In term 1 you will develop your core data scientific knowledge and skills. It is divided into three compulsory modules which span the breadth of data science, from the fundamentals of statistics and programming in Python and R, to modern machine learning and artificial intelligence. These core modules provide a strong foundation for the specialist pathways in term 2.
What will I study?
Core modules
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This module provides an introduction to statistical learning.
Topics to be covered will include
- Big data
- Missing data
- Biased samples and recency
- Likelihood and cross-validation
On successful completion of this module students will
- Understand cross-validation of sample splitting into calibration, training and validation samples.
- Be able to move to handling regression problems for large data sets via variable reduction methods such as the Lasso and Elastic Net.
- Understand a variety of classification methods including logistic and multinomial logistic models, regression trees, random forests and bagging and boosting.
- Examine classification methods that will culminate in neural networks presented as generalised linear modelling extensions.
- Understand big data using K-means, PAM and CLARA, followed by mixture models and latent class analysis.
The main goal of this module is to explore the essence of AI and Data Science, their origins, and their roles in solving real-world challenges. You'll delve into the duties and skills of data professionals, emphasising effective communication and ethical considerations. The module also covers the legal and societal impacts of AI, while promoting teamwork through hands-on projects that tackle AI and Data Science challenges. Supported by industry talks, you'll learn to formulate problem statements, select appropriate methods, and communicate findings effectively, preparing you for a successful career in this dynamic field.
The main goal of this module is to equip you with essential Python programming skills and foundational mathematical concepts crucial for AI and Data Science. Through hands-on learning, you'll develop the ability to solve real-world problems, process complex data sets, and apply key mathematical techniques like probability and matrix operations. Formative assessments will support your learning, leading to a final practical assessment that prepares you for advanced studies. Perfect for those new to computing or mathematics, this module sets the stage for your success in AI and Data Science.
Course structure
The Data Science MSc is structured into three distinct terms. Your path through the course has a series of options depending on both your academic background and your choice of modules.
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Term 2: Optional modules
Term 2 allows for specialism in either advanced technical or application areas. Applications offered are those for which there is a considerable demand for data scientists. The specific pathways are delivered by subject-specific experts from across the University.
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Term 3: Dissertation and work placement
In the third term, you will complete a substantial dissertation project. The majority of our students base their dissertations on 12-week placement projects hosted by our partner organisations. Host organisations will generally provide you with a bursary of £3,000.
