TecnoCampus Foundation
eCampus
University Business
eCampus

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General information

Subject type: Mandatory

Coordinator: Maria Dolors Celma Benaiges

Trimester: Third term

Credits: 2

Teaching staff: 

Manuel Guerris Larruy

Academic year: 2025

Teaching course: 1

Languages ​​of instruction

  • Spanish

Competencies / Learning Outcomes

Basic skills

  • CB7. That students know how to apply the knowledge acquired and their ability to solve problems in new or little-known environments within broader (or multidisciplinary) contexts related to their area of ​​study. 

  • CB8 - That students are able to integrate knowledge and face the complexity of making judgments based on information that, being incomplete or limited, includes reflections on social and ethical responsibilities linked to the application of their knowledge and judgments

  • CB9. That students know how to communicate their conclusions and the latest knowledge and reasons that support them to specialized and non-specialized audiences in a clear and unambiguous way. 

Specific skills

  • CE1. Show critical distance autonomy in issues or issues related to the maritime business, logistics and supply chain and in the application of innovative ideas in these areas.

  • CE2. Apply tools and methodologies that facilitate creative and innovative thinking in everyday situations related to the supply chain environment and logistics and maritime businesses.

  • CE5. Design and implement logistics systems, assessing the different possible alternatives, technical and resource constraints and taking into account the coordinated direction and management along the supply chain.

  • CE6. Evaluate the performance of the entire logistics system, taking into account the fulfillment / non-fulfillment of the objectives of quality, cost and service planned to detect and prioritize areas for improvement.

  • CE7. Manage (plan, schedule and control) the flow of materials and information (supply chain flow) through the coordinated direction and management of the areas of purchasing, production and physical distribution of the company. 

Transversal competences

  • CT1. Show willingness to learn about new cultures, experiment with new methodologies and encourage international exchange in the context of logistics, supply chain and maritime business.

  • CT2. Demonstrate entrepreneurial leadership and leadership skills that build personal confidence and reduce risk aversion. 

  • CT3. Develop tasks applying the acquired knowledge with flexibility and creativity and adapting them to new contexts and situations. 

Presentation of the subject

Data Mining and Big Data for Logistics.

 

 

 

Contents

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. 

 

Activities and 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 

Bibliography

Basic

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.

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.

Complementary

Recommended bibliography 

Presentations and data files provided by the teacher 

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

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.