Programming I

Code:

CC1015

Acronym:

CC1015

Level:

100

Keywords
Classification	Keyword
OFICIAL	Computer Science

Instance: 2021/2022 - 1S

Active?	Yes
Web Page:	http://www.dcc.fc.up.pt/~jpp/P1
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:B	1	Official Study Plan	3	-	6	56	162
L:EG	30	The study plan from 2019	1	-	6	56	162
L:F	81	Official Study Plan	1	-	6	56	162
			3
L:G	15	study plan from 2017/18	1	-	6	56	162
			3
L:Q	1	study plan from 2016/17	3	-	6	56	162

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

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

Complementary Bibliography

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

Teaching methods and learning activities

Lectures; program analysis.
Practical, programming classes.

Software

Python 3.x

keywords

Physical sciences > Computer science > Programming

Evaluation Type

Distributed evaluation with final exam

Assessment Components

designation	Weight (%)
Exame	60,00
Teste	40,00
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.

Additional conditions:
- Having successfully completed at least half of the proposed problems in class (with automatic correction).

Calculation formula of final grade

20% Average mark of exercises during theoretical classes
20% Average mark of exercises during practical classes
60% Final exam grade
(Minimum grade at the final exam: 50%)

Classification improvement

Exam (with weight 100% on the grade)

Observations

Jury: João Pedro Pedroso, Rita Ribeiro.