General information

Subject type: Mandatory

Coordinator: Jesus Ezequiel Martínez Marín


Credits: 2

Teaching staff: Manuel Guerris Larruy


Data Mining and Big Data for Logistics.


This subject has methodological and digital resources to make possible its continuity in non-contact mode in the case of being necessary for reasons related to the Covid-19. In this way, the achievement of the same knowledge and skills that are specified in this teaching plan will be ensured.

The TecnoCampus will make available to teachers and students the digital tools needed to carry out the course, as well as guides and recommendations that facilitate adaptation to the non-contact mode.

Learning outcomes

  • Recognize the concepts of Data Mining and Big Data through the story of real cases and the operation of the main algorithms used today. 

  • Evaluate the different tools available on the market and make a choice according to the logistical needs of the company. 


Working methodology

The course sessions will combine the following teaching methodologies: 


Theoretical sessions 

  • MD1.Master class: Expository class sessions based on the teacher's explanation attended by all students enrolled in the subject 

  • MD3. Presentations: Multimedia formats that support face-to-face classes 


Guided group learning 

  • MD5. Seminars: Face-to-face format in small work groups. These are sessions linked to the face-to-face sessions of the subject that allow to offer a practical perspective and in which the participation of the student is key. 

  • MD7. Case study: Dynamics that starts from the study of a case that serves to contextualize the student in a specific situation, the teacher can propose different activities, both individually and in groups, among their students 


Individual Autonomous Learning 

  • MD9. Solving exercises and problems: Non-contact activity dedicated to solving practical exercises based on the data provided by the teacher 


Big data:  

  • History, definition and context 

  • Big data as a strategic factor in companies 

Data and its treatment 

  • Data structure 

  • Storage technologies 

  • Languages. 

Data Mining:  

  • What is Data Mining 

  • Objectives and potential 

  • Advanced methods of analysis: machine learning 

Computer tools for processing 

  • Free software 

  • Own software 

  • Software as a Service (SaaS) 

Big data and logistics 

  • Specific applications 

  • Sector trend. 


Learning activities

Theoretical sessions 

  • MD1. Master class.

  • MD3. Presentations.


Guided group learning 

  • MD5. Seminars.

  • MD7 Case study.

Individual Autonomous Learning 

  • MD9. Solving exercises and problems. 

Evaluation system

  • Participation in the activities proposed in the classroom: 30% of the final grade 

  • Individual activity to present once the sessions of the subject end: 70% of the final mark 



Samelson, S. (2019). Machine Learning: The Absolute Complete Beginner's Guide to Learn and Understand Machine Learning From Beginners, Intermediate, Advanced, To Expert Concepts. Amazon.

Marr, B. (2015). Big Data: Using SMART Big Data, Analytics and Metrics to Make Better Decisions and Improve Performance. Wiley.

Roldán, María C. (2013). Pentaho Data Integration Beginner's Guide. 2nd ed. Packt Publishing.


Recommended bibliography 

Presentations and data files provided by the teacher 

Use of software to develop exercises and datamining cases. Depending on the need, OpenSource WEKA, R Studio with R, or RapidMiner Studio will be used and students will be informed in advance so that they can install the necessary software on their computers. 

Robertson, PW (2020). Supply Chain Analytics: Using Data to Optimize Supply Chain Processes. 1st ed. Routledge.