TecnoCampus Foundation
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University Business
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General information

Subject type: Basic

Coordinator: Adso Fernández Baena

Trimester: Second term

Credits: 6

Teaching staff: 

Moses Burset Albareda
Xavier Font Aragonés 

Skills

Basic skills

  • B1_That students have demonstrated knowledge and understanding in a field of study that is based on general secondary education, and is accustomed to finding at a level that, although with the support of advanced textbooks, also include some aspects that involve knowledge from the forefront of your field of study

     

  • 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

     

  • B4_That students can convey information, ideas, problems and solutions to both specialized and non-specialized audiences

     

Specific skills

  • EFB1_Ability to solve mathematical problems that may arise in engineering. Ability to apply knowledge about: linear algebra, differential and integral calculus, numerical methods, numerical algorithms, statistics and optimization

     

Transversal competences

  • T1_That students know a third language, which will be preferably English, with an adequate level of oral and written form, according to the needs of the graduates in each degree

     

  • T2_That students have the ability to work as members of an interdisciplinary team either as one more member, or performing management tasks in order to contribute to developing projects with pragmatism and a sense of responsibility, making commitments taking into account the available resources

     

Description

Understanding and the ability to analyze random phenomena can be of great relevance in some branches of computer engineering, such as in the processing and analysis of biological information (bioinformatics). There are processes that, by their very nature, are random (such as the study of the time that can pass until a machine breaks down, or what size an animal will be, ...) which, paradoxically, does not involve that are not treatable and / or modelable phenomena.

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.

Learning outcomes

1.- Describe the general structure of a statistical study. Define the objectives, the acquisition of data together with a first exploration of the same, analyze them, draw conclusions and present the results (using some type of data analysis software)
2.- To synthesize the information (graphically and numerically) by means of descriptive statistics
3.- To know how to apply the basic principles of combinatorics, using the main properties of probability theory, as well as to solve specific problems.
4.- Identify the reference distribution in a specific random phenomenon
5.- Identify the typical situations of the normal distribution
6.- Solve statistical inference problems, either using confidence intervals or hypothesis tests.
 

Working methodology

All the theoretical concepts of the subject will be exposed in the theory classes (large groups), although we will be constantly mixing the theory with examples and exercises, so it would most likely be more appropriate to talk about theoretical-practical sessions.

Some of the exercises will be solved in class and others will remain as individual work of learning and consolidation of concepts. You can take advantage of the practical sessions, or laboratory (small groups), to solve some of the exercises or to propose new ones, based on those that have already been worked on in the theoretical-practical sessions. These activities, due to their brief and sometimes optional nature, will serve the student as an instrument of self-assessment of their achievement of the contents of the subject.

 

Contents

1.-Descriptive statistics
2.-Combinatorics and probabilities
3.-Distributions
4.-Inference
5.-Regressions

 

Learning activities

Students will receive a set of activities (short exercises, problems, ...) that will be the basis for their learning. Sometimes these activities will be solved in the theory sessions, other times they will be solved in the practical sessions and other times they will become individual tasks to solve after class.

In order to gather evidence of the achievement of the expected learning outcomes, the following evaluative activities are carried out:

Exams: there will be due individual written tests. A partial exam (P) and a final one (F), where the whole syllabus of the subject will enter. The score will be calculated as: maximum ((P + F) / 2, F). In short, the partial can only raise the grade, never lower it. This part will have the possibility of being recovered, if it has been suspended.

Work in group: towards the end of the subject, within the practical sessions, it will be necessary to write and present a report in front of the class where the results of a statistical study applied to specific data, obtained from some public database ( such as the INE).

Any undelivered activity will be considered scored with zero points

 

Evaluation system

The final grade will be the weighted sum of the grades of the learning activities, ie:

Final_qualification = 0.70 exam_note + 0.30 group_work

REFERENCES

Basic

Hossein Pishro-Nik, Introduction to Probability, Statistics, and Random Processes. Kappa Research, LLC 2014

MICHAEL BARON. Probability and Statistics for Computer Scientists. 2nd Ed. CRC Press 2014

Joseph K. Blitzstein, Jessica Hwang, Introduction to Probability, Chapman & Hall / CRC Texts in Statistical Science Har / Psc Edition 2014

Complementary

Pierre Lafaye de Micheaux, Rémy Drouilhet, Benoit Liquet; The R Software: Fundamentals of Programming and Statistical Analysis (Statistics and Computing), springer 2013th Edition