MSc Analytics

Programme Overview

This module is a continuation of Research Methods for Business and Management I in which the student will complete the project or dissertation presented in the first module. 

Programme Overview

This module is designed for students to develop a comprehensive understanding of research in general and business. Both academic business research and business consultancy research are addressed in the module. 

The module examines the concepts and theories underlying research, as well as the practice of research. The module has an applied focus. The readings and assignments are designed to identify a feasible research topic and develop a research proposal for academic business or business consultancy research. How research problems and questions may be identified, and the process of achieving thoughtful, effective and efficient research design are examined. The importance of the literature review and how it can be carried out for optimal effectiveness in research are investigated.

In addition, the module provides an overview of research methodologies and methods and the techniques of the research process as applied to business, whether as a business consultant or as an academic researcher. The philosophical and epistemological assumptions underpinning the research approaches and methodologies, as well as their role in determining the design and implementation of the research, are studied. Besides these aspects, the ethical aspects of research and consultancy are also addressed. 

Programme Overview

You will learn important database concepts, including query optimisation, distributed databases, big data/knowledge management, data modelling, database design, and data extraction. Topics covered will allow you to gain data analysis skills needed to transform raw data into useful business information and knowledge for decision-making and problem-solving. You will gain hands-on project experience using SQL, learn tuning and performance techniques, and evaluate different types of software technologies that can be used such as Oracle. As a result, you will understand database architectures, query optimisation, and how to document data models. Your knowledge gained in this course will allow you to develop solutions based on database systems, techniques, and requirements by understanding various use cases in business. 

Programme Overview

This module will challenge students by introducing them to making business decisions using data analysis. Data analysis is the use of statistics combined with analytical methods to give insights into a business. After starting with a refresher on basic statistics, students will learn how to create statistical experiments to create a process for decision-making. These experiments will use methods such as regression and optimisation to determine the optimal business decisions. The analysis of these experiment outcomes will give you a deeper insight into your business units. This will result in an increase in your ability to add value to your organisation. 

Programme Overview

Over the years, organisations have accumulated a vast amount of data in their enterprise-wide information systems. These data typically represent daily operations and transactions within a business context. Organisations rely on computer-based information systems for capturing, analysing, and distributing the information required to develop, implement, and evaluate strategies in all functional areas. Managing data as a corporate resource requires an understanding of business processes and of the underlying structure of the data needed to support them. Business intelligence (BI) is the process of collecting and turning this resource into Business value. 

The course provides an introduction to decision-making and technologies used to support organisational decision-making. (BI), which uses historical data to better understand and thereby improve business performance, as well as create new strategic opportunities for growth. Part of this process is to display the results in graphical images for easier understanding. This course will provide an understanding of data organisation, and examine the BI processes and techniques used in transforming data into knowledge and value. Various functions and applications of business intelligence are described, including but not limited to reporting, online analytical processing, data visualization and business process management.

Programme Overview

You will explore the background of data science, fundamentals of data mining, and data analytics concepts and techniques in business Intelligence. The goal is for you to have a solid foundation in how data mining and analysis works together by understanding data patterns, structures, and useful information to solve real-world problems. You will demonstrate proficiency in programming skills (i.e.: Python, SQL, R or Java) to use algorithms in different languages to build data models, create testing strategies, and analytical methods. As a result, you will gain knowledge of data processing, design, and implementation of data warehouse, customer relationship management (CRM), and OLAP (online analytical processing) systems for emerging applications examples (i.e. eCommerce platforms). 

Programme Overview

This module offers an in-depth understanding of Data Mining and Strategic Management techniques for improving business decision-making. Through an examination of data mining and machine learning terminology and techniques, students will be introduced to data blending and wrangling concepts applicable to formulating a strategic data management plan. The challenge is to select the appropriate method. Students will learn to use and design data mining-based solutions to solve “real-time” business problems. 

Students will explore the strategic planning concept for using decision support systems or hybrid platforms when wrangling NoSQL and text data. The coursework will explore data mining concepts for blending unstructured data sets or working with semi-structured data using the Extract, Load, Transform (ELT) process. Also, students will briefly examine automated processes, like Extract Transformation and Load (ETL) or loading data sets as use cases. This course will theorize on data optimisation techniques for using machine learning and artificial intelligence for deep learning of the data. 

Finally, students will conduct statistical data modeling used in predictive and descriptive analysis such as classification trees, segmentation, clustering, and perform basic exploratory data analysis with Excel to create data analytical presentation projects.

Programme Overview

You will receive an introduction to data visualisation to understand how data is analysed and viewed by creating interactive dashboards in a graphical representation such as infographics. You will use best practices to design and map data interactions that allow various stakeholder audiences to intuitively explore the dataset and the impacts on a business area. As a result, you will understand the architecture needed to support various visualisation tools, methodologies, and techniques to identify trends in data by analysing various situations.

Programme Overview

This module equips you with a thorough understanding of the advantages and challenges presented by workplace diversity, suggesting techniques to manage effectively and maximise the benefits of diversity. You will be guided through a comprehensive understanding of effectively managing diversity on the basis of race, sex, sexual orientation, gender identity, religion, age, ability, national origin, and intersectionality in organisations and about issues various groups face, including discrimination. 

In addition, you will analyse data in ways that facilitate the development and evaluation of human resource (HR) policies, practices, and strategies. Having an engaged workforce improves organisational productivity, because engaged employees are involved in, enthusiastic about, and committed to their work and workplace. It is critical for managers to understand the human resource management practices that facilitate employee engagement as well as how to conduct a competent workforce survey and use the data to assess and maintain/improve employee engagement. 

Programme Overview

This module will provide a practical, hands-on set of lectures surrounding modern predictive analytics and machine learning algorithms and techniques. It will emphasise practice over mathematical theory, and students will spend a considerable amount of class time gaining experience with each algorithm using existing packages in R, Python, and Linux libraries. The module will cover the following topics: regression and logistic regression, regularised regression including the lasso and elastic net techniques, support vector machines, neural networks, decision trees, boosted decision trees and random forests, online learning, k-means and special clustering, and survival analysis.

Programme Overview

It is important for managers to know how organisations intend to engage with their environment and consequently develop capabilities and competencies to provide competitive advantages and fulfil their organisational objectives. This course focuses on models conceived by observing businesses in developed countries and applies to all sizes of firms including corporations operating globally.

Programme Overview

Students learn statistical techniques for further study in business, economics, and finance. The module covers sampling distributions, probability, confidence intervals, hypothesis testing, regression and correlation, basic modelling, analysis of variance, and chi-square testing. The module emphasises statistics to solve management problems.