MSc Business Analytics Timetable and Modules
Note: Modules offered each academic year are subject to change. Listed below are the modules and timetable for 2024/25.
Michaelmas Term |
Hilary Term |
Trinity Term |
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Michaelmas Term (September to December)
- Analytics in Practice (Workshops)
- Business Data Mining
- Data Management & Visualisation
- Foundations Of Business Analytics
Hilary Term (January to April)
Trinity Term (May to August)
- Research Project/Dissertation
This project allows students to showcase the knowledge they have gained and enhance their career potential by specialising in a particular area
Module Descriptions
Analytics in Practice (Workshops) (5 ECTS)
This module caters to the growing need to connect the industry with academic research and train the students to read the best of both worlds. There is a lot of content that is delivered in classroom for training of students and introduce them to the fundamentals of the field of analytics and AI. This module strives to be the bridge between these various facets of learning and bring together the different elements through which students should learn.
The module will consist of multiple short lectures delivered by visiting guest academics on topics of research interest that advances the field of analytics and AI. This will be supplemented by visiting lectures from industry professionals who will demonstrate how analytics and AI is being implemented in their industry. Students will have the opportunity to talk to and interact with researchers from across the world and from the industry leaders and connect the classroom education with research and practice.
Having successfully completed this module, the student should be able to:
- Examine the practical challenges in analytics implementation in industry.
- Connect the academic research and practice of analytics in industry.
- Understand the changing dynamics of analytics applications in different industries.
- Evaluate the analytics practices and challenges in Irish industries.
Business Data Mining (10 ECTS)
Business Analytics is about the translation of business challenges into discrete solvable statistical problems, the solving of these problems, and deriving meaningful insights and results for improving competitive advantage. To achieve this we will follow a three pronged approach:
Methodological focus: We will use analytics to explain business phenomena and inform decision making by: describing and visualizing data; creating statistical models from domain knowledge; testing our domain understanding against data; creating experiments; and guarding against fallacious use of statistics.
Statistical focus: We will use a computational approach to statistics, wherein we use computing power to overcome limitations of data quality and quantity. We will learn to reshape data, simulate data and statistics, discover unseen dimensions in data, and create complex models of unobservable phenomena.
Technical focus: We will learn to write data analytics code in R on par with industry standards. Students will implement their own algorithms, write code that is highly readable and reusable, produce highly performant code, create bespoke visualizations, and apply different styles of analytic programming.
Having successfully completed this module, the student should be able to:
- Translate a practical business challenge into a solvable statistical problem.
- Understand the various approaches to time-series forecasting.
- Match an appropriate statistical method to a business challenge and data type.
- Apply statistical methods in the R computational environment.
- Evaluate results from computational processes and generate business insights and solutions.
- Communicate results of analyses to stakeholders.
Data Management & Visualisation (5 ECTS)
Data is the mainstay of businesses in an analytics centric world. In this module, students would be brought to appreciate the world of data management, its related complexities, but most importantly, understand the fundamentals of data storytelling. The module would introduce students to 3 different strands of concepts.
The first concept is of data management and manipulation. Students would learn concepts about data cleansing (including missing value handling) and data management. Data cleansing and management are the most time consuming but equally the most important part of analytics and students would learn to understand this aspect.
The second strand that would be introduced in this module is databases. While this module briefly addresses some technical concepts, it would not dive into the details of data warehousing and data science. We would introduce the fundamentals of this topic to ensure students can converse with the engineering side of affairs. Students would learn the different kinds of databases and basic operations of a relational database.
The third strand of concept revolves around data visualisation. Students would be introduced to the concepts of data visualisation and how to use visualisations to perform basic analysis and extract meaning from data. Similarly, would be introduced to data storytelling, which is beneficial to understand the process of translating data into understandable terms in order to influence business decisions or action.
Having successfully completed this module, the student should be able to:
- Demonstrate good understanding of data and data model.
- Demonstrate good understanding of database management techniques and concepts.
- Use SQL for storing, manipulating and retrieving data.
- Understand the concepts of cognition and attributes of visualisation that appeal to humans in their pre-attentive sense.
- Demonstrate good understanding of visualisation practice to represent large data in a small space and make information coherent.
- Demonstrate good understanding of data storytelling using data manipulation and data visualisation principles.
Foundations of Business Analytics (10 ECTS)
Most decision-making in business will require managers to deal with data. This module introduces the students to use statistics for business research and data analysis. It covers the following topics - descriptive statistics and data types, random variables, basics of probability and distributions, sampling and data collection, hypothesis testing, linear and logistic regression. This module will use real-world examples to help you acquire the skills to make informed decisions with data.
Having successfully completed this module, the student should be able to:
- Appreciate the role of data-driven insights generation in real-world situations.
- Learn the working principle of each prescribed technique.
- Obtain practical skills in data collection and analysis;
- Understand the basic concepts of statistical tools in business analytics.
- Be able to identify key drivers of business outcomes and make meaningful predictions in decision-making.
