Programming and Data Science

Recommended Prerequisites

NA

Teaching Methods

This Curricular Unit is structured in theoretical-practical classes taught in a computer room. An initial presentation will be made by the teacher on the different topics, which will be followed by the autonomous resolution of practical exercises by the students.

Learning Outcomes

AAfter completing the course, students should know and understand the fundamental principles of algorithmic thinking and programming, as well as know and understand the main steps that make up the process associated with Data Science.

They must also demonstrate the ability to develop applications that automate the different data processing stages associated with the Data Science process.

Work Placement(s)

No

Syllabus

1. Introduction to computer programming

1.1. Programming languages and Integrated Development Environment (IDE)

1.2. Basic sintax

1.3. Data storage and manipulation structures

1.4. Control flow structures

1.5. Functions and subroutines

2. Stages in the Data Science process

2.1. Importing data

2.2. Data cleaning and organization

2.3. Exploring and visualizing data

2.4. Analysis models

2.5. Results report

Head Lecturer(s)

Manuel Paulo Albuquerque Melo

Assessment Methods

Assessment
Periodic or by final exam as given in the course information: 100.0%

Bibliography

Programação em Python: fundamentos e resolução de problemas, Ernesto Costa, FCA, 2015.

 Python Data Science Handbook, Jake VanderPlas, O'Reilly, 2016 (https://jakevdp.github.io/PythonDataScienceHandbook/).