-
Presentation
Presentation
This curricular unit is the first touchpoint of the students with the various dimensions of Data Science. It is a bird's eye view of Data Science, covering topics at a high level, to be developed in detail throughout the Data Science undergraduate curriculum.
In this unit we aim to provide the student with the perspectives of how data science is viewed and used in the academic, business and societal domains. Basic principles, its beginnings and historical development, interactions and relations with other disciplines, its tools and ground theoretical principles will be discussed, as well as some of the ethical issues that its usage may imply.
-
Class from course
Class from course
-
Degree | Semesters | ECTS
Degree | Semesters | ECTS
Bachelor | Semestral | 6
-
Year | Nature | Language
Year | Nature | Language
1 | Mandatory | Português
-
Code
Code
ULHT6638-23084
-
Prerequisites and corequisites
Prerequisites and corequisites
Not applicable
-
Professional Internship
Professional Internship
Não
-
Syllabus
Syllabus
S1 - Introduction to the Course
S2 - Data, Data Types and Data Manipulation
- Metadata
- Bias
- Data Types and representations
- Processing and Storage
S3 - Exploratory Data Analysis
- Centrality and dispersion measures
- Visualization
S4 - Data Modeling, Databases, Data Extraction, Integration, and Processing
- Data origin
- Data sources
- Extraction
- Data vs. Information
- Processing
S5 - Machine Learning
- Supervised models
- Unsupervised models
S6 - Case Study
S7 - Ethics in Data Science
S8 - Data Science Projects - Methodologies
-
Objectives
Objectives
After successfully completing this curricular unit, the student should have achieved the following learning objectives (LO):
LO1. Understand and explain what is data. Distinguish between different types of data. Identify and classify data sources.
LO2. Be able to defend the need, usefulness and value of applying data science to scientific, management and social problems, describing, predicting and pescribing actions to address them.
LO3. Distinguish data science from related disciplines, identifying similarities and differences.
LO4. Examine the implications of data collection in science, business and society, and its ethical framework.
LO5. Understand the contextual needs of a data analysis model and be able to draw and create a simple analytical model.
-
Teaching methodologies and assessment
Teaching methodologies and assessment
The classes are complemented with practical examples and tutorials, to be developed in the classroom under the guidance of the instructor, as well as independently during dedicated hours outside the classroom.
-
References
References
Wheelan, C. (2013). Naked statistics: Stripping the dread from the data. WW Norton & Company. ISBN 978-0-393-07195-5
O'Neil, C., & Schutt, R. (2013). Doing data science: Straight talk from the frontline. O'Reilly Media, Inc. ISBN: 9781449358655
-
Office Hours
Office Hours
-
Mobility
Mobility
No
