Bachelor of Engineering (Honours) (Chemical and Sustainable Process)/Master of Data Science

5 Years On Campus Bachelors Program

Queensland University of Technology

Program Overview

This integrated degree combines chemical and sustainable process engineering with advanced data science, preparing students to design efficient, sustainable industrial systems powered by data-driven decision-making. It is ideal for students who want to work in emerging fields such as smart manufacturing, energy systems, environmental analytics, and industrial data science.

Campus: Gardens Point Campus, Brisbane, Queensland, Australia

Curriculum Structure

First Year

In the first year, students build a strong foundation in engineering, mathematics, chemistry, and introductory programming concepts. Core units typically include Engineering Mathematics, Introduction to Chemical Engineering, and Engineering Design, alongside data-focused subjects such as Foundations of Data Science and Programming Fundamentals. This year develops analytical thinking, technical problem-solving, and computational skills.

Second Year

The second year strengthens core chemical engineering knowledge while introducing deeper data science and statistical methods. Students study Thermodynamics, Fluid Mechanics, and Process Engineering Principles, alongside Statistical Analysis and Data Modelling. This stage helps students understand how data can be used to improve chemical and industrial systems.

Third Year

In the third year, students move into advanced engineering and applied data science techniques. Engineering units include Reaction Engineering, Separation Processes, and Process Control, while data science subjects cover Machine Learning Fundamentals and Data Visualisation. Students begin integrating engineering systems with data-driven optimisation approaches.

Fourth Year

The fourth year focuses on advanced sustainable engineering systems and applied data science for industrial applications. Students study Advanced Process Design, Sustainable Engineering Systems, and Industrial Optimisation, alongside advanced data science electives such as Predictive Analytics. This year strongly emphasises real-world decision-making using data.

Fifth Year

In the final year, students complete a major capstone project combining chemical engineering and data science. Projects typically focus on process optimisation, smart manufacturing systems, or sustainability analytics using real industrial datasets. Graduates leave with strong expertise in both engineering design and advanced data-driven problem solving.

Focus Areas:

Chemical process engineering, data science, machine learning, industrial analytics, process optimisation, sustainable systems, predictive modelling, smart manufacturing, and environmental data analysis.

Learning Outcomes:

Graduates develop the ability to design and optimise chemical processes using data-driven methods, apply machine learning and analytics to industrial systems, and solve complex engineering problems using computational and statistical tools.

Professional Alignment (Accreditation):

The Engineering (Honours) component is aligned with Engineers Australia accreditation standards, ensuring professional recognition. The Data Science component is delivered within QUT’s internationally recognised STEM framework, supporting careers in advanced analytics and digital industries.

Reputation (Employability Rankings):

Queensland University of Technology is globally recognised for strong graduate employability, applied STEM education, and industry-integrated learning. It consistently performs well in QS World University Rankings for employer reputation and practical, career-focused education.

Experiential Learning (Research, Projects, Internships etc.)

At QUT, this program is built around hands-on engineering practice combined with advanced data-driven problem solving, so students continuously apply both chemical engineering principles and data science techniques to real industrial challenges. You’ll gain practical experience in modern laboratories, computational environments, and industry-style project settings where engineering systems and data analytics come together to solve sustainability and optimisation problems. The learning experience is strongly applied, ensuring students graduate with real skills in both process engineering and advanced data science methods:

Chemical engineering laboratories: hands-on experimentation in thermodynamics, reaction engineering, fluid mechanics, and process optimisation
Data science and analytics computing labs: practical use of statistical analysis, machine learning models, and data visualisation techniques
QUT Science and Engineering Centre (SEC): collaborative STEM environment supporting engineering design, data-driven projects, and interdisciplinary teamwork
Process simulation and data analytics software: industry-relevant tools used for chemical process modelling, predictive analytics, and industrial optimisation
Capstone interdisciplinary projects: final-year projects combining chemical engineering systems with data science applications such as predictive modelling and process optimisation
Work Integrated Learning (WIL): structured internships and industry placements with engineering firms, data analytics companies, energy organisations, and manufacturing industries
Central Analytical Research Facility (CARF): advanced instrumentation supporting chemical analysis, materials research, and scientific data generation
Interdisciplinary group projects: teamwork combining engineering design, statistical modelling, and machine learning applications
QUT Library (Gardens Point): access to engineering journals, data science research papers, technical standards, and global industry datasets
Industry-linked applied learning projects: real-world datasets and case studies developed in collaboration with engineering and analytics partners

Progression & Future Opportunities

Graduates of this program are prepared for high-demand careers that combine engineering expertise with advanced data science capabilities, enabling them to optimise industrial systems, improve sustainability outcomes, and build intelligent data-driven solutions. You can progress into roles such as Process Engineer, Data Scientist (Industrial Systems), Sustainability Analyst, and Machine Learning Engineer, working across energy, manufacturing, technology, and environmental sectors:

QUT Career and Employability Services: personalised career coaching, CV and interview preparation, employer networking events, and access to graduate recruitment pathways across STEM and data industries
Work Integrated Learning (WIL): structured internships and placements with engineering companies, data analytics firms, energy organisations, and advanced manufacturing industries
Graduate employment outcomes & salary range: engineering and data science graduates in Australia typically achieve strong employability, with starting salaries often around AUD 75,000–110,000+, depending on role, industry, and specialisation
Industry partnerships: QUT collaborates with organisations in engineering, energy, data analytics, and advanced manufacturing, supporting internships, applied research projects, and graduate employment pathways
Professional accreditation value: the Engineering (Honours) component is aligned with Engineers Australia accreditation standards, ensuring long-term professional recognition and global engineering mobility
High-demand interdisciplinary advantage: graduates are especially valued for combining engineering systems knowledge with advanced data science, machine learning, and analytics expertise

Further Academic Progression:

After completing this program, graduates can pursue advanced qualifications such as a Master of Data Science (advanced specialisation routes), Master of Engineering (Chemical, Sustainable, or Systems Engineering), or other postgraduate STEM degrees. Those interested in research or innovation careers can also progress to Master of Philosophy (MPhil) or PhD pathways, focusing on areas like industrial AI systems, sustainable process optimisation, or advanced engineering data analytics.