Data Science (Conversion) (Taught)

• Develop industry-relevant data science skills through a programme co-designed with industry and informed by leading research.

• Upskill or retrain with our Data Science (Conversion) MSc if you come from a varied academic or professional background.

• Transition into the data science workforce through a clear pathway designed for career changers, working professionals and international learners.

• Study flexibly through full-time or part-time options designed to fit around work and other commitments. The two modes aim to cater for various professional backgrounds, with guidance available on the mode best suited to you.

The demand for data science professionals continues to grow across industries such as finance, banking, politics and healthcare due to the increasing reliance on AI and data-driven decision-making.

What you will study
Start your journey into data science by developing the essential mathematics and programming skills that underpin the core modules of the programme. Gain experience working with real-world datasets across the entire data analysis pipeline while building strong technical knowledge in modern machine learning and statistical methods and their application in business contexts.

The course covers key practical topics, such as business analytics and data visualisation, data security, building databases and working with cloud-based technologies. You will also have the opportunity to dive deeper into specialist areas such as natural language processing, deep learning, and other advanced AI methods, allowing you to shape your expertise in this dynamic and transformative field.

You will complete a dissertation where you can further explore an area of personal interest under the guidance of an academic supervisor. Dissertation projects focus on topical, real-world problems and are often linked to cutting-edge research. Through this process, you will develop research skills, learn how to communicate effectively, and engage with the ethical and security considerations central to data science.