Data Mining in Chemical Engineering and Bioengineering
About This Course
This course equips learners to extract meaningful insights from complex datasets commonly found in chemical production and biological systems. It develops strong competencies in data mining, statistical learning, and machine learning, supported by hands‑on use of modern computational tools. Learners build skills in data preprocessing, predictive modelling, interpretation of outcomes, and data‑driven problem solving. Designed for learners in chemical engineering, bioengineering, and related fields, the course addresses industry needs in process optimization, monitoring, and decision support. These capabilities are increasingly valued in engineering and biotechnology sectors, enhancing employment prospects and enabling career progression in roles shaped by digitalisation and advanced analytics.
As this micro-credential course is only offered in the FlexiMasters in Computational Chemical Modelling and AI programme, learners are required to enrol into the programme and complete all required courses within this programme.
What You'll Learn
• Apply principles of data mining and statistical learning to analyze datasets arising from chemical and biological processes.
• Implement and evaluate data pre-processing, regression, dimensionality reduction, and machine learning algorithms using appropriate computational tools.
• Interpret and critically assess the outcomes of data mining models in the context of prediction and monitoring.
• Propose data-driven strategies to address challenges in chemical engineering and bioengineering.
Entry Requirements
No pre-requisites are needed to enroll in the individual courses; however, learners without qualifications or work experience in relevant engineering or the related fields may find the course contents challenging.
Note: Shortlisting will be conducted for the courses