Statistical Applications of Industrial Big Data

Module code: COS7049-B

Nowadays large amounts of data are collected from many different sources; such data can be used for enhanced benefits and impact to society by evaluating the quality and relevance, integrating with existing information and digital resources, extracting patterns and creating new knowledge for decision support in engineering, healthcare and wellbeing, and society sustainable development. However, large amounts of data create continuous challenges for relevant and effective usage in industry. The module is intended to Engineering, Management, Data Analytics, Computer Science and similar subject graduates to gain hands-on development of advanced knowledge and skills in the application of statistical methods in support of robust big data-based decision-making. This research-informed module enables students to develop both specialist knowledge and enhanced problem-solving skills in statistical data analysis required to apply data science principles, and to provide data-driven, innovative engineering solutions for decision support and data-enhanced applications. Students will explore how statistical applications of industrial big data resources can support knowledge discovery for decision making in domains such as industry 4.0/5.0, product design and development, product quality management and product safety. The module will also address legal, social ethical and professional aspects of such application domains. Students will also benefit from a hybrid approach of learning and assessment, benefitting from critical research, planning and working as team members, and solution development and demonstration as individual professionals. The example topics are aligned with the programme of studies topics through relevant data resources and references. The module assessment will provide opportunities to develop interest, knowledge and skills in problem solving applying statistics to industrial big data challenges and projects within the new wave of data-enhanced engineering developments. Download the PDF for COS7049-B_2023_4.pdf