FUNDAMENTALS OF STATISTICS AND DATA ANALYSIS TecnoCampus | Centers attached to Pompeu Fabra University and Business Park

General information

Subject type: Basic

Coordinator: Núria Masferrer Llabinés

Trimester: Third term

Credits: 6

Teaching staff:

Jose Maria Raya Vilchez
Alfredo Smilges Gaffe
Catherine Llaneza Hesse
Laura Muñoz Ortiz

Teaching languages

Spanish
English

There may be materials in Spanish, English and Catalan.

Check the schedules of the different groups to know the language of teaching classes. Although the material can be in any of the three languages.

Skills

Basic skills

B3_Students have the ability to gather and interpret relevant data (usually within their area of study), to make judgments that include reflection on relevant social, scientific or ethical issues
B5_That students have developed those learning skills necessary to undertake further studies with a high degree of autonomy

Specific skills

E9_Use mathematical tools and advanced statistical tools for decision making and contrasting various economic assumptions

General competencies

G1_Be able to work in a team, actively participating in tasks and negotiating dissenting opinions until reaching consensus positions, thus acquiring the ability to learn together with other team members and create new knowledge

Transversal competences

T4_Domain the computer tools and their main applications for the ordinary academic and professional activity

Description

Ability to recognize, become familiar with and use statistical techniques when making market decisions. All this approach will make compatible the ability to manually calculate the various tools for a small set of data with the ability to use and analyze and interpret the outputs of statistical software. The knowledge worked will allow us to give a first answer to questions such as: How do we know which company pays more salaries? Are the salaries of the companies very similar or are they very scattered? Are sales and advertising associated? What does the Google correlate tool tell us? Can we know the profitability of a year of education? How much does the price of a house in Barcelona increase when its surface area increases by one square meter? How much is the seasonal effect of August due to the number of tourists? How is the probability that a blog will have 1000 visits tomorrow if we know that visits to blogs in Spain follow a certain normal distribution? Can an individual’s personality be quantified through data mining?

Learning outcomes

Master quantitative and qualitative techniques to solve economic and / or company problems for decision making.
Understand and apply the basic concepts of probability, basic statistical calculations and the computer tools that facilitate them.

Working methodology

Theoretical sessions

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

MD2. Conferences: Face - to - face sessions or broadcast on streaming, both in the classrooms of the university and in the framework of another institution, in which one or more specialists present their experiences or projects to the students.

Guided learning

MD5. Seminars: Face-to-face format in small work groups (between 14 and 40). These are sessions linked to the face-to-face sessions of the subject that allow to offer a practical perspective of the subject and in which the participation of the student is key.

Autonomous learning

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

MD11. Non-contact tutorials: for which the student will have telematic resources such as e-mail and ESCSET intranet resources.

The face-to-face sessions with the whole group will combine theory sessions with hand-held exercise resolution sessions and through case studies with data from real companies.

In the practical sessions students will work on exercises and databases with the Minitab / SPSS / R program (to be determined). You will need to bring a laptop to the classroom.

Learning activities

Individual work (inside and outside the classroom)

Data mining exercises

Exercise Descriptive statistics.

Descriptive statistical test.

Exercise association between quantitative variables.

Exercise of time series.

Association test between quantitative and qualitative variables and time series.

Binomial distribution exercise.

Normal distribution exercise.

Probability test.

Work in group

Work with computer software.

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.

Evaluation system

The quarterly evaluation will take into account the following aspects with the weights indicated:
- Final exam of the term: 60%. Minimum grade 3.5 out of 10.
- Work -in group- with database: 30%
- Participation in activities proposed in the classroom: delivery of exercises and proposed practices: 10%

There will be a recovery at the start of the next quarter. Only the exam will be retaken (60% keeping the minimum grade to average) and the other 40% will keep the grade of the quarterly assessment.

A student who has not applied for the first call CANNOT apply for recovery.

REFERENCES

Basic

RAYA, J. (2012): Statistics applied to business and marketing. Prentice Hall

MOORE, Mc. CABE (2005), Introduction to the practice of Statistics. Freeman

SPIEGELHALTER, D. (2019): The art of statistics: learning by data. Pelican

Complementary

NEWBOLD, PAUL, Carlson, W., Thorne, W. (2007), Statistics for Business and the Economy, 6th edition, Madrid, Prentice Hall

1166 - FUNDAMENTALS OF STATISTICS AND DATA ANALYSIS

Degree in Business Administration and Innovation Management