MSc Data Science

This module provides a sound basis in data analysis. The module introduces feature engineering and selection, including variance thresholding, correlation and checking for multicollinearity. You’ll be introduced to the principal component analysis (PCA) including making sense of high dimensional data, dimensionality reduction, intuition linear algebra background and algorithm, using Pandas and Scikit-learn.

Machine learning is a sub-discipline of the Artificial Intelligence that deals with teaching the computer to act without being programmed. In this module you will learn about the tools and algorithms that can be used to create machine learning models. Big data and their economic, legal and ethical aspects are examined, along with data acquisition and pre-processing methods that are used to make these suitable for machine learning algorithms. You will also look into how large data sets should be divided into a training set and a test set.

Data engineering is a process to design, build and manage the information or “big data” infrastructure. It gives an understanding of how to develop the architecture that helps analyse and process data in the way the organisation needs it. This module will examine the entire data lifecycle, including data creation, modelling, representation, analysis, maintenance and disposal. As the majority of data is stored in databases, this module will provide an introduction to various types of databases and discuss the methods to ensure clean, reliable, and performative access to data.

Deep learning and its applications have revolutionised numerous fields in recent years. This module explores the two main areas of neural networks and deep learning. We’ll start analysing the structure of neural networks, from the theoretical aspects to the practical implementations. We will then move to training a neural network using Keras. Then, this module will explore the convolutional neural networks (CNNs) and introduce deep learning from the convolutional operator and stacking convolutional layers to regularisation, batch normalisation and data augmentation.

The module will provide an introduction to emerging techniques allowing data scientists and practitioners to study and investigate nonlinear time series. It will offer a collection of tools designed to dive deep down into underlying structures of data, allowing future data scientists to detect whether stochastic or deterministic dynamics most likely drive observed complexity. In other words, this module will teach you how to become a ‘data detective’ accumulating hard empirical evidence supporting your modelling approach.

The module builds up on previous knowledge gained in the course. It aims to test, through real life scenarios, as part of practical projects, concepts of artificial intelligence and machine learning techniques that enable a system to learn from data rather than through explicit programming. These techniques are becoming essential in business operation innovation and more generally in generating more efficient workflows.

This module supports you in the preparation and submission of a Masters Stage Dissertation. The topic may be drawn from a variety of sources including: school research groups, previous / current work experience, the company in which they are currently employed, a lecturer suggested topic or a professional subject of their specific interest (if suitable supervision is available). The chosen topic will require you to identify / formulate problems and issues, conduct literature reviews, evaluate information, investigate and adopt suitable development methodologies, determine solutions, develop hardware, software and/or media artefacts as appropriate, process data, critically appraise and present your findings using a variety of media.