109817 - BIG DATA

Subject type: Optional

Coordinator: Joan Triadó Aymerich

Trimester: Second term

Credits: 4

Academic year: 2025

Teaching course: 4

• English

Course taught in English

Optional subject within the Intelligent Manufacturing block of Industry 4.0.

This course introduces students to the fundamental concepts and practical applications of Big Data in the context of Industry 4.0. With a focus on real industrial data, students will learn how to prepare, reduce, and analyze large-scale data sets to uncover hidden patterns and generate actionable knowledge.

Topics covered include essential data preparation techniques, dimensionality reduction, and machine learning, both supervised (regression, XGBoost, and an introduction to deep learning) and unsupervised (K-means, hierarchical clustering, and DBSCAN). Special emphasis is placed on data quality, model interpretability, and visualization, with a practical orientation for working in modern analytical environments.

The course provides students with the tools and knowledge necessary to support data-driven decision-making in smart manufacturing systems.

The final grade is the weighted sum of the grades for the learning activities:

Q = 0.20 Pex1 + 0.40 Proj + 0.15 ExiPar + 0.25 Lab

Observations relating to Recovery:

The theory part of the Pex1 subject is recoverable as well as the Project part. The other parts are not recoverable. For students attending the resit exam their Pex1 grade will be the one obtained in this test and their final grade (Q) will be calculated with the formulas detailed above and in no case will it exceed 7.

Rules for carrying out the activities

Remarks:

To pass the assessment activities, students must demonstrate the MECES Level - 2:

• (point c) have the ability to collect and interpret data and information on which to base their conclusions, including, where necessary and relevant, reflection on issues of a social, scientific or ethical nature in the field of their field of study

• (point e) know how to communicate to all types of audiences (specialized or not) in a clear and precise way, knowledge, methodologies, ideas, problems and solutions in the field of their field of study;

• (point f) be able to identify their own training needs in their field of study and work or professional environment and to organize their own learning with a high degree of autonomy in all types of contexts

For each activity, teachers will be informed of the particular rules and conditions that govern them

The one-to-one activities presuppose the student's commitment to carry them out individually and without any collaboration with other people. All activities in which the student does not comply with this commitment to individuality will be considered suspended (grade 0), regardless of their role (sender or receiver) and without this excluding the possible application of other sanctions in accordance. with the current Disciplinary Regime.

Likewise, the activities to be carried out in groups presuppose the commitment on the part of the students who make it up to carry them out within the group and without any kind of collaboration with other groups or people who are alien (group individuality). All activities in which the group has not respected this commitment regardless of its role (sender or receiver) and without this excluding the possible application of other sanctions in accordance with the current Disciplinary Regime will be considered suspended (rating 0).

In the case of activities that can be done in groups, when in any of them the commitment of group individuality is not respected and / or fraudulent means are used in its accomplishment, the qualification of the activity will be, for all members of the group, of 0 points (Activity Note = 0) and without this excluding the possible application of other sanctions in accordance with the current Disciplinary Regime.

Any undelivered activity will be considered scored with zero points.

It is up to the teachers to accept or not deliveries outside of the indicated deadlines. In the event that these late deliveries are accepted, it is up to the teacher to decide whether to apply any penalty and its amount.

Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies (MIT Press) 1st Edition John D. Kelleher, Brian Mac Namee and Aoife D'Arcy

The MIT Press; 1 edition July - 2015

ISBN-978 0262029445

Practical Big Data Analytics: Hands-on techniques to implement enterprise analytics and machine learning using Hadoop, Spark, NoSQL and R. by Nataraj Dasgupta (Packt Publishing; 1st Ed - 2018)

Practical Machine Learning with H2O: Powerful, Scalable Techniques for Deep Learning and AI 1st Edition Darren Cook

O'Reilly Media; 1 edition, December 2016

ISBN-978 1491964606