Master of Science (MSc) in Data Analytics

Program Overview

The MSc in Data Analytics is a blended-learning program designed for IT graduates and professionals seeking to specialize in data analytics. It provides a comprehensive foundation in data preparation, visualization, machine learning, and big data storage and processing. Graduates are equipped with the skills and knowledge to pursue careers in data analytics across various sectors, including business intelligence, finance, healthcare, and transportation.

Program Outline

Degree Overview:

The Master of Science (MSc) in Data Analytics is a blended-learning postgraduate masters degree course designed for IT graduates and professionals, and graduates from cognate
umeric disciplines. The program aims to produce graduates who can enter roles related to Data Analytics across all sectors of the economy.

Objectives:

• To provide a progression pathway for graduates of level 8 major awards in ICT or cognate disciplines to specialize in Data Analytics.
• To award graduates with a level 9 qualification on the National Framework of Qualifications.
• To enable graduates to improve their careers by earning a qualification that helps them secure or advance in employment in intermediate and advanced industry positions specific to Data Analytics.
• To provide the IT sector with graduates who have the necessary attributes to contribute positively to the industry.
• To provide graduates with the foundation for further studies at level 10 (PhD) in Computing or related disciplines.

Outline:

Stage 1 (Taught Stage)

• Programming for Data Analytics: Concepts, problem-solving techniques, data manipulation operations, optimization, concurrency, testing, quality control, and maintenance.
Assessment: 100% continuous assessment (CA).
• Statistics for Data Analysis: Statistical methods, probability, and numerical methods.
Includes an embedded "bootcamp" of basic statistics. Assessment: 100% continuous assessment (CA), comprising three assignments.
• Data Preparation and Visualization: Exploratory data analysis, data preparation, feature selection, dimensionality reduction, bias-variance trade-off, encoding, feature engineering, data visualization, and transmission media.
Includes an embedded "bootcamp" of basic programming concepts. Assessment: 100% continuous assessment (CA), comprising three assignments.
• Machine Learning for Data Analysis: Machine learning methods, techniques, and algorithms for data analysis.
Serves as a basis for more advanced data analytics introduced in adjoining modules. Assessment: 100% continuous assessment (CA), comprising three assignments.
• Research and Professional Ethics: Knowledge, skills, and competencies in research, professionalism, ethics, and governance.
Includes embedded learning from other modules, particularly the applied data project in the final semester. Assessment: 100% continuous assessment (CA), completed throughout the module.
• Big Data Storage and Processing: Data management, storage, and processing for analysis.
Assessment: 100% continuous assessment (CA), comprising three assignments.
• Advanced Data Analysis: Development of a learning system for data analysis, building upon statistical modelling knowledge and machine learning.
Assessment: 100% continuous assessment (CA), comprising three assignments.

Stage 2 (Project)

• Data Analytics Project: Application of knowledge gained in taught modules in a structured environment, with freedom to engage with a specialist area of interest.
Includes project management tools and theory, as well as the practical implementation of these tools to formulate, plan, and deliver on a chosen area of research and application.

Assessment:

• 100% continuous assessment for all taught modules, comprising three assignments for each module.
• Peer presentation and solution demonstration for the project stage.
• Integration of assessment and module-specific assessment utilizing both group and individual work.
• Formative assessment integrated into module delivery and feedback.

Teaching:

• Blended learning format, combining on-campus and online activities.
• Online activities include live or pre-recorded lectures, independent learning, assessments, research tasks, discussion forums, simulations, quizzes, and e-portfolio work.
• On-campus activities include small group tutorials, labs, project supervision, problem-solving case studies, library research, and seminars.

Careers:

• Business Intelligence Analyst
• Data Analyst
• Data Scientist
• Data Engineer
• Quantitative Analyst
• Data Analytics Consultant
• Operations Analyst
• Marketing Analyst
• Data Project Manager
• IT Systems Analyst
• Transportation Logistics Analyst
• Financial Data Analyst
• Healthcare Data Analyst