Class Data Science in Biomedicine

Data Science in Biomedicine is a curricular unit that will enable students to acquire the skills needed for data analysis, statistics, and machine learning to interpret large volumes of biomedical information. This field allows for the identification of patterns, aiding in diagnosis, personalizing treatments, and advancing health research, contributing to advances in precision medicine and improving patient care.

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Introduction to Data Science in Biomedicine Mathematics Topics Oriented to Data Science Data Munging. Scoring and Rankings Statistical Analysis Data Visualization Mathematical Models in Data Science Linear Algebra Distance Methods and Networks Machine Learning Big Data

In the theoretical lectures, the course content is presented through presentations and simulations, encouraging discussion between students and the instructor. In the theoretical-practical sessions, students solve exercises with gradually increasing complexity. Assessment can be either continuous or non-continuous. Continuous assessment includes a written exam (theoretical component, TC) and the submission of two exercises completed during the semester (theoretical-practical component, TPC). The TC consists of two tests or one final exam. The TPC involves the submission of two exercises via Moodle, with no minimum grade required. The final grade for the course is calculated as: Final Grade = 50% TC + 50% TPC, where TC and TPC are the averages of the respective assessments in each component. Alternatively, at the beginning of the semester, students may choose non-continuous assessment. In this case, students take a final exam, which requires a minimum grade of 9.5 for passing.

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). Andrew McMahon. Machine Learning Engineering with Python - Second Edition: Manage the lifecycle of machine learning models using MLOps with practical examples 2nd ed. (2021). Steven Skiena. (2017) The Data Science Design Manual. 1st edition. Springer (ISBN-13: 9783319554433). Marc Deisenroth, Aldo Faisal, Cheng Ong (2020) Mathematics For Machine Learning. 1st edition. Cambridge University Press (ISBN-13: 978-1108470049). Myra Samuels, Jeffrey Witmer, Andrew Schaffner (2012) Statistics For The Life Sciences. 4th edition. Prentice Hall (ISBN-13: 978-0321652805).