Class Data Sciences in Biotechnology

This Course Unit aims to understand concepts associated with Data Science oriented to biotechnology and similar areas. The student is introduced to the mathematical formulation of tools in the domains of Statistics and Probability, which enable the implementation, development and interpretation of scripts in Python language. In this sense, through supervised and unsupervised learning mechanisms, the student is able to generate solutions that comprise processing, analysis and visualization of relevant data sets, which can be extensive (big data) or not.   The relevance of this Curricular Unit is also completed with the study and modeling of applications in the biotechnological domain, such as the prediction of diseases in living beings to effects in genetically modified crops, in order to support decision-making either in academic research or in biobusiness.  

Not applicable

Introduction to Data Science in Biotechnology Mathematical Preliminaries for Data Science Data Munging. Scores and Rankings Statistical Analysis Data Visualization Mathematical Models in Data Science Linear Algebra Linear and Logistic Regression Machine Learning Big Data

The theoretical classes present the program content, using presentations and simulations, stimulating discussion between students and teachers. In the theoretical-practical classes, students solve exercises with a progressive transition in complexity. Assessment may be continuous or non-continuous.  Continuous assessment: written test (theoretical component, CT) and submission of two exercises solved during the semester (theoretical-practical component, CTP). CT: completion of two tests or one exam. CTP consists of the submission of two solved exercises via Moodle and their discussion (40% exercises, 60% discussion), with no minimum grade. The final grade for the course unit is calculated as follows: Final Grade = 50% CT + 50% CTP

Trevor Hastie, Robert Tibshirani, Jerome Friedman (2009) The  Elements  of  Statistical  Learning:  Data  Mining,  Inference,  and  Prediction. 2nd edition. Springer (ISBN-13: 978-0387848570) Laura Igual, Santi Segui (2017) Introduction  to  Data  Science:  A  Python  Approach  to  Concepts,  Techniques,  and  Applications. 1st edition. Springer (ISBN-13: 978-3319500164)