Master of Science in Industrial Data Analytics
School of Data Science

Programme Overview

Aims and Learning Outcomes

The MScIDA programme offers a comprehensive curriculum designed to equip students with the knowledge, skills, and practical experience necessary to excel in the field of industrial data analytics. The programme integrates foundational concepts, advanced methodologies, cutting-edge tools, and real-world applications, providing students with a holistic understanding of data science, artificial intelligence, business analytics, and its diverse applications across industries.

 

Aims

  • Foster students to explore and push the boundaries of industrial data analytics, fostering a spirit of innovation and creativity in developing novel approaches, algorithms, and solutions to address emerging challenges.

  • Encourage collaboration and integration of knowledge from various disciplines, leveraging insights from statistics, computer science, mathematics, and domain knowledge from specific industrial sectors such as manufacturing, energy, transportation, logistics, and healthcare.

  • Provide students with hands-on experience and real-world projects to develop skills that are directly applicable to industry and research settings.

 

Programme Intended Learning Outcomes (PILOs)

On completing the MScIDA programme, students will be able to: 

  • Demonstrate a comprehensive understanding of the fundamental principles and methodologies of industrial data science.

  • Describe and effectively apply contemporary artificial intelligence, data analytics, internet of things, sustainable development and other information technology techniques for industrial data science.

  • Design, implement, deploy, and manage the solutions for real-world problems using industry-standard data science tools, programming languages, and software platforms.  

  • Develop and apply critical thinking and problem-solving skills to identify, select, and evaluate appropriate data science methods, tools, and algorithms for specific industrial challenges, assessing their performance and limitations across various scenarios.

  • Analyse and articulate the ethical implications of data science methods and tools, ensuring responsible practices related to security, privacy, fairness, transparency, and accountability in data collection, analysis, and decision-making processes. 

  • Demonstrate advanced communication and interpersonal skills within team environments, facilitating effective collaboration and knowledge sharing.