DATA SCIENCE, AI & MACHINE LEARNING.
The programme is crafted for busy professionals; you can keep up with work at your own pace and your own convenience. Tadiwanashe Muganyi . MSc Global Central Banking and Financial Regulation, Graduate .
Who would benefit from this module? The module is aimed at both individuals looking to acquire hands-on experience with machine learning techniques and looking to improve their coding skills. It provides an intuitive introduction to machine learning for managers or critical consumers of projects with machine learning components.
Module overview . This module covers the foundations of big data and machine learning. You will gain a complete introduction to machine learning for managers or critical consumers of projects with machine learning components. You will critically analyse machine learning techniques used to forecast the state of the economy, such as inflation and the risk of recession. You will also discuss the special ethical challenges which arise in banking, including issues related to data privacy associated with machine learning and AI. You will learn how to visualise data, and how to apply machine learning techniques for regression, classification, and clustering. We will move from the fundamentals of computer programming to implementing state-of-the-art neural networks. Through this module, you will gain hands-on experience to acquaint non-specialists with current machine learning techniques, using the R programming language. Faculty This module is led by Dr Caspar Kaiser (Associate Professor, Behavioural Sciences group, Warwick Business School).
Key benefits .
Learn to think like a data scientist . Explore data analytics and visualisation through machine learning techniques. Hands on coding experience . Gain practical experience in coding using RStudio, the industry standard for data analysis and visualisation. Understand key issues and algorithms at an intuitive level . Learn how machine learning works – both intuitively and technically. Award . Gain a Postgraduate Award upon successful completion.
Module structure . The module lasts for 17 weeks
and culminates in an assessment. Module materials are organised into 10 lessons – each lasting one week. You can expect to spend around 11 hours studying per lesson. Lessons include reading materials, video interviews, discussion points, reflection activities. Each lesson includes hands on activity in R using the tidymodels framework. There are four wbsLive online sessions with your tutor.
Key information .
The module will start in June 2027.
Key topics covered .
This module features a two-week induction period.
Introduction to programming using the R programming language. Overview of Machine Learning techniques used in economics and the
Theory behind supervised and unsupervised ML algorithms. How to implement and evaluate these algorithms in practice. Different
How neural nets and language models work. What the rise of advanced AI means for person coding work and its wider social and economic impacts
Watch the film
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social sciences. How to create
DataScience
interpretability techniques for complex black ML techniques.
industry-standard data visualisations.
Please note that this is not an exhaustive list and we recommend you contact us for a more detailed discussion.
wbs.ac.uk/go/banking
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Global Central Banking & Financial Regulation qualification
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