply analytics techniques for knowledge discovery and dissemination to assist researchers or decision-makers in achieving organisational objectives.
Objectives
The objectives of the Master of Data Science are to produce graduates who are able to:
- Apply quantitative modelling and data analysis techniques to find solutions to real world business problems
- Communicate findings, and effectively present results using data visualisation techniques
- Recognise and analyse ethical issues in business related to intellectual property, data security, integrity, and privacy
- Demonstrate knowledge of statistical data analysis techniques utilised in decision-making.
- Use data mining software to solve real-world problems
- Employ cutting-edge tools and technologies to analyse Big Data
- Apply algorithms to build machine intelligence
- Demonstrate skills in teamwork, leadership and decision-making
Why study Master of Data Science at HELP’s ELM Graduate School?
ELM is the acronym for Entrepreneurship, Leadership and Management. It reflects the School’s understanding of the multifaceted role of both individuals and organisations that at any one time the trinitarian role of entrepreneurship, leadership and management interplays to create, manage and sustain a business over different phases of its life.
The ELM Framework underlies our philosophy of the ELM Graduate School executive education and the way we teach and learn business in the HELP Group. It synthesises relevant theoretical constructs and integrates them into a practical system of decision-making for ELM. It is a useful tool for facilitating thinking out the right business model and strategy execution.
The ELM approach is used in our teaching and learning in the ELM Graduate School. This is the multi-perspective lens that we use to identify, respond and adapt to education, enterprise and execution.
This unique framework shapes our postgraduate programmes and brings depth and practical value to the knowledge and experience that all our students will no doubt gain.
Programme Overview
Structure
These are the modules that you will take as part of the programme.
Modules
- Programming for Data Science
- Data Management
- Statistics for Data Management
- Research Methods
- Applied Machine Learning
- Dissertation
You will need to fulfill one of these entry requirements to join the programme.
Entry Requirements
- A Bachelor’s degree (Level 6, MQF) in Computing or related fields with a minimum CGPA of 2.50, as accepted by the HEP Senate; OR
- A Bachelor’s degree (Level 6, MQF) in Computing or related fields or equivalent with a minimum CGPA of 2.00 can be accepted subject to a minimum of FIVE (5) years of working experience in the related fields and rigorous internal assessment; OR
- Candidates without a qualification in the related fields or relevant working experience
- Achieve a minimum score of 6.0 in the IELTS or equivalent.
- If a student does not meet this requirement, the HEP must offer English proficiency courses to ensure that the student’s proficiency is sufficient to meet the needs of the programme.
Here are the career pathways for graduates who complete the Master of Data Science programme.
Career Prospects
- Machine Learning Scientist
- Decision Analytics Manager
- Data Analytics Manager
- Data Scientist
- Data Innovation Manager
- Business Analyst Manager
- Business Intelligence Developer
- Data Architect
- Data Analyst
- Statistician
- Data Mining or Big Data Engineer
