| Code |
15345
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| Year |
1
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| Semester |
S1
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| ECTS Credits |
6
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| Workload |
PL(30H)/T(30H)
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| Scientific area |
Informatics
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Entry requirements |
N/A
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Mode of delivery |
Face-to-face.
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Work placements |
Not applicable.
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Learning outcomes |
This course is designed to equip students with the following competencies: (i) understanding data types and syntax used in Python; (ii) the ability to develop and structure dynamic programs in Python, employing relevant data structures; (iii) the skill to organize and segment programs in Python to effectively partition functionalities for problem-solving; and (iv) proficiency in reading and producing files while managing errors/exceptions that arise during program execution, in addition to enhancing program functionalities with external Python libraries. In terms of skills, students will be capable of: structuring abstract thinking to interpret problems presented in natural language and translating them into Python programs; writing dynamic Python programs while addressing errors/exceptions that may occur; segmenting programs to partition functionalities based on specific problem requirements; and utilizing data structures and libraries that are suitable for effective problem-solving.
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Syllabus |
1. Introduction to Python Programming
2. Basic Data Types
3. Tests and Conditions
4. Iterations (Repetition Statements)
5. Data Structures: Lists and Dictionaries
6. Iterations in Data Structures
7. Functions
8. Classes
9. Python Libraries
10. Reading and Writing Files
11. Tests and Exceptions
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Main Bibliography |
Portela, F. and Pereira, T. (2024). Introdução à Algoritmia e Programação em Python. FCA
Sobral, S. (2023). Introdução à Programação usando Python. Silabo.
Carvalho, A. (2021). Práticas de Python - Algoritmia e Programação. FCA.
Matthes, Eric. (2019). Python Crash Course, 3rd Edition. No Starch Press, 2023. 2ª Edição
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
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Teaching Methodologies and Assessment Criteria |
Theory and practical tests – 70% of final evaluation: TPT= 0,35xT1 + 0,35xT2. Practical Exercises (PE) – 30% of final evaluation: PE=0,1xPE1 + 0,1xPE2+0,1PE3.
The student must obtain a minimum of 9.5 in sum of the components, TPT and PE, to obtain approval for the curricular unit. Classification (C)= 0,35xT1 + 0,35xT2 + 0,1xPE1 + 0,1xPE2+0,1xPE3. The student is approved if he/she obtains a classification greater than or equal to 9.5 during the teaching-learning period. In case of approval, the final classification (FC) is the integer closest to C, that is: If C >= 9.5, then approved with FC = round (C). In case of approval in the teaching-learning period, the student is exempt from the exam, although they may improve their final exam classification. FC < 5.5 (out of 20) => Not approved and Not Admitted to the Exam;FC >= 9.5 (out of 20) => Approved and Exempted from Exam; Remaining cases => Not approved and Admitted to the Exam
Exam(70%)+30%(relating to practical exercise)
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Language |
Portuguese. Tutorial support is available in English.
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