10 credits

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 class will provide the basic tools in statistical data analysis and the underlying theory. The class will enable to:

• understand fundamentals of probability and distributions
• learn appropriate visualisations and summaries for different data types
• undertake appropriate statistical tests for different types of data, including producing confidence intervals

20 credits

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 course will provide the fundamental business modelling skills such as generic problem-solving and basic methodological issues, as well as a good detailed overview of decision analysis techniques relevant to analytics, including decision trees and multi-criteria decision analysis.

This course will provide the fundamental optimisation knowledge necessary to the students, such as network optimisation and integer programming, and develop their practical understanding by modelling challenging problems and understanding algorithmic aspects.

This class will provide the crucial opportunity for the students to apply their broad knowledge of tools and techniques from other data analytics classes to messy business problems that are presented to them by real clients.

This class will help students develop skills in creating and managing database systems, including:

• developing initial database specifications
• formulating database queries using SQL
• understanding the facilities and services which should be provided by a fully featured database management system
• experiencing using a relational database management system in a client-server environment
• understanding future trends in database systems

Evolutionary computing techniques are computational algorithms that use inspiration from systems and phenomena that occur in the natural world. This class will introduce students to the nature of evolutionary computing, in particular genetic algorithms and genetic programming, and enable them to develop and apply these algorithmic techniques to financial applications.

This class will explore the more advanced aspects of evolutionary computing and machine learning with special emphasis on financial applications and large times-series datasets. The class will focus on strategies such as neural networks and deep learning.

This class will give an overview of the legal, ethical and social issues involved in managing digital data. This will cover topics such as privacy, security, intellectual property and various aspects of cybercrime. This will allow both designers and managers to make informed decisions about data management.

To aim of this class is to equip students with a sound understanding of the principles of machine learning and a range of basic approaches, along with the knowledge of how and when to apply the techniques.

The aim of the class is to:

• understand the aims and fundamental principles of machine learning
• understand a range of the key algorithms and approaches to machine learning
• be able to apply the algorithms covered and interpret the outcomes
• understand the applicability of the algorithms to different types of data and problems along with their strengths and limitations

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

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 class will provide the basic theories of machine learning knowledge to construct a machine model for a real dataset using R. Students are expected to understand ethical issues regarding data processing and management.

This class will develop students' skills in data presentation and statistical communication. Students 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.

This elective course will offer the students an opportunity to learn methods to analyse systems with uncertainty, as uncertainty modelling is key to a number of applications.

The module will focus on the main two forms of business simulation:

• discrete-event simulation (DES)
• system dynamics (a continuous simulation technique)

The class will provide a rational approach to simulation using a number of examples from manufacturing and service operations.

10 credits

This module will explore the entire process of structuring a risk problem, from modelling it to communicating recommendations, both theoretically and in practice.

Risk management is linked with decision analysis in so far as we explore decision making under uncertainty and it has links with quantitative business analysis as we explore the use of statistics in understanding risk. However, the topic has some unique attributes such as risk communication and the role that experts play in risk assessment.

The class adopts a process-based approach, ie all discussion follows the logic of the business processes. You'll be introduced to Business and Information Systems (BIS) as well as the various types of IS, including the databases, Enterprise Resource Planning (ERP) systems, knowledge-based systems, corporate portals and collaboration support systems.

Lectures will provide you with the conceptual knowledge, and in tutorials you'll gain hands-on experience of using packages of the various IS categories.