Course Topics
The course aims to analyze experimental data in food sciences using Excel and Google Sheets. By the end of the course, students should be able to: 1. Summarize datasets with graphics 2. Control charts for food process control 3. Process capability and Six Sigma 4. Regression and calibration

Propaedeutic courses
None

Teaching format
The course combines lectures and hands-on exercises using Excel and Google Sheets.

Educational objectives
Knowledge and understanding: Knowledge of analytical methods for determining the authenticity, functionality, and microbiological safety of food products, as well as the related methodological tools for risk assessment. These knowledge areas will be developed through an educational program that integrates theoretical teaching activities with practical activities, such as laboratory exercises, computer simulations, simulations of food processes using pilot plants, and company visits. Ability to apply knowledge and understanding: Ability to solve problems related to the organization of production, logistics, and management of agri-food chains. Making judgements: Independent judgement is developed through a training programme designed to stimulate critical analysis in students. This includes the use of case studies, simulations using spreadsheets and videos, the reading and critical discussion of scientific articles, as well as specialist seminars held by experts in the food sector. The assessment of the independent judgement acquired by students is entrusted to the individual teachers responsible for the training activities, who will assess it through oral and/or written reports on specific topics and/or through exams. Communication skills: Use of the English language, both written and spoken, at a B2 level, with a command of technical and scientific vocabulary related to food science. Structure and draft scientific and technical documentation describing project activities; Prepare and present technical reports in English on specialist topics. Learning skills: The degree course provides graduates with the cognitive skills, logical tools and familiarity with new information technologies necessary to ensure continuous updating of knowledge, both in their specific professional field and in the field of scientific research.

Assessment
The final evaluation consists of a written multiple-choice quiz focused on interpreting technical outputs (histograms, control charts, ANOVA tables, box plots).

Evaluation criteria
The final evaluation will be based on a multiple-choice written quiz. Passing criteria: pass with at least 18/30.