TecnoCampus Foundation
eCampus
University Business
eCampus

What are you looking for?

General information

Subject type: Optional

Coordinator: Jesus Ezequiel Martínez Marín

Trimester: Third term

Credits: 3

Teaching staff: 

Paula Bel Piñana

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. 

  • 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

  • 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.

  • 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

MARKET ANALYSIS WITH PREDICTIVE ANALYSIS.

Learning outcomes

  • Advise customers of logistics services, to make decisions that pursue the success of their business adventure. 

  • Carry out studies of potential markets in order to find new customers and new services. 

  • Carry out market research, with multidisciplinary groups, where the segregation of functions is an essential tool. 

  • Recognize the competition, its strengths and weaknesses, to define the appropriate strategies 

Working methodology

Theoretical sessions 

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

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

 

Guided learning 

  • Practical exercises: To complement the theoretical explanations, practice of solving exercises. 

  • Case study: Dynamics that starts from the study of a case, which serves to contextualize the student in a specific situation. 

Contents

  • Fundamentals of market research 

  • Data preparation 

  • Data types, variables and measurements 

  • Single and multiple regression model 

  • Logistic regression model 

  • Poisson and Binomial Negative Regression Model 

  • Decision Trees and Random Forest 

Learning activities

Theoretical sessions 

  • Master class

  • presentations

 

Guided learning 

  • Practical exercises

  • Case study 

 

Evaluation system

It will consist of the continuous evaluation, with the following percentages: 

 

  • 40%: Continuous evaluation 

  • 60%: Final exam 

 

The continuous assessment (40% of the mark) will consist of completing different test-type questionnaires at the end of each of the topics presented in class. 

 

The final exam (60% of the mark) consists of 2 parts. One part with test-type questions and one part with open-ended questions. 

REFERENCES

Basic

James, G., Witten, Daniela, Hastie, T., Tibshirani, R. (2021). An Introduction to Statistical Learning with Applications in R. 2nd ed. Springer.

Angrist, JD, Pischke, JS (2014). Mastering 'Metrics: The Path from Cause to Effect. 1st ed. Princeton University Press. 

Artís O., M., Suriñach C., J. (2012). Topics of econometrics. Editorial UOC.

Bardina, X., Farré, Mercè. (2009). Descriptive statistics. Publications service. Autonomous University of Barcelona.

Alea R., Ma. Victoria, Jiménez G., E., Muñoz V., Carme, Viladomiu C. Núria. (2015). Statistics I: Theory and exercises. Barcelona, ​​OMADO. Barcelona University.

Breiman, L., Cutler, Adele. (2018). Breiman and Cutler's Random Forests for Classification and Regression. CRAN.

Greene, WH (2012). Econometric Analysis. 7th ed. Prentice Hall. 

Malhotra, NK (2014). Market research: essential concepts. 1st ed. Anaya Group General Publications. 

Torfs, P., Brauer, Claudia. (2014). A (very) short introduction to R. The Netherlands, Hydrology and Quantitative Water Management Group. Wageningen University.

Wickham, H., Grolemund, G. (2016). R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. 1st ed. O'Reilly Media.

Complementary

Torfs, P., Brauer, Claudia. (2015). A (very) short introduction to R using RStudio. The Netherlands, Hydrology and Quantitative Water Management Group. Wageningen University.