Educational guide

IDENTIFYING DATA

2024_25

Subject

DATA SCIENCE I

Code

00717017

Study programme

0717 - GRADO INGENIERÍA DATOS INTELIGENCIA ARTIFICIAL

Descriptors

Credit.

Type

Year

Period

Compulsory

Second

Language

Castellano

Prerequisites

Department

MATEMATICAS

Coordinador

QUIROS CARRETERO , ALICIA

E-mail

aquic@unileon.es
mttrom@unileon.es

Lecturers

TROBAJO DE LAS MATAS , MARÍA TERESA

QUIROS CARRETERO , ALICIA

Web

http://

General description

Introductory course in mathematical statistics for data science.

Tribunales de Revisión

Tribunal titular
Cargo	Departamento	Profesor
Presidente	MATEMATICAS	GOMEZ PEREZ , JAVIER
Secretario	MATEMATICAS	ARIAS MOSQUERA , DANIEL
Vocal	MATEMATICAS	ARANA SUAREZ , MARIA VICTORIA

Tribunal suplente
Cargo	Departamento	Profesor
Presidente	MATEMATICAS	SANTAMARIA SANCHEZ , RAFAEL
Secretario	MATEMATICAS	SAEZ SCHWEDT , ANDRES
Vocal	MATEMATICAS	VEGA CASIELLES , SUSANA

Competencias

Code
A18963
A18966
A18979
A18989
B5800
B5801
B5802
B5806
B5807
B5808
C2	CMECES2 That students know how to apply their knowledge to their work or vocation in a professional manner and possess the skills that are usually demonstrated through the development and defense of arguments and the resolution of problems within their area of study.
C4	CMECES4 That students can transmit information, ideas, problems and solutions to both a specialised and non-specialised audience

Learning aims

	Competences
Understands the main statistical methods and applies them to solve mathematical problems in data science and artificial intelligence.	A18963 A18966 A18989	B5800 B5806	C2
Demonstrates the ability to design and develop a data analysis project and applies it correctly to a decision-making process and/or includes critical reasoning.	A18966 A18989	B5801 B5802 B5806 B5807
Applies the descriptive and inferential statistical concepts and procedures learnt to effectively communicate and represent the analysis and results, selecting the most appropriate data visualisation techniques and tools.	A18966 A18979	B5801 B5806	C2 C4
Applies the mathematical concepts and procedures learnt both in the elaboration of correct arguments and to face situations that imply the use of new mathematical knowledge and techniques, thus promoting autonomous learning.	A18966 A18979	B5807 B5808	C2
Communicates orally and/or in writing knowledge, reasoning and solutions to statistical problems using mathematical language.	A18979	B5800 B5808	C4

Contents

Topic	Sub-topic
Topic I: Descriptive statistics	Sub-topic 1: Introduction to statistics Sub-topic 2: Descriptive statistics
Topic II: Inference	Sub-topic 3: Introduction to inference Sub-topic 4: Bayesian inference: point and interval estimation, hypothesis testing and prediction Sub-topic 5: Frequentist inference: point and interval estimation, hypothesis testing and prediction Sub-topic 6: Non-parametric inference
Topic III: Study design	Sub-topic 7: Study design

Planning

Methodologies :: Tests
	Class hours	Hours outside the classroom	Total hours
Problem solving, classroom exercises	22	44	66

Practicals using information and communication technologies (ICTs) in computer rooms	6	9	15
Tutorship of group	4	6	10

Lecture	22	22	44

Extended-answer tests	6	9	15

(*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students.

Methodologies

Methodologies ::

	Description
Problem solving, classroom exercises	Problem posing and solving in the classroom.
Practicals using information and communication technologies (ICTs) in computer rooms	Solving exercises with a computer.
Tutorship of group	Monitoring of the students' learning process. Content review and problem-solving sessions will be held.
Lecture	Theoretical presentations in which the lecturer introduces the concepts, results and methods of the discipline.

Personalized attention

Tutorship of group

Description

Complementary activities and tutoring for continuous assessment.

In addition to group tutorials, students may request individual tutorials. For this, it is recommended to make an appointment with the teacher by email or in person.

Assessment

	Description	Qualification
Practicals using information and communication technologies (ICTs) in computer rooms	Practicals will be assessed by means of an examination.	10%
Extended-answer tests	Two partial written tests	40%, 40%
Others	Online assessment tests	10%

Other comments and second call
In order to pass the course in the first call (ordinary call), by continuous assessment, it will be necessary to achieve a minimum mark of 4 points (out of 10) in both mid-term exams, and the final mark (including online tests and practicals' marks) must be higher than 5 (out of 10). Students who do not pass the first exam will have to sit the second call (extraordinary exam), which will consist of a single written exam with content from both parts.

Sources of information

Access to Recommended Bibliography in the Catalog ULE

Basic	Bolstad, W.M., Introduction to Bayesian Statistics, John Wiley & Sons, 2004 Agresti, A.; Klingenberg, B.; Franklin, C. & Posner, M., Statistics : the art and science of learning from data, Pearson, 2018 Verzani, J., Using R for introductory statistics, Chapman and Hall, 2005
	https://catoute.unileon.es/permalink/34BUC_ULE/4ifthk/alma991008847377605772 https://catoute.unileon.es/permalink/34BUC_ULE/1ekdeev/alma991008792181705772 https://cran.r-project.org/doc/contrib/Verzani-SimpleR.pdf
Complementary	Hoff, P.D., A First Course in Bayesian Statistical Methods, Springer, 2010 Everitt, Brian S. &amp; Hothorn, Torsten , A., A Handbook of Statistical Analyses Using R, CRC Press, 2009
	https://catoute.unileon.es/permalink/34BUC_ULE/2dgtuo/cdi_askewsholts_vlebooks_9783030105310 https://catoute.unileon.es/permalink/34BUC_ULE/2dgtuo/cdi_askewsholts_vlebooks_9783030828080

Recommendations

Subjects that it is recommended to have taken before

PROBABILITY CALCULUS / 00717012