Class Applied Programming for Data Science

In this CU, we intend to develop knowledge about fundamental concepts of procedural, functional and object-oriented programming using the Python programming language. Students should also develop skills in the creation and implementation of algorithms using different types of data. The skills developed in this UC allow a deep understanding of what is at the base of programming languages and how to make an efficient program and their application on data science.

The curricular unit program is:
PC1. Introduction to programming
PC2 Introduction to Python language and its syntax
PC3. Introduction to the Jupyter Notebook, Google Collab and Moodle CodeRunner work environments.
PC4 Python syntax. Variables and operators. Simple data types.
PC5: Flow control, with decision makers and cycles. Functions.
PC6: Composed data types: lists, tuples, sets and dictionaries
PC7: Manipulation and management of files (text, JSON, CSV).
PC8: Data visualization with matplotlib.
PC9: Functional programming. Comprehensions, lambda, map, filter, reduce.
PC10: Object-oriented programming. classes.

Teaching methodologies (TM) including assessment:
TM1: Expositional / Experimental/ Active: Brief theoretical exposition according to the syllabus, practical problem solving at informatics laboratory.
TM2: Self-study: individual work on a weekly basis working problems.
Important assessment notes:
– Mandatory Minimum score of 10 in each component.
– All evaluation components are mandatory

• Martins, P. (2019). Programação em Python. IST Press. 3ª Ed.
• Grus, J. (2015). Data science from scratch: first principles with python. O’Reilly Media, Inc.
• Zinoviev, D. (2018). Complex Network Analysis in Python. Edited by Adaobi Obi Tulton. Pragmatic Bookshelf.
• Belorkar, A., Guntuku S.C., Hora, S. and Kumar, A. (2020). Interactive Data Visualization with Python. 2nd edition. Packt Publishing. Birmingham UK.

A combinar com o docente.