September 2025

MSc Data Science (conversion)

Master's Degree, Postgraduate, September 2025

The MSc Data Science programme is designed as a conversion master’s to introduce graduates from diverse backgrounds to the field of data science. The programme aims to provide a comprehensive foundation in data analysis, statistical methods, probability models, big data, machine learning and modern data imputation and processing technologies. Students will learn to extract insights from complex data sets, use machine learning algorithms, and apply predictive analytics in various sectors.

The course provides broad knowledge and practical skills needed to develop software systems to generate predictive, descriptive, diagnostic, preventive and prescriptive analytics from huge and complex real-world datasets. The practical skills include programming, analyses of algorithms, statistical & probabilistic techniques, neural networks, large language models, distributed processing and data science. The integral academic writing, research and project management skills and a mandatory independent dissertation/project in the final semester further underpin application of the industry-style professional approaches as well as an appreciation of future directions of technological advances and research.

The balanced academic preparation is aligned with emerging trends in job market and allows graduates to pursue in-demand and lucrative careers in IT industry or assume interdisciplinary roles by effectively combining applied computing with their first degrees. The course also provides foundations and opportunity to progress to and formally pursue specialized computer science research on a Ph.D. programme

  • All modules across the course are designed to support the transition of graduates into globally-relevant careers in Data Science and allied domains of Big Data, AI and Machine Learning.
  • Learning opportunities encourage and support the spirit of intellectual curiosity and enquiry, an ability to apply computational thinking to solve problems and the capacity for creative computing.
  • Dynamic landscape of technology offers jobs and careers beyond geographical boundaries with lucrative salaries.
  • Incessant infiltration of computers and technology in every walk of life and industry promulgates ever-increasing demand of computer science experts.
  • Progression opportunities to specialized, industry-driven research on a Ph.D. programme
  • 3 hours of contact time per 20 credit module, a total of 9 contact hours per week.

As guided by the Birmingham Newman Principles of Assessment, a wide and innovative range of coursework assessment methods will be used including both individual and group-based tasks allowing students to foster independent learning skills alongside team working, collaboration and leadership skills.

Authentic assessments and applied projects are used throughout with a real-world focus. Students will be expected to develop software applications, write technical reports and engage in reflective practice. An in-depth dissertation project demonstrating latest trends and emerging technologies in computer science will be undertaken individually.

Computer science offers an incredibly wide range of high demand careers at the forefront of software-based technologies, such as:

  • Data scientists
  • Artificial Intelligence engineers
  • Natural language processing engineers
  • Information systems developers
  • Distributed data processing administrators
  • Healthcare informatics experts
  • IT consultants/project managers
  • Systems architects
  • Distributed applications developers
  • Big Data analysts, and;
  • Financial technology experts etc.
Register your interest

Entry Requirements

  • A relevant honours degree 2:2 or above
  • IELTS (Academic) overall 6.5 with no individual component less than 6.0

Course Fees

UK home students

The full-time course fee, for UK home students, for January 2025 is £8,900 per year.

International students

The course fee for international students is as follows:

Academic year 2024/2025
  • Postgraduate Taught: £13,500 tuition fee
  • International Excellence Scholarship: £2,500

All International Students who apply in 2024/25 will be eligible for the International Excellence Scholarship which will be automatically applied to their offer as a tuition fee reduction.

Academic year 2025/2026
  • Postgraduate Taught: £16,000 tuition fee
  • Undergraduate Taught: £14,000 tuition fee
  • Scholarships: TBC

International students must pay fees and deposit amounts as instructed within their offer letter.

The University will review tuition fees and increase fees in line with any inflationary uplift as determined by the UK Government, if permitted by law or government policy, in subsequent years of your course. It is anticipated that such increases would be linked to RPI (the Retail Price Index excluding mortgage interest payments).

Modules

Please be aware that, as with any course, there may be changes to the modules delivered. For information view our Changes to Programmes of Module Changes page.

  1. This module develops the theoretical, mathematical and practical foundations of algorithms in Computer Science. The time and space trade-offs and their relation to size and nature of inputs are fundamental in all software applications of programming, databases and distributed computing, machine learning, computer vision, deep learning, natural language processing, big data analytics, cryptography and information retrieval etc.
  2. This module develops the mathematical and statistical underpinnings of artificial intelligence and data science domains. These fundamental concepts are used for analysis of different datasets for forecasting the values, predicting the unknowns, relating the variables for getting deeper insights and indicating data differences with real world complexities. Students will gain knowledge and hands-on experience using Python programming language by implementing specific algorithms for extraction and selection of statistical features, data curation, interpolation and extrapolation, kernel methods, probabilistic reasoning and graphical models, likelihood estimations, dimensionality reduction, principal components, discriminant analyses, singular value decomposition, auto-regression, moving averages, penalised cost functions, generalization, regularization and inference.
  3. This module develops the theoretical and practical skills of technology of Big Data – massive amounts of information that necessitate software systems and resources with significantly enhanced storage, communication and processing and analysis algorithms beyond the capabilities of traditional databases and OLAP. The module introduces the programming paradigm and mindset that are required in this emerging field. Additionally, the module serves as a deep dive into indispensable backend technologies to address the immediate needs of the industry.
  1. This module develops the theoretical and practical knowledge of design and development of distributed systems that operate on various devices, from cloud services to servers to smartphones. The module presents concepts of models of distributed systems, distributed file systems, load balancing, replication and consistency, emerging trends and challenges.
  2. This module will provide students opportunity to understand and apply computational techniques to analyse and synthesize natural language and speech – Natural Language Processing (NLP). An interdisciplinary bridging of linguistics, information retrieval and machine learning will provide necessary skills to develop applications capable of comprehending, manipulating and generating natural language text and speech similar to Large Language Models.
  3. This module introduces objectives and importance of research in Computer Science, systematic literature review, problem statement and hypothesis formulation, experiment design, identifying types of variables and data wrangling, sampling techniques, quantitative and qualitative research, mixed methods of research, data imputation, types of statistical tests and evaluation measures. The module also discusses ethical constraints, intellectual property rights and legal requirements. The students are expected to conduct data analyses and present reports in a variety of formats and visualizations.
  1. Having studied core Computer Science topics, students have the opportunity to apply a range of conceptual knowledge and practical implementation tools to an in-depth development of a real-world project of their particular interest. The aim is to develop the skills expected at postgraduate level and equip Computer Science students with imperative knowledge, research & analysis skills, application of software development life cycle and critical insights into the process of transforming user requirements into practical software solutions.