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Programming I

Code: CC1015     Acronym: CC1015     Level: 100

Classification Keyword
OFICIAL Computer Science

Instance: 2017/2018 - 2S

Active? Yes
Web Page: http://www.dcc.fc.up.pt/~pbv/aulas/programacaoI
Responsible unit: Department of Computer Science
Course/CS Responsible: First Degree in Chemistry

Cycles of Study/Courses

Acronym No. of Students Study Plan Curricular Years Credits UCN Credits ECTS Contact hours Total Time
L:M 87 Official Study Plan 1 - 6 56 162

Teaching Staff - Responsibilities

Teacher Responsibility
Pedro Baltazar Vasconcelos

Teaching - Hours

Theoretical classes: 2,00
Laboratory Practice: 2,00
Type Teacher Classes Hour
Theoretical classes Totals 1 2,00
Pedro Baltazar Vasconcelos 2,00
Laboratory Practice Totals 3 6,00
Inês de Castro Dutra 4,00
Pedro Baltazar Vasconcelos 2,00

Teaching language



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



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. Using pseudo-random numbers; examples for simulation
7. Numerical solution of equations; implementation of basic algorithms
8. Processing indexed variables (lists) and text (strings)
9. Writing and reading data as text files; plotting data using external tools (e.g. gnuplot)
10. Recursion

Mandatory literature

Peter Wentworth, Jeffrey Elkner, Allen B. Downey, and Chris Meyers; How to Think Like a Computer Scientist, n/a, 2012. ISBN: n/a
Brad Miller and David Ranum; Learning with Python: Interactive Edition

Complementary Bibliography

Allen B. Downey; Think Python
H. M. Deitel, P. J. Deitel, J. P. Liperi, B. A. Wiedermann;; Python: How to Program

Teaching methods and learning activities

Lectures; program analysis; practical, programming classes.

Evaluation Type

Distributed evaluation with final exam

Assessment Components

designation Weight (%)
Exame 80,00
Participação presencial 0,00
Teste 20,00
Total: 100,00

Eligibility for exams

Frequency of practical classes, according to the rules of the University.
Successful submission at least half of the requested problems (with automated evaluation).

Calculation formula of final grade

80% * Final exam grade + 20% * Mid-term test grade

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