Programming II

Code:

CC1022

Acronym:

CC1022

Level:

100

Keywords
Classification	Keyword
OFICIAL	Computer Science

Instance: 2019/2020 - 2S

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

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	27	The study plan from 2019	1	-	6	56	162
L:F	2	Official Study Plan	3	-	6	56	162
L:G	0	study plan from 2017/18	2	-	6	56	162
L:M	2	Official Study Plan	2	-	6	56	162
			3
L:Q	0	study plan from 2016/17	3	-	6	56	162

Teaching language

Suitable for English-speaking students

Objectives

The purpose of this course is to develop the ability of using a programing language to develop complex programs, and to offer an introduction to  data extraction, processing, and visualization.

 

Learning outcomes and competences

The student is able to:

• Using the basic python data structures with confidence.
• Program with the adequate level of abstraction and encapsulation.
• Produce correct, well-structured, and well-documented code.
• Extract and process data from diverse sources.
• Use external libraries for the visualization of numerical and geospacial data.

Note: all coding in this course is to be done in Python.

Working method

Presencial

Program

Review of Python. Basic Python data types. Data types for collections: lists, queues, stacks, tuples, dictionaries and sets. Definition of new data types: class, objects, and methods.

Three basic programming principles: encapsulation, abstraction and separation of concerns.

Introduction to data extraction and processing. The use of external libraries. Data visualization. Introduction to geospacial data visualization.

Mandatory literature

Allen Downey; How to think like a computer scientist. ISBN: 0-9716775-0-6

Complementary Bibliography

Daniel Y. Chen; Pandas for Everyone, Addison-Wesley

Teaching methods and learning activities

- Lectures, with examples of problem solving.

- Practical sessions in the laboratory.

- Homework.

Software

Pyzo
Idle

keywords

Physical sciences > Computer science > Programming

Evaluation Type

Distributed evaluation with final exam

Assessment Components

designation	Weight (%)
Trabalho prático ou de projeto	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

All enroled students are considered to have attended the course.

Calculation formula of final grade

The final grade will be the grade of the final project.

Classification improvement

The project may be improved and re-submitted during appeal season, and in that case new functionality will be required.