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Main Bibliography |
- Sobral, S. (2023). Introdução à Programação usando Python. Silabo.
- Carvalho, A. (2021). Práticas de Python - Algoritmia e Programação. FCA.
- Downey, A. (2015). Think Python - How to Think Like a Computer Scientist. O'Reiley. Green Tea Press
- Severance, C. (2013). Python for Everybody - Exploring Data Using Python
- Miller, B., and Ranum, D. (2011). Problem Solving with Algorithms and Data Structures using Python: Interactive Edition
- Stefanie Molin (2019). Hands-On Data Analysis with Pandas: Efficiently perform data collection, wrangling, analysis, and visualization using Python. Packt Publishing.
- Chang, J., Chapman, B., Friedberg, I., Hamelryck, T., de Hoon, M., Cock, P., Antao, T., Talevich, E. and Wilczynski, B. (2023). BioPython Tutorial and Cookbook. https://biopython.org/wiki/Documentation
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Teaching Methodologies and Assessment Criteria |
- Test: 60% (computer-based)
- Practical Project: 40% (groups of 3 elements)
The final classification of the course results from the weighted average of the classifications obtained in the defined evaluation components. The student obtains approval at the course, being exempt from the Exam, in case he/she obtains a grade equal to or greater than 9.5 values.
Evaluation by Exam
- Exam: 100% (computer-based)
Admission to the Teaching/Learning and Exams:
- Minimum of 70% class attendance during the teaching-learning period (except student workers);
- Minimum of 80% in the submission of the programming problems proposed in the class. The problems are not classified with a grade nor do they count towards evaluation, they are, however, a necessary condition for approval of the course within the learning assessment period and the exam.
- Minimum score of 6 points in each of the defined evaluation components.
Failure to comply with any of these items prevents the student from being approved
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