Programming II

Code:

CC1028

Acronym:

CC1028

Keywords
Classification	Keyword
OFICIAL	Computer Science

Instance: 2024/2025 - 2S

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

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
L:B	3	Official Study Plan	3	-	6	48	162
L:BIOINF	32	Official Study Plan	1	-	6	48	162
L:EG	20	The study plan from 2019	1	-	6	48	162
L:F	3	Official Study Plan	3	-	6	48	162
L:G	0	study plan from 2017/18	2	-	6	48	162
			3
L:M	15	Official Study Plan	2	-	6	48	162
			3
L:MA	43	Official Study Plan	2	-	6	48	162
L:Q	0	study plan from 2016/17	3	-	6	48	162

Teaching language

Suitable for English-speaking students

Objectives

The purpose of this course is to understand and develop techniques for using the programing language to develop complex programs and automatize practical tasks. This includes understanding and developing programs with abstract data types (ADT), in particular for 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.

Concepts on the most typical examples of objects (linked lists, stacks, queues, trees, among others).

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
Jake VanderPlas; Python data science handbook: Essential tools for working with data, O'Reilly Media, Inc (https://jakevdp.github.io/PythonDataScienceHandbook/)
Wes McKinney; Python for data analysis: Data wrangling with Pandas, NumPy, and IPython, O'Reilly Media, Inc
Christian Hill; Learning Scientific Programming with Python 2nd Edition, Cambridge University Press , 2020. ISBN: 1108745911 (https://scipython.com/book2/)

Teaching methods and learning activities

- Lectures, with examples of problem solving.

- Practical sessions in the laboratory.

- Homework.

Software

Pycharm Community Edition

keywords

Physical sciences > Computer science > Programming

Evaluation Type

Distributed evaluation without final exam

Assessment Components

designation	Weight (%)
Trabalho prático ou de projeto	50,00
Teste	50,00
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

To get an attendance certificate, the student must attend the required number of lectures. Students who obtained attendance in the previous year are exempted from attending practical classes.

Calculation formula of final grade

Grading will the done via:
- a practical individual project (50%), to be developed during the practical sessons and including additional homework tasks
- a written test on the concepts taught in class (50%).

The final grade will be the arithmetic average of the grades from the project and the test, where the component of the test has a minimum grade of 6 out of 20 for the student to be approved.

Students that do not have the minimum grade of 6 out of 20 in the test can repeat this component in the resit exam.

Special assessment (TE, DA, ...)

Students with working student status or access to the special phase will be able to submit a version of the project in that timing.

Classification improvement

The resit exame will substitute the test grade component in the calculation of the final grade.