Postgraduate Studentships - Search for funding opportunities.
Postgraduate Studentships - Search for funding opportunities.
Study Details: 24 months
This MSc Data Analytics and Technologies programme is designed to develop advanced technical expertise in analysing, interpreting, and managing data within modern organisations. You will gain practical experience in areas such as data analysis, machine learning, data visualisation, and applied analytics, preparing you for roles in data science, business intelligence, and data-driven decision-making.
The programme combines hands-on technical work with real-world datasets, enabling you to extract insights, build analytical models, and communicate findings effectively. You will work with industry-relevant tools and approaches, including modern data platforms, machine learning techniques, and cloud-based data processing.
Developed with input from industry, the programme equips you with both technical depth and professional skills, including problem-solving, communication, and critical thinking, enabling you to adapt to rapidly evolving technologies.
Normally, you should have successfully completed an honours degree (or equivalent) in a relevant subject and have appropriate work experience.
The standard entry requirement for the course is a BSc (Hons) in a Computing-related area, usually with a first or upper second class classification. For applicants without a first or upper second, a lower second degree will be considered on a case-by-case basis (particularly for candidates with good relevant industrial experience).
We'll be happy to consider your application if you have non-traditional entry qualifications and relevant experience or a suitable portfolio of work that we deem a reasonable substitute for the qualifications we typically accept for this course.
Applicants from related disciplines with relevant technical or professional experience are encouraged to apply.
We'll consider applications where appropriate work experience can be demonstrated in lieu of, or in addition to, the published academic qualifications in line with the University’s Recognition of Prior Experiential Learning (RPEL) procedures.
Graduates of this programme are well prepared for roles such as:
Data Analyst
Data Scientist
Business Intelligence Analyst
Machine Learning Engineer
Data Engineer
The demand for data professionals continues to grow across sectors including finance, healthcare, government, and technology, creating strong career opportunities for graduates with these capabilities.
The programme also provides a strong foundation for further specialist study or progression to doctoral research.
Core Modules:
Research Methods
Project
Professional Practice
Data Analysis and Visualisation
NLP and GenA
Data Mining and Machine Leaming
Optional Modules:
Business Analytics
Health Informatics
Marketing Analytics
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