Programming II

Code:

CC1022

Acronym:

CC1022

Level:

100

Keywords
Classification	Keyword
OFICIAL	Computer Science

Instance: 2021/2022 - 2S

Active?	Yes
Web Page:	https://github.com/hpacheco/progii
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	2	Official Study Plan	3	-	6	56	162
L:EG	18	The study plan from 2019	1	-	6	56	162
L:F	2	Official Study Plan	3	-	6	56	162
L:G	1	study plan from 2017/18	2	-	6	56	162
L:M	2	Official Study Plan	2	-	6	56	162
			3
L:Q	2	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 automatize practical tasks of data exploration, 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 and in different formats.
• 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

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

Some basic familiarity with the Python language and its development environment is preferential, as present in the program of the Programming I Curricular Unit.

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. Structuring of code using modules.

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

Introduction to data extraction and processing. Reading of textual data in different formats and conversion to Python data structures. Programatic manipulation and treatment of data.

Introduction to data visualization. The use of external libraries. Visualization of numeric and geospacial data.

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

Pycharm Community Edition 2020

keywords

Physical sciences > Computer science > Programming

Evaluation Type

Distributed evaluation without final exam

Assessment Components

designation	Weight (%)
Trabalho prático ou de projeto	70,00
Prova oral	30,00
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

Grading will the done via a practical project (70%), to be developed during the practical sessons and including additional homework tasks. An oral defense if the practical project is also planned (30%).

The final grade will be the sum of the grades of the practical project and the oral defense.

Classification improvement

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