Data Processing in Biotecnology

The teaching methodologies were designed to ensure coherence between the learning objectives, the program of the curricular unit, and the institution?s pedagogical model, promoting a student-centered approach. The curricular unit combines practical laboratory classes and autonomous work.
In the practical laboratory classes the fundamentals of data analysis in biotechnological processes are addressed, including qualitative and quantitative methods as well as linear and non-linear analysis techniques. The methodology integrates structured exposition, problem-solving, and case studies, encouraging the understanding of stability/variability duality, the construction of representative models, and the selection of strategies appropriate to the problem under study. Problem-based learning activities and flipped classroom moments are also used, in which students prepare topics in advance and present syntheses for discussion, reinforcing autonomy, scientific communication, and critical thinking.
Autonomous work plays a central role in consolidating learning, involving the reading of specialized literature, preparation of exercises, writing of reports, analysis of scientific articles, and the use of digital tools for data processing, visualization, and interpretation. The Moodle platform supports access to materials, submission of assignments, and communication between teachers and students.
The methodologies also include opportunities for oral communication (presentations, discussion of assignments, analysis of articles), collaborative work (in pairs or groups), creativity (design of innovative data processing strategies), and the development of critical thinking (self-assessment, reflection, and selection of appropriate methods).
In summary, these methodologies enable students to understand and apply data analysis methods in biotechnological processes, to develop analytical skills in qualitative and quantitative contexts, to construct representative models of observed behaviors, to integrate current scientific and technological research into learning, and to use computational resources autonomously and effectively. This articulation ensures coherence between methodologies, objectives, and the curricular unit?s program, guaranteeing solid, critical, and innovative training in data analysis and processing in Biotechnology.