This module aims to:

Provide students with an understanding of what analytics is, and what it is used for;

Enable students to critically evaluate reports and data analysis produced by different functional and operational specialists (e.g., marketing, operations) and financial analysts;

Provide students with the skills to identify how data and analytics can be used to support their decision making;

Enable students to articulate and explain results of big data analysis.

(LO1) Students will be able to critically assess the strategic role of business analytics in organisations, evaluating both its potential value and the challenges associated with its implementation in complex business environments.

(LO2) Students will be able to demonstrate advanced analytical reasoning by critically evaluating the application of data analytics to complex business problems, selecting appropriate methodologies and justifying their relevance.

(LO3) Students will be able to independently perform advanced data processing, analytical, and visualisation tasks, applying industry-standard tools and techniques to extract meaningful insights from complex datasets.

(LO4) Students will be able to develop and refine analytical models, leveraging a range of regression techniques and advanced machine-learning algorithms to generate data-driven business solutions.

(LO5) Students will be able to critically reflect on the limitations and ethical implications of data analytics, recognising biases, uncertainties, and potential risks associated with data-driven decision-making.

(S1) People Focused
Students will work with multiple stakeholders within the MBA programme and externally. This will be done through challenges posed in the curriculum and outside the curriculum, working and networking with businesses locally and internationally. In doing so students will demonstrate people-focused leadership by prioritising human agency and well-being in a dynamic, disruptive world. Develop inclusivity, transparency, and reflection through teamwork and decision-making exercises, while leveraging innovation to drive personal, organisational, and societal advancement in the digital age.

(S2) Authenticity
Students will develop emotional intelligence through coaching to enhance self-awareness and awareness of others in achieving long-term, purposeful goals. This will permeate with all modules, cultivating accountability by mastering time, resources, and relationships to problem-solve and take actions. In doing so students will act with integrity, foster trust, and engage in respectful challenge and debate.

(S3) Agility
Students will have opportunities to thrive in turbulence and uncertainty. Actively build personal and organisational resilience to embrace disruption, find solutions and keep learning in dynamic environments. Informed, relevant, forward-focused mindsets stemming from constant challenges inside and outside the classroom. Provide clear direction for people and organisations to navigate effectively through simulated and real work problem solving.

(S4) Digital
Students will use a number of digital tools and develop a mindset to confidently embrace new knowledge and technologies, leverage its future potential to inspire, to enhance connectivity, decision making and learning. To bring wisdom in leadership to navigate the new digital revolution and make positive impact. Innovate. Engage in respectful debate.

(S5) Global Perspective
All modules will indulge and challenges students to think globally and consider how they drive the wider global ethical, sustainable and environmental agenda, taking a purposeful approach to aligning people, planet and profit. Experience and appreciate how building partnerships to deliver innovate solutions to economic, environmental and societal challenges are needed to solve wicked problems, improve co-operation globally and be an agent of change.

Lectures (12.5 hours)
Lectures will introduce, explore, and critically assess key concepts, ideas, and practices relevant to the subject. A variety of resources, including journals, textbooks, online videos, media articles, and opinion pieces, will be provided in advance for further reading and application across different contexts. The lecture will also outline expectations for the seminar by providing necessary guidance.

Seminars (17.5 hours)
Seminars will create an interactive environment where participants engage individually or in groups in activities such as case studies and scenario analysis. These sessions will encourage the practical application of concepts introduced in the lectures.

Peer-to-Peer Learning (3 hours)
Peer-to-peer learning fosters a collaborative approach where participants learn from and teach each other through active discussions, idea exchange, and feedback. This method enhances critical thinking, problem-solving, and communication skills while building a sense of community and mutual support among learners.

Self-Directed Learning (67 hours)
Participants will be expected to review a range of materials, including core and recommended readings, reports, and articles available through libraries and open-access sources. Additionally, they will conduct independent research using both academic and non-academic sources to support their learning and assessment. Lecture notes will provide guidance, and suggested answers or solutions will be available for seminar activities. Participants should complete seminar tasks as instructed before progressing to the next topic.

2.5 hour lecture x 5
3.5 hour seminar x 5
36 mins asynchronous or peer to peer directed learning x 5
67 hours self-directed learning

Key topics:

Big data and analytics: an introduction;

Data analytics software, including R/RStudio;

Visualising data with R;

Techniques for data analytics: machine learning (classification and clustering), regression; interpreting model outputs (inference); and forecasting future or new values.

Case studies, including from different sectors including, for example, aviation, sport and health management, will be used throughout the module.

Materials will be made available to students via the VLE and students will also be expected to read additional materials from the suggested and required reading list, using online library resources.