Running an experiment without a solid design behind it usually means wasting time, money, and lab resources on results nobody can trust or repeat. Data science offers a way out of that trap — structuring experiments so that every run extracts the maximum possible knowledge from the minimum amount of resources. The Arab British Fellowship Training Academy built this programme around that exact skill set, aimed at scientists, engineers, technicians and analysts who design, run or interpret experiments as part of their work.

Participants move from the core principles and structures of data science into the practical mechanics of experimental design — planning an experiment, running it, analysing the results, and using laboratory automation and robotics to reduce bias and make findings more reproducible. The programme also looks at the real-world obstacles to adopting data-driven experimental design inside actual laboratories, from an ethnographic and sociological angle, alongside the growing role of robotic labs in the future of experimental research.

Who Should Attend?

• Six Sigma practitioners applying data-driven methods to process improvement.
• Scientists, engineers and technicians running experiments that need to maximise knowledge from limited resources.
• Managers accountable for delivering results on time and within budget.
• Analytics managers, students and professionals who work with experimental data.
• Anyone interested in designing, conducting or analysing experiments.

Knowledge and Benefits:

By the end of the course, participants will be able to:

• Prepare data properly for effective, reliable analysis.
• Apply data science principles to design experiments that are as informative as possible.
• Optimise experiments to save time and cost, using laboratory automation and robotics to reduce research bias.
• Make research findings more reproducible.
• Recognise the practical, ethnographic and sociological obstacles to adopting data-driven experimental design in real laboratories.
• Analyse experimental data, make evidence-based decisions, and understand how robotic laboratories are shaping the future of experimental research.

Module 1: Foundations of Data Science for Experimenters

• What data science means in an experimental context, and why it matters.
• The data structures used to organise and work with experimental data.
• The theoretical foundations that underpin a data-science approach to experiments.

Module 2: Putting Data Science into Practice

• Applying data-science principles to real research and laboratory work.
• Using data analytics to extract meaning from experimental results.

Module 3: Statistical Design and the Purpose of Experimentation

• Why experiments are run, and what they are meant to achieve.
• The role of statistical design and analysis in building trustworthy results.

Module 4: Designing the Experiment

• The core components that make up a sound experimental design.
• Practical guidelines for designing experiments that maximise knowledge from limited resources.

Module 5: From Planning to Execution

• Planning an experiment before any data is collected.
• Conducting the experiment, including the use of laboratory automation and robotics to reduce bias.

Module 6: Analysing Results and Turning Theory into Practice

• Analysing experimental data and making decisions based on the results.
• Translating theoretical experimental-design concepts into real research practice, including the growing role of robotic laboratories.