Amongst the many excellent open access educational resources available for analytical chemistry, there is a distinct lack of resources to support the teaching of chemometrics for analysis of analytical data. The use of context/problem-based learning is known to be effective in engaging students with course material that can otherwise be dry or dull. However, the development of effective context/problem based learning scenarios requires a great deal of time and skill. Here, we describe outputs resulting from an industrial-academic collaboration focused on producing context/problem-based learning resources to teach students how to apply appropriate statistical methodologies in instrumental analysis. The resources developed support two distinct phases of student activity. For the first phase, a scaffolded data analysis activity has been developed, which requires students analyse a data derived from a range of calibration approaches. Resources for this phase include a spreadsheet exercise and video tutorials. Students are walked through the analysis of data to determine the level of analyte concentrations using standard series, standard additions, internal standards and ion-selective electrodes calibration. For the second phase, a context/problem-based scenario has been devised based on platinum group metal (PMG) refining. The scenario follows the challenges faced by Dr Jo Flowers, Head of Process and Analysis at Sterling Metal Enhancement Ltd. Students are presented with problems to solve in the context of a real-life scenarios faced in the PGM refining industry. These problems require students to think about which analysis method is most appropriate for different situations. Students then analyse the resultant data to determine PGM levels at different stages of the process, overcoming a range of commercially relevant issues. Access to the data analysis activity and context/problem-based scenario in advance of the conference forms the basis of the flipped material for this session.