Class Data Science Applied to Food Biotechnology

The curricular unit "Data Science Applied to Food Biotechnology" lies at the intersection of computational sciences and biotechnology, focusing on statistical analysis, predictive modeling, and data visualization in both laboratory and industrial contexts. It operates within domains such as food innovation, quality control, safety, and sustainability, equipping students with essential quantitative tools for solving real-world problems. Its relevance in the study cycle stems from its role in developing technical and analytical skills aligned with the demands of advanced professional settings in Food Biotechnology.

Not applicable

1. Fundamentals programming for data analysis 2. Descriptive statistics applied to food analysis 3. Visualization of laboratory and industrial data 4. Introduction to statistical inference and hypothesis testing 5. Linear and logistic regression in food production and safety contexts 6. Classification and clustering methods applied to microbiological or nutritional data 7. Data acquisition and processing in Food Biotechnology

The course adopts innovative methodologies such as problem-based learning using real-world cases from the food industry, interactive notebooks (Jupyter) for computational experimentation, and integration of open datasets for practical analysis. It also promotes the use of collaborative online platforms for peer discussion and formative feedback. The continuous assessment component, based on applied projects, enables active, autonomous, and contextualized learning, strengthening the connection between theory and practice with a focus on data-driven problem-solving and critical thinking.

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An introduction to statistical learning (2nd ed.). Springer. Pandey, B., Balas, V. E., Tripathi, S. L., Pandey, D. K., & Mahmud, M. (Eds.). (2025). Computational intelligence for genomics data. Elsevier. Provero, P. (2025). Principles of computational genomics. Routledge.