Semester 1	*Statistics for Business
Knowledge of the theory and application of probability and statistics is an essential component of business analytics. Statistical methods make up part of the set of tools required in business analytics, and form the basis for more advanced topics such as machine learning and artificial intelligence.
In this module, students will focus on descriptive and inferential statistics using the R programming language. This provides the necessary statistical foundation for business analytics as well as introducing R programming.

Topics may include but are not limited to:
• Descriptive statistics
• Correlation
• Probability
• Distributions
• Hypothesis testing and confidence intervals
• Linear regression with two variables
• Multiple regression
• Assessing performance and assumptions
• Logistic regression
• R programming

*Data Management
The effective management of small and big data is a crucial component of all business analytics projects.
This module explores the theory and practice of managing data, including identifying and extracting data, data pre processing, data quality, data warehousing, relational databases, and big data solutions.

Course content may include, but is not limited to:
Structured and unstructured data
Data acquisition
Data extraction using SQL
Data storage (relational database management systems)
Big data solutions
Data preparation
Data quality
Security, legislation and ethical considerations

*HR Analytics
The effective use of human resource (HR) data can enhance human resource management (HRM) and thus wider organisational performance. This module will consider the practical use of data in HRM, through applications such as monitoring and evaluating employee activity and performance, predicting future performance and predicting employee attrition. The module will also consider the theoretical basis for the use of data in HRM, thereby linking the practical side of people analytics with HRM theory.

Course content may include, but is not limited to:

Introduction and overview to HR analytics.
The strategic and operational role of HR analytics within an organisation.
Monitoring and enhancing the performance of human resources using data.
The applications of analytics to HRM, and the theoretical basis for these applications.
Descriptive and visual analytics with HR data.
Predictive analytics with HR data.
Ethical considerations with HR analytics.

*Operations Management
This course develops the major themes and strategies of Operations Management within both manufacturing and service organisations. The primary objective is to familiarise students with the basic concepts, techniques, methods and applications of operations management. Topics include operations strategy, process design and analysis, capacity management, quality management, lean management, inventory management and supply chain management.
Semester 2	*Advanced Analytics and Machine Learning
Machine learning is the core technology underpinning predictive analytics and artificial intelligence, as well as many other analytical tasks.
This module will build on the skills developed in the statistics module in terms of both programming and more advanced statistical techniques, namely the application of machine learning algorithms.

Topics may include but are not limited to:
• The analytics process
• Analytics tools
• Feature selection
• Supervised learning
• Unsupervised learning
• Evaluating model performance
• Programming machine learning models
• Evaluation of the ethical implications of the use of algorithms e.g. the potential for reinforcing bias, security and privacy.

*Data Driven Decision Making
The analysis of data is only useful if it contributes to improvements in business decision making. This module explores how businesses use data for making business decisions. This includes a focus on gaining business insights from the effective management and analysis of data, data visualisation and storytelling, and prescriptive analytics techniques. Students will have the opportunity to work with advanced visualisation and optimisation software such as tableau, excel, and R. The module will also consider the people side of analytics, placing analytical techniques for decision making in a business context, considering the managerial and organisational factors involved in becoming a data driven organisation.

Module content may include but is not limited to:

The role of analytics in decision making, at both operational and strategic levels
Data Visualisation: visualisation of a variety of types of data such as numeric, text, and geospatial data.
Prescriptive analytics and optimisation
The role of data driven decision making in organisations
Benefits, barriers, and limitations of data driven decision making
Ethical considerations in the use of data in decision making
Appreciation of the cultural differences in the use of data, and the potential for data to be used in wider national and international decision making (e.g. sustainable development, disaster planning, corporate social responsibility)

*Artificial Intelligence in Business and Society
Artificial intelligence (AI) has already had a substantial impact on business and society, such as data driven business strategies, changes to the nature of work, the development of innovations which shape the behaviour of individuals and society, privacy and surveillance concerns, and recent ethical crises in the use of data.

With the fast pace of AI development, these trends seem likely to continue, making it essential to consider the wider implications of AI on business and society. This module will encourage students to engage with these issues, building a deeper understanding of the wider implications of AI, and how students can contribute to responsible development and use of AI in their future career.

Course content may include, but is not limited to:

The strategic implications of AI innovations for business
The wider economic and societal consequences of AI
Changes in the nature of work due to AI
Ethical use of data
Surveillance and privacy considerations in the use of data
Legal consideration in the use of data

*Marketing Analytics
Module Description
This module focuses on a new and exciting development in marketing theory and practice. The use of data, ‘big data’, to assist in marketing decision-making and accountability continues to grow in importance, particularly in the current age of austerity and resource scarcity. The module takes both a theoretical and practical approach to the use of marketing analytics in practice.

A highlight of the module is the use of SAS or SPSS software to analyse data for marketing-related decision-making and evaluative purposes. Students who successfully complete and pass the module will be able to signal to potential employers that they have the theoretical, practical plus industry-standard software skills to compete.

Module Content:

Indicative contents include:
• Introduction and overview of marketing analytics
• Competing on marketing analytics – developing a marketing analytics culture
• Marketing analytics at the strategic, functional, analytical and warehouse levels
• Customer engagement and customer analytics
• Performance implications of marketing analytics
• Current issues and trends in marketing analytics
• The dark side of marketing analytics

Other content focuses on data mining techniques for marketing (including sales and customer relationship management). Taught through instructor led computer workshops using SAS or SPSS software to solve marketing-related problems. Contents include:
• SAS or SPSS training – introduction and overview
• The marketing analytics process
• Data for marketing analytics
• Understanding the customer
• Predicting customer behaviour
• Amalgamating into marketing operations
• Case studies
• Self-learning
Semester 3	*Dissertation
The dissertation provides students with the opportunity to undertake an independent project. This will involve the development of a technical business analytics solution incorporating elements from the course. The suggested technologies for the solution will be those covered in the course. The solution should typically include a combination of a database, machine learning, and a visualisation component. It is recognised that in some cases projects may focus on specific components (e.g. storage and processing, predictive analytics, or advanced visualisation and interpretation), and this should be agreed in advance by the students supervisor. Students will also be provided with suggestions around potential data sources for use in the project.
In addition to the technical solution, students will be required to produce a written report include a review of the literature, methodology for solving the problem, and results and conclusions.
The module requires students to draw from across the course, incorporating knowledge from the three core business analytics domains: statistics, computing, and business.