Statistics with Data Analytics MSc

Compulsory

• Probability and Stochastics
  This module aims to equip students with the basic measure-theoretic and probabilistic concepts and techniques needed for them to be able to apply the modern mathematical theory of finance, and to enable students to use methods of stochastic calculus based on Brownian motion in such a way that they are able to carry out the necessary mathematical manipulations and calculations required for use and critical assessment of the various financial models introduced in other modules of the programme.
• Research Methods and Case Studies
  The aims of this module are to develop students’ knowledge and critical awareness of a variety of research methods, to encourage students to develop critical thinking skills and transferable skills appropriate to their discipline, to enable students to develop an understanding of the current needs of industry and commerce, and to prepare students for their dissertation.
• Computer Intensive Statistical Methods
  This module aims to introduce the students to a range of computational intensive statistical methods, to further develop their skills in correct interpretation and clear reporting of results, and to enable the students to create algorithms for regression models (parametric regression and nonparametric regression) to cope with massive data.
• Quantitative Data Analysis
  The aim of this module is to develop knowledge and skills of the quantitative data analysis methods that underpin data science. Content covers a practical understanding of core methods in data science application and research, such as bivariate and multivariate methods, regression and graphical models. A focus is also placed on learning to evaluate the strengths and weaknesses of methods alongside an understanding of how and when to use or combine methods.
• Big Data Analytics
  The aim of this module is to develop the reflective and practical understanding necessary to extract value and insight from large heterogeneous data sets. Focus is placed on the analytic methods/techniques/algorithms for generating value and insight from the (real-time) processing of heterogeneous data. Content will cover approaches to data mining alongside machine learning techniques, such as clustering, regression, support vector machines, boosting, decision trees and neural networks.
• Dissertation
  The dissertation aims to enable students to develop a robust understanding of more advanced and practical issues in statistics and data analytics of their choosing, and to demonstrate a students’ ability to engage critically and analytically in literature from the field, building upon relevant concepts and theory covered in the taught element of the degree.
• Financial Markets
  This module aims to equip students with the basic concepts of financial markets, including market terminology and conventions, required for other modules of the programme, and to enable candidates to perform calculations to obtain solutions to basic portfolio optimisation problems using a range of models and techniques.
• Time Series Modelling
  This module aims to equip students with the ability to employ different methods for modelling and forecasting time series data, in particular in the context of financial data and forecasting financial risk, and to enable students to apply a range of models and tools to make financial decisions such as risk assessment.

Optional

• Financial and Economic Data Analytics

  This course is designed to provide students with fundamental knowledge and skills of how to handle and analyse big data with a focus on financial applications. Students will also gain knowledge on designing and applying machine learning predictive models in finance.

• Fundamentals of Machine Learning
  This module aims to equip students with the knowledge and ability to use modern regression and classification methods with different types of data, to enable students to apply a range of models and tools to variable selection and model selection.