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Introduction to Programming

Code: CC1008     Acronym: CC1008

Keywords
Classification Keyword
OFICIAL Computer Science

Instance: 2023/2024 - 1S Ícone do Moodle

Active? Yes
Responsible unit: Department of Computer Science
Course/CS Responsible: Bachelor in Agricultural Engineering

Cycles of Study/Courses

Acronym No. of Students Study Plan Curricular Years Credits UCN Credits ECTS Contact hours Total Time
L:EA 34 The study plan from 2019 1 - 6 56 162
Mais informaçõesLast updated on 2024-01-12.

Fields changed: Calculation formula of final grade, Componentes de Avaliação e Ocupação, Tipo de avaliação, Obtenção de frequência

Teaching language

Portuguese

Objectives

In this course the students will:
1. Get acquainted with personal computers in the GNU/Linux operating system and their usage;
2. Learn how to write computer programs using Python and execute them in a terminal.
3. Acquire competence in the implementation of simple algorithms;
4. Acquire good code structuring and programming style;
5. Learn some basic data structures and algorithms;
6. Get acquainted with program debugging and testing.

Learning outcomes and competences

Outcomes:
1. Understanding the role of programming for solving problems in the degree.
2. Acquaintance with the basic components of a recent programming language.
3. Ability to write programs that allow accomplishing useful goals.
4. Confidence in the usage of the Python language and its standard library.

Working method

Presencial

Program

1. A short introduction to computers
2. Variables, expressions and statements
3. Program Flow
4. Functions
5. Data Types
6. Numpy
7. Files
8. Modules
9. More datatypes
10. Recursion
11. Classes and Objects
12. Exceptions

Mandatory literature

Allen B. Downey; Think Python
Allen B. Downey; Modeling and Simulation in Python, Green Tea Press, 2017

Complementary Bibliography

Peter Wentworth, Jeffrey Elkner, Allen B. Downey and Chris Meyers; How to Think Like a Computer Scientist: Learning with Python 3
Peter Wentworth, Jeffrey Elkner, Allen B. Downey, and Chris Meyers; How to Think Like a Computer Scientist, 2012

Teaching methods and learning activities

Lectures; program analysis.
Practical, programming classes.

Software

Python 3.x

keywords

Physical sciences > Computer science > Programming

Evaluation Type

Evaluation with final exam

Assessment Components

designation Weight (%)
Exame 100,00
Total: 100,00

Amount of time allocated to each course unit

designation Time (hours)
Estudo autónomo 106,00
Frequência das aulas 56,00
Total: 162,00

Eligibility for exams

Frequency of practical classes, according to the rules of the University.


Calculation formula of final grade

100% Final exam grade

Classification improvement

Exam (with weight 100% on the grade)

Observations

Juri: João Pedro Pedroso e Bernardo Portela
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