Introduction to Data Science
General details of the subject
- Mode
- Face-to-face degree course
- Language
- English
Description and contextualization of the subject
This short course aims to give the students a meaningful introduction and hands-on experience of what data science is, to teach them how to continue to learn data science, and to give them the desire and skills needed to do so. The provided materials will perform as an index. For each topic, the aim is to give a cursory overview and a simple demonstration of what the topic under investigation is, and guide the students to bigger and better resources to really dive into it.Teaching staff
Name | Institution | Category | Doctor | Teaching profile | Area | E-mail |
---|
CASILLAS RUBIO, ARANTZA | University of the Basque Country | Profesorado Agregado | Doctor | Not bilingual | Computer Languages and Systems | arantza.casillas@ehu.eus |
JUSTO BLANCO, RAQUEL | University of the Basque Country | Profesorado Agregado | Doctor | Bilingual | Computer Languages and Systems | raquel.justo@ehu.eus |
RODRIGUEZ FUENTES, LUIS JAVIER | University of the Basque Country | Profesorado Agregado | Doctor | Not bilingual | Computer Languages and Systems | luisjavier.rodriguez@ehu.eus |
Competencies
Name | Weight |
---|
Que los estudiantes sean capaces de adquirir y relacionar adecuadamente entre sí los conocimientos necesarios para poder abordar y asimilar el estudio de los conceptos teóricos y de aplicación práctica en el ámbito de la asignatura | 100.0
%
|
Study types
Type | Face-to-face hours | Non face-to-face hours | Total hours |
---|
Lecture-based | 18 | 30 | 48 |
Applied laboratory-based groups | 12 | 15 | 27 |
Training activities
Name | Hours | Percentage of classroom teaching |
---|
Autonomous work | 45.0 | 0
%
|
Classroom/Seminar/Workshop | 18.0 | 100
%
|
Laboratory/Field | 12.0 | 100
%
|
Assessment systems
Name | Minimum weighting | Maximum weighting |
---|
Practical tasks | 30.0
%
| 50.0
%
|
Works and projects | 40.0
%
| 70.0
%
|
Temary
1- Overview
2- Computing with Python
3- Data collection, transformation, exploration and visualization
4- Machine learning for data modeling
Bibliography
Compulsory materials
- Apuntes de la asignatura