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Biomedical Sciences
Computers and Programming

Computers and Programming

Code	13541
Year	2
Semester	S1
ECTS Credits	6
Workload	PL(30H)/T(30H)
Scientific area	Biomedical Sciences
Entry requirements	there are no admission pre-requirements
Mode of delivery	in classroom
Work placements	Not applicable.
Learning outcomes	This course aims to introduce students to computers and programming in Biomedical Sciences. Upon completing this course, the student must (1) have knowledge of the architecture of a computer and the impact of AI and models such as chat-gpt in the healthcare field; (2) know how to structure problems using algorithms; (3) know what a programming language is; (4) know how to use Python programming frameworks; (5) be aware of its main commands; (6) know how to import and use external libraries; (7) know how to automate routines using control and iteration structures; (8) know how to structure programs using lists and perform numerical computations using Numpy arrays; (9) know the main Python advanced data structures; (10) be able to process files and programmatically access Biomedical Sciences databases using APIs; (11) know how to decompose problems through functions; (12) know how to use Python libraries for data analysis and visualization with pandas dataframes and matplotlib
Syllabus	1. Introduction to Computers 2. Introduction to Computational Thinking 3. Introduction to Programming in Biomedical Sciences 4. Programming Toolkit 5. Introduction to Python 6. Importing and using Python Libraries 7. Control Structures and Iteration 8. Data Structures 9. Advanced Data Structures 10. Reading, Writing Files and APIs 11. Functions 12. Data Analysis and Visualization with Pandas dataframes and Matplotlib
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
Teaching Methodologies and Assessment Criteria	Teaching/Learning Assessment - Test I: 30% (computer-based) - Test II: 50% (computer-based) - Practical Laboratory Exercises: 20% 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 (Practical Laboratory Exercises) proposed in the class. - 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
Language	Portuguese. Tutorial support is available in English.