This module will provide the student with an in-depth knowledge of the theoretical and applied concepts underpinning business intelligence. It will examine conceptual and logical data modelling whereby the student will gain the practical skills in schema development and be able to extract data on single and multiple tables using SQL. The student will explore the tools and techniques available to capture and analyse data to allow them to create a data science solution to meet business requirements. This course is designed to evaluate and explain how to critically appraise the appropriateness of business intelligence for industry. At the end of this module, the student should be able to collect, process and query data with a view to how they provides insight into managerial decision making.

1. Critically discuss the area of business intelligence .
2. Perform data schema design and critically appraise modelling techniques.
3. Define, create, alter and delete tables and data.
4. Effectively complete data manipulation using SQL commands.
5. Appraise data categories (nominal and ordinal) and how data can be processed to solve a real-world problem or address a business requirement.
6. Critically appraise data analysis techniques available to ingest raw unstructured data, transform, clean and manipulate this data for further analysis.
7. Apply best practice for data gathering, data cleaning, and data formatting to a specific use case or business requirement.

The aim of this module is to provide the prospective Engineering Manager with the ability to interrogate relevant data such as sales activity, equipment performance, investment justification, resource utilisation, process control and quality. This is attained by exploring the tools and techniques available to capture, collate and analyse large collections of information. The module will conclude with the exploration and implementation of graphical and visual methods that best represent the trends and information sought from data to aid projections and quantified recommendations so that pertinent actions can be implemented.

1. Appraise, investigate, and evaluate the various forms of big data methodologies.
2. Appreciate the need for data analysis and evaluate optimal methods.
3. Appraise best practice for data gathering using appropriate methods and tools including Tableau.
4. Develop techniques to process, transform and manipulate raw unstructured data for analysis and presentation.
5. Critically evaluate data structures and types, their distributions, and then implement and interpret relevant hypothesis and statistical tests.
6. Critique, plan and interpret statistical concepts relevant to experimental design, and interpret relevant metrics.
7. Devise, create and develop appropriate visualisation techniques to extrapolate and interpret relevant information, and then construct using appropriate tools.

This module provides students with an opportunity to apply advanced data science techniques to real-world offshore wind energy challenges. The focus is on practical applications of data analytics for performance optimisation, predictive maintenance, reliability assessment, and cybersecurity in offshore wind farms. Students will explore SCADA (Supervisory Control and Data Acquisition) data from offshore wind turbines to develop data-driven strategies for improving efficiency, uptime, and reliability. The module will cover fault detection, anomaly detection, forecasting, and digital twin approaches to predictive maintenance. In addition, learners will develop a strong awareness of cybersecurity risks associated with wind turbine data and infrastructure and how to mitigate these threats. By the end of the module, students will have gained hands-on experience in developing, testing, and deploying data-driven models that can help wind farm operators reduce maintenance costs, minimise downtime, and enhance turbine performance. This will be achieved through a capstone project, where learners will independently analyse offshore wind datasets and present their findings in a technical report and professional presentation.

1. Critically evaluate the role of data science in offshore wind energy management, including applications in predictive maintenance, fault detection, and performance optimisation
2. Analyse and preprocess SCADA datasets from offshore wind farms, applying appropriate techniques to extract insights and detect anomalies
3. Develop and implement advanced machine learning and statistical models to predict turbine failures, optimise maintenance schedules, and enhance wind farm reliability
4. Assess the cybersecurity risks associated with SCADA systems and wind turbine data, and apply reliability engineering principles to ensure secure and robust data management
5. Synthesise and communicate analytical findings through data visualisations, a technical report, and a professional presentation, highlighting the economic, environmental, and regulatory implications to support sustainable, risk-informed decisions in offshore wind operations.