Foundations of Data Science

Information

Teachers: Sérgio Matos

Duration: One semester

Work hours: 162

Contact hours: 45

ECTS: 6

Scientific area: Computer Science and Mathematics

Objectives

This course offers an overview of the entire chain of processes, challenges, and different application areas of Data Science.

Learning Outcomes

At the end of this course, students should:

be able to explain the chain of processes and the challenges involved in a data science project;
know and understand the various types of data, the collection processes, the characteristics of large-scale data, including speed, variety, variability, veracity;
know and apply methods for processing and cleaning data, dealing with wrong and missing data;
know, apply and interpret the results of data exploration and representation methods;
understand and know how to deal with the implications of data science in terms of ethics and privacy.

Grading

The assessment will be based on a practical project, with partial deliveries, report and presentation.

Methodology

This course will follow a predominantly expositive approach to present and relate the different topics, supported by the analysis of typical cases whenever pertinent. This exposition will be complemented with the incremental development of projects, which will allow students to consolidate the theoretical aspects and acquire practical expertise.

Syllabus

Introduction and fundamental concepts
Tools and frameworks
Data sources and types
Data collection
Data quality, cleaning and pre-processing
Data storage and management
Dimensionality reduction
Exploratory Data Analysis
Visualization and presentation of results
Operationalization
Ethics and Privacy