TecnoCampus Foundation
eCampus
University Business
eCampus

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General information

Subject type: Mandatory

Coordinator: Jesus Ezequiel Martínez Marín

Trimester: Third term

Credits: 2

Teaching staff: 

Manuel Guerris Larruy

Skills

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. 

Description

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 

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. 

 

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 

REFERENCES

Basic

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.

Complementary

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.