This module is highly technical in nature, as it predominantly attempts to expose the quantitative aspects that people in the business profession must be acquainted with should they want to perform everyday tasks effectively. This course covers concepts and methods used in empirical business research. Topics include descriptive statistics, probability theory, tests of statistical significance, hypothesis testing and regression models. The emphasis of the course is on various empirical applications.

This module covers data management, processing, and visualisation topics on Big Data environments. The main challenges and characteristics of Big Data are addressed. Furthermore, the methods and technologies for storing, fetching, preparing, visualizing, and analysing data at scale are covered (including data stores, data warehouses, data integration architectures, distributed storage & processing, decision support systems, and analytics). The theory will be followed by hands-on practical exercises in which the students will become familiar with data warehouses, business intelligence (BI), visualisation, and analytics technologies and relate the concepts to business examples.

This module spans ten weeks and comprehensively explores strategies from foundational concepts to advanced data-driven decision-making. It is designed to equip students with the skills to understand, formulate, and implement effective business strategies while integrating analytics to monitor and refine them in a dynamic environment.

This module’s culmination will synthesize the learned concepts into a cohesive understanding of how strategy, data, and analysis intersect, empowering students to develop and maintain robust strategic management systems in a constantly evolving business landscape a constantly evolving business landscape

This is an integrative capstone module that draws on your entire first semester of studies in order to equip you with the essential knowledge and tools to effectively translate data analytics into business value. It aims to develop your decision making (managerial) skills rather than train you as a data-science expert. Therefore, through this module you will get a perspective on how data models and techniques are used in practice.

Companies have access to an unprecedented volume and variety of marketing data through websites, social media and ad campaigns. In this module students will learn how analytics and predictive analytics is used in digital marketing, how key performance indicators are set to evaluate what has happened in the past and learn how to provide an assessment of what will happen in the future. The ultimate objective is to form strategies to improve marketing campaigns and develop strategies for optimal performance and ROI and plot cumulative transaction plots to understand the effectiveness of your marketing campaigns.

The module objectives unfold in the spring semester. The purpose of this module is to provide students with the opportunity to independently undertake a research project on a particular topic of their choice. Students will determine an appropriate research question; review the literature on the subject of examination; develop, design and present a research proposal addressing the subject of examination; recruit participants; collect and analyze data.

This non-credit preparatory module is designed to provide students with foundational programming knowledge essential for success in the credited module Programming for Business Analytics. It introduces basic programming concepts, logical thinking, and problem-solving strategies using a widely adopted programming language. Students will gain hands-on experience with data types, control structures, functions, and simple data handling techniques. The module emphasises practical application and aims to build the confidence and skills necessary for students with little or no prior programming experience.

Data in Modern Business Analytics is the “new oil”. Decision making is based on past data that businesses have at their disposal and it is that same data that can potentially generate value for them. Modern businesses must be informed on automated decision making and its applications as well as integrating Artificial Intelligence in conveying those decisions. We will focus on how to use data to make comparisons and decisions, and how to develop insights and predictive capabilities using machine learning.

This module will introduce the theory and practice of Project Management and Risk Management and how they support management decisions. Awareness of Project Management and Risk Management practices is critical in modern corporations and assists in impacting both effectiveness and efficiency. The purpose of this module is to cover and contrast the main operational research concepts of the 2 topics according to the methodologies of PMI (USA) and PM2 (EU). There will be a focus on practical project work, founded in theory, and students will be introduced to a range of applicable tools and techniques as well as to several Project Management and Risk Management software packages.

This module covers in detail the practice of research methods in their field of study, with particular attention to the opportunities and challenges that researchers face in applying quantitative methods in a business environment. The students will explore the various designs that are used in quantitative research (e.g., experimental and quasi experimental design, correlational design, surveys) and various data analysis methods will be introduced. In general, students will be advised on how to select the most suitable methodological approach to their problem, how to design their study, and how to proceed with data collection and, analysis, as well as with the discussion and interpretation of their findings.