MSc Applied Statistics in Finance (online)

The course and thus this introductory module is aimed at graduates who have not previously studied statistics at university level. The module will provide the foundation elements of probability and statistics that are required for the more advanced classes studied later on.

This will include:

• an introduction to probability distributions
• introductory hypothesis testing
• non-parametric hypothesis testing
• linear regression
• introductory power and sample size calculations

This module will introduce the R computing environment and enable you to import data and perform statistical tests. The module will then focus on the understanding of the least squares multiple regression model, general linear model, transformations and variable selection procedures.

You can expect to cover concepts such as:

• use of functions and packages in R
• use of the tidyverse for data manipulation
• data visualisation using both base R and ggplot2
• multiple linear regression
• using variable selection techniques to cope with large data sets
• more general model comparison

This module will cover the fundamental statistical methods necessary for the design and analysis of scientific experiments. There will be an emphasis on the use of real data and the interpretation of statistical analyses in the context of the research hypothesis under investigation.

You will cover topics such as:

• analysing designed experiments such as randomised block, factorial, nested and repeated measures designs
• classification techniques such as logistic regression, nearest neighbours and discriminant analysis
• clustering techniques
• dimension reduction using principal component analysis

10 credits

You'll be exposed to a number of diverse topics in econometrics that can be used to model real financial data, with an emphasis on the analysis of financial time series. The statistical software R is introduced for financial modelling.

Topics covered will include:

• basic statistics in finance
• Time Series modelling
• financial volatility modelling
• forecasting

10 credits

This module aims to expose you to a number of diverse topics in stochastic processes that can be used to model real systems, with an emphasis on the valuation of financial derivatives. In additional to theoretical analysis, appropriate computational algorithms using R are introduced.

Topics covered will include:

• how stochastic models arise
• financial options
• the Black-Scholes equation
• simulation of financial mathematical models

You'll undertake a research project in which you'll work on a real-life data set, putting the theoretical skills you have learned into practice.

This module will develop your skills in data presentation and statistical communication. You will learn to develop data dashboards, which are increasingly used to allow key stakeholders (and the public) to gain key insights into data via interactive visualisation.

Topics covered will include:

• Creating a data dashboard in RStudio
• User interface design with respect to accessibility
• Creating interactive data visualisations which reflect a specific aim
• Reactive programming  in RStudio
• Static programming in R

10 credits

This module will cover the theory of assessing risks under uncertainty. It will focus on the practical assessment of risk using simulation methods such as Monte Carlo simulation. You'll develop skills in communicating risk to risk managers as well as formulating practical risk questions that can influence policy decisions.

You can expect to learn about:

• uncertainty and variability
• bootstrapping
• Monte Carlo Simulation
• selecting appropriate probability distributions based on given scenarios

Surveys are an important way of collecting data. This class will introduce you to the methods that are commonly used to design questionnaires and analyse data resulting from these questionnaires.

You will consider:

• how to design appropriate survey questions
• a variety of sampling methods
• analysing data for different sampling methods

This module will cover the fundamental statistical methods necessary for the application of classical statistical methods to data collected for health care research. There will be an emphasis on the use of real data and the interpretation of statistical analyses in the context of the research hypothesis under investigation.

Topics covered will include:

• survival analysis
• analysing categorical data using hypothesis tests
• experimental Design and sampling
• clinical measurement

This module covers all aspects of statistical consultancy skills necessary for being a successful statistician working in any research or customer environment. You will work on real-life problems in small groups and have the opportunity to interact with stakeholders researchers to formulate hypotheses.

This module will cover how to:

• engage with professionals working in business, industry and the public sector
• apply their statistical knowledge in different situations
• effectively communicate statistical results to non-statisticians

This module will introduce you to Bayesian statistics and the modern Bayesian methods that are used in a variety of applications. Like with other modules, the focus is on real-life data and using statistical software packages for analysis.

You will gain experience in working with the following:

• visualising spatial data
• geospatial data, including methods for prediction
• bayesian modelling using software to implement Markov Chain Monte Carlo
• areal unit modelling

This module will provide you with a sound understanding of the principles of Machine Learning and a range of popular approaches. We will provide a sound balance between theory and practical, hands-on applications using Python, so you should be familiar with programming in Python.

You can expect to learn about:

• machine Learning: aims & fundamentals
• machine learning algorithms
• understand when to apply which algorithm
• deep Learning
• artificial neural networks

This module will introduce the challenges of analysing big data with specific focus on the algorithms and techniques which are embodied in data analytics solutions.

At the end of the module, you'll understand:

• the fundamentals of Python for use in big data technologies
• how classical statistical techniques are applied in modern data analysis
• the limitations of various data analysis tools in a variety of contexts

This module will enhance your understanding of the challenges posed by the advent of Big Data and will introduce you to scalable solutions for data storage and usage.

You can expect to learn about:

• the design and implementation of cloud NoSQL systems
• addressing design trade-offs and their impact
• the Map-Reduce programming paradigm