1. Understand computing concepts.
2. Know how to communicate programming ideas using graphical or symbolic pseudo-languages.
3. Know the main primitives of the Python language, exploring their use in the development of visualization and data processing programs.
4. Analyze a real problem and create an effective and efficient algorithm for solving it.
Regarding transversal competences, the aim is for students to acquire autonomy in their learning activity, promote creativity and improve their oral and written communication skills.
1. Introduction to computing and programming;
2. Data types and structures, use of files;
3. Data Flow Control Structures;
4. Data processing and visualisation with the pandas and mathplotlib libraries;
5. Algorithms and structured programming;
6. Object-oriented concepts with Python.
Theoretical classes include lectures in which concepts are presented using examples and demonstrations that illustrate the use of programming language. Some dynamic methodologies based on gamification are used.
Laboratory classes include joint problem solving and individual activities.
Main Texts
Gaël Varoquaux, Olav Vahtras, Emmanuelle Gouillart, & Pierre de Buyl (Eds.). (2024). Scipy Lecture Notes. Release 2024.1 (April 2024). https://scipy-lectures.org/
Ernesto Costa. (2015). Programação em Python—Fundamentos e Resolução de Problemas. FCA. Biblioteca SD 004.43 C871p
Pine, D.J. (2019) Introduction to Python for Science and Engineering. CRC Press.
Supplementary
Downey, A. B. (2024). Think Python: How to Think Like a Computer Scientist. O'Reilly Media; 3rd edition.
Galea, A. (2018). Beginning Data Science with Python and Jupyter: Use powerful tools to unlock actionable insights from data. Packt Publishing."
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