Programming I

Code:

CC1015

Acronym:

CC1015

Level:

100

Keywords
Classification	Keyword
OFICIAL	Computer Science

Instance: 2020/2021 - 2S

Active?	Yes
Web Page:	https://brunoloff.wordpress.com/programacao-i-2021/
Responsible unit:	Department of Computer Science
Course/CS Responsible:	Bachelor in Chemistry

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
L:M	111	Official Study Plan	1	-	6	56	162

Teaching language

Portuguese

Objectives

Introduction to the use of computers running GNU/LInux operating systems.

Introduction to programming using the Python language.

Notions of low and high level languages; interpreters and compilers; editor and development environmnets. Values, types and expressions. Functions and procedures. Conditionals and selection. Iteration and recursion. Basic data structures and algorithms:  data processing, text, numerical computation.

Learning outcomes and competences

At the end of the course, the student should be able to:

1. simulate the step-by-step execution of simple programs;

2. write programs to solve solve simple problems (e.g. numerical computations or text processing);

3. decompose problems into sub-tasks suitable for re-usable sub-routines;

4. debug programs using testing

5. know elementary algorithms for data and text processing and numeric computation.

Working method

Presencial

Pre-requirements (prior knowledge) and co-requirements (common knowledge)

Program

1. Brief historical introduction to computing and programming languages
2. Introduction to Python: variables, expressions and instructions
3. Interactive use of Python language; defining scrips and short functions
4. Conditions and logical values; instructions for conditional execution
5. Cicles and iteration. Examples using turtle graphics
6. Recursion
7. Processing indexed variables (lists) and text (strings)
8. Writing and reading data as text files
9. Simple numerical algorithms.

Mandatory literature

Peter Wentworth, Jeffrey Elkner, Allen B. Downey, and Chris Meyers; How to Think Like a Computer Scientist (http://openbookproject.net/thinkcs/python/english3e)

Complementary Bibliography

Justin Solomon; Numerical Algorithms (https://people.csail.mit.edu/jsolomon/share/book/numerical_book.pdf)

Teaching methods and learning activities

Lectures; program analysis; practical, programming classes; students will use tools for automatic evaluation of their programs.

Software

Pycharm Community Edition 2020

Evaluation Type

Distributed evaluation with final exam

Assessment Components

designation	Weight (%)
Exame	50,00
Teste	50,00
Total:	100,00

Amount of time allocated to each course unit

designation	Time (hours)
Frequência das aulas	56,00
Trabalho laboratorial	50,00
Estudo autónomo	56,00
Total:	162,00

Eligibility for exams

Frequency of practical classes, according to the rules of the University and FCUP.  Students must attend at least 75% of the practical classes to be admitted to the exams. Students who miss more than four classes cannot attend the exams. 

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Changes due to Covid-19:

All students will be allowed to take the exam. 

Calculation formula of final grade

There will be one test and one exam. The final grade will be 50% * Test grade + 50% * Final exam grade. The test and exam will both have a coding component and a written component.

Examinations or Special Assignments

Internship work/project

Special assessment (TE, DA, ...)

Classification improvement

Grade improvement is possible in the second exam season. It is possible to improve only the test, or only the exam.

Observations