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ECTS Course Overview



Business Analytics

* Exchange students do not have to consider this information when selecting suitable courses for an exchange stay.

Course Unit Code156-0589/01
Number of ECTS Credits Allocated3 ECTS credits
Type of Course Unit *Choice-compulsory
Level of Course Unit *First Cycle, Second Cycle
Year of Study *
Semester when the Course Unit is deliveredWinter Semester
Mode of DeliveryFace-to-face
Language of InstructionEnglish, Spanish
Prerequisites and Co-Requisites Course succeeds to compulsory courses of previous semester
Name of Lecturer(s)Personal IDName
AND0096doc. Antonio Rodríguez Andrés, Ph.D.
Summary
Course contents includes the basics of R, pre-processing and data visualization techniques, study and application of linear, non-linear models, and basic panel data models. Students will apply and analyze the results using the R software. The student will prepare and present both in oral and written form a research project in which the concepts studied will be discussed and applied to a particular problem.
Learning Outcomes of the Course Unit
Passing the course, student will be able to work with important basics of the descriptive analysis and statistics, data visualization, regression analysis, binary choice models, count and panel data models using statistical software R.
Course Contents
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Recommended or Required Reading
Required Reading:
LANDER, Jared P. [i]R for everyone: Advanced Analytics and Graphics.[/i] Crawfordsville: Adison Wesley, 2014. ISBN-13: 978-0-321-88803-7.
VERZANI, John. [i]Using R for introductory statistics.[/i] [online]. Available from: https://cran.r-project.org/doc/contrib/Verzani-SimpleR.pdf.
WOOLDRIDGE, Jeffrey M. [i]Introductory econometrics: a modern approach. [/i] Sixth edition. Boston: Cengage Learning, 2016. ISBN 978-1-305-27010-7.
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Recommended Reading:
DOUGHERTY, Christopher. [i]Introduction to econometrics. [/i] Fifth edition. Oxford: Oxford University Press, 2016. ISBN 978-0-19-967682-8.
JAMES, G., D. WITTEN, T. HASTIE and R. TIBSHIRANI. [i]An Introduction to Statistical Learning Applications in R. [/i] Stanford: Springer, 2012. Available from: http://www-stat.stanford.edu/~tibs/.
NEUSSER, Klaus. [i]Time series econometrics.[/i] Switzerland: Springer, 2016. Springer texts in business and economics. ISBN 978-3-319-32861-4.
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Planned learning activities and teaching methods
Lectures, Individual consultations
Assesment methods and criteria
Tasks are not Defined