Advanced Topics in Analytics (5 ECTS)
In this module, students will learn fundamental prescriptive analytics tools to construct models and propose solutions for complex business problems. Spreadsheet modelling will utilize the available data to find the best courses of action and optimal solutions for single and multi-objective business problems. Furthermore, for problems where random system elements substantially affect the performance outcomes, the simulation will be adopted via spreadsheet tools to conduct a “what‐if” analysis and obtain insights into the consequences of different strategies and alternative actions in business environments.
Having successfully completed this module, the student should be able to:
- Explain the concept of optimization and multicriteria decision-making.
- Structure optimization and simulation problems to analyze complex business environments and decisions.
- Develop spreadsheet models to represent optimization and simulation problems.
- Solve and interpret the results of spreadsheet models to determine the best courses of action.
- Increase awareness of the scope of business environments and functions optimization and simulation could be applied as prescriptive analytics tools.
Big Data and AI in Business (10 ECTS)
This module introduces students to the field of Big Data: how it is generated, stored, and used. It also introduces them to the field of Artificial Intelligence (AI): the different frameworks, methods, and business applications. It addresses emerging technologies and the implications these will have on the data/AI ecosystem. Students will be exposed to new AI technologies and taught how to identify the appropriate technology for a given need.
The objective of this module is to prepare students to be data managers/Head of Department. It will teach them how to build a robust data strategy, how to manage the people they will encounter, and how to define, develop, and run a sophisticated data-science project.
Overall, this module teaches students how to make suitable long-term data strategies that can anticipate and adapt to a shifting field, emerging AI technologies, new types of data professionals, and ultimately build a long-term plan for the future whilst also addressing the current Big Data and Artificial Intelligence needs of a business.
Having successfully completed this module, the student should be able to:
- Understand the history and state-of-the-art of Big Data, along with a practical knowledge of its architecture, and how new technologies might shape it in the near future.
- Understand Artificial Intelligence (AI) history, frameworks, methods, personnel, and applications, given varying industries and business-needs.
- Combine knowledge of Big Data, AI, and emerging technologies to anticipate potential futures, through analysis of current and future needs, and how these needs could be addressed by the application of suitable stable or emerging technologies.
- Apply knowledge of Big Data, AI, and business-needs (current & future) to create a robust 3-5 year data strategy.
- Identify, evaluate and select suitable AI technologies to address business-needs and be able to map out the full process of an AI proof-of-concept.
Esg Analytics (5 ECTS)
This course will delve into data analytics with a focus on sustainability challenges across various domains, including environment, energy, infrastructure, and agriculture. The course will begin with Foundations of Sustainability, introducing key concepts, global frameworks, and sustainability metrics. Next, the Data Analytics for Sustainability module will cover data sources, collection methods, and tools like Python for sustainability data analysis.
The course will focus on Quantitative Methods, exploring statistical techniques and predictive modeling to assess environmental impact. The Sustainability Reporting and Compliance aspects will guide students on how to interpret and create sustainability reports, aligned with standards such as GRI and SASB. Emphasizing the importance of clear communication, this module will teach students how to visualize and report data insights effectively, aligning with global sustainability standards.
Having successfully completed this module, the student should be able to:
- Critically examine sustainability initiatives and actions of firms and nations.
- Identify and separate useful and impactful data from all available sources.
- Analyze data from varied sources to create deep insights.
- Write sustainability specific reports using data driven insights.
Social Media Analysis (10 ECTS)
Social media platforms like Facebook, X(Twitter), and YouTube play an important role in online communication, be it at organizational or individual levels. The volume of data generated by social media users has increased phenomenally over the years. Accordingly, searches and processing of social media data have become increasingly important to businesses and end users. Detection of sentiment, emotion, facial characteristics, user communities, and so on, are all valuable social mining components that promise to be important elements of next generation search engines. The emerging area of extracting meaning from social media data using automated methods is known as Social Media Analysis, that this module would explore. The module will cover methods related to analysis of text, image and social network data used on popular social media platforms.
Having successfully completed this module, the student should be able to:
- Analyse a wide range of social media usage
- Apply data mining techniques to social media platforms
- Develop intelligence based on emotion and sentiments in online content
- Analyze the text and image aspects of social media data
- Understand how networks and graphs are used in social media
Research Project/Dissertation (30 ECTS)
This module is the culmination of year-long learning that students have been on in the program. It allows students to learn new methods and topics in an in-depth manner, in an area of their choice. It also provides a platform to students to demonstrate and apply the techniques and knowledge acquired from the taught modules to a business context or to create new knowledge in a field of their interest. This shall be achieved by conducting an independent piece of empirical or theoretical research under the guidance of an academic supervisor. Or alternatively by working on a research project provided by an industry partner. In an experiential learning manner, the key actions involved are designed to develop different research skills and demonstrate the student’s ability to write clearly and to undertake various types of management research topics, to reflect on their own development and learning, and to effectively communicate the results of their research.