nireEHU 
Subject
Data Analytics for Engineering
General details of the subject
- Mode
- Face-to-face degree course
- Language
- English
Description and contextualization of the subject
This module focuses on teaching the practical programming skills required for analysis of engineering data sets.Teaching staff
| Name | Institution | Category | Doctor | Teaching profile | Area | |
|---|---|---|---|---|---|---|
| BLANCO ILZARBE, JESUS MARIA | University of the Basque Country | Profesorado Titular De Universidad | Doctor | Not bilingual | Fluid Mechanics | jesusmaria.blanco@ehu.eus |
| EGUIA LOPEZ, PABLO | University of the Basque Country | Profesorado Agregado | Doctor | Not bilingual | Electrical Engineering | pablo.eguia@ehu.eus |
| ESTEBAN ALCALA, GUSTAVO ADOLFO | University of the Basque Country | Profesorado Titular De Universidad | Doctor | Not bilingual | Fluid Mechanics | gustavo.esteban@ehu.eus |
Competencies
| Name | Weight |
|---|---|
| Modelling of engineering scenarios to understand context, data flows/sources etc. Data integration | 40.0 % |
| Data processing and cleaning of integrated data | 15.0 % |
| Data exploration, visualisation and comprehension of dataset (e.g. data distribution, summary statistics etc.) | 15.0 % |
| Building, testing and deploying predictive models | 30.0 % |
Study types
| Type | Face-to-face hours | Non face-to-face hours | Total hours |
|---|---|---|---|
| Lecture-based | 24 | 51 | 75 |
| Seminar | 14 | 0 | 14 |
| Applied classroom-based groups | 12 | 24 | 36 |
Training activities
| Name | Hours | Percentage of classroom teaching |
|---|---|---|
| Classroom/Seminar/Workshop | 12.0 | 100 % |
| Drawing up reports and presentations | 24.0 | 0 % |
| Individual study | 51.0 | 0 % |
| Lectures | 24.0 | 100 % |
| Seminars | 14.0 | 100 % |
Assessment systems
| Name | Minimum weighting | Maximum weighting |
|---|---|---|
| Drawing up reports and presentations | 0.0 % | 100.0 % |
Learning outcomes of the subject
Analyse data in Engineering contexts.Manipulate and transform data.
Produce visualisations of data.
Formulate data models.
Deploy data models.
Evaluate machine learning algorithms.
Temary
Lesson 1. Introduction and Data IntegrationLesson 2. Probability and Statistics
Lesson 3. Data processing and cleaning.
Lesson 4. Data exploration and visualisation.
Lesson 5. Programming Fundamentals
Lesson 6. Data Visualisation
Lesson 7. Database Management
Lesson 8. Model Development
Lesson 9. Model Deployment
Lesson 10. Model Testing
Lesson 11. Engineering Case Study
Lesson 12. Engineering Case Study