Educational guide
IDENTIFYING DATA							2024_25
Subject	PROGRAMMING IN DISTRIBUTED DATA ENVIRONMENTS					Code	01751008
Study programme	1751 - M.U.ROBOTICA E INTELIGENCIA ARTIFICIAL
Descriptors	Credit.	Type	Year	Period
	4.5	Compulsory	First	Second
Language	Castellano
Prerequisites
Department	ING.MECANICA,INFORMAT.AEROESP.
Coordinador	GUERRERO HIGUERAS , ANGEL MANUEL				E-mail	agueh@unileon.es mcasl@unileon.es
Lecturers	CASTEJÓN LIMAS , MANUEL GUERRERO HIGUERAS , ANGEL MANUEL
Web	http://agora.unileon.es
General description
Tribunales de Revisión	Tribunal titular Cargo Departamento Profesor Presidente ING.MECANICA,INFORMAT.AEROESP. MATELLAN OLIVERA , VICENTE Secretario ING.MECANICA,INFORMAT.AEROESP. PANIZO ALONSO , LUIS Vocal CONDE GONZALEZ , MIGUEL ANGEL Tribunal suplente Cargo Departamento Profesor Presidente MATEMATICAS GARCIA SIERRA , JUAN FELIPE Secretario ING.MECANICA,INFORMAT.AEROESP. RODRIGUEZ LERA , FRANCISCO JAVIER Vocal ING.MECANICA,INFORMAT.AEROESP. FERNANDEZ LLAMAS , CAMINO

Topic	Sub-topic
Part I. Introduction.	1. Introduction to programming in distributed data environments.
Part II. Calculation engines and analysis techniques	1. Calculation engines. 2. Distributed data analysis techniques.

Methodologies  ::  Tests
	Class hours	Hours outside the classroom	Total hours
Practicals using information and communication technologies (ICTs) in computer rooms	30	51	81
Assignments	3	1.5	4.5
Lecture	10	15	25
Objective short-answer tests	2	0	2
(*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students.

Methodologies   ::  

	Description
Practicals using information and communication technologies (ICTs) in computer rooms	Programs will be developed to search, filter and analyze large collections of data using different calculation engines, interpreting the results obtained.
Assignments	Different works will be carried out applying the concepts seen in theory and practices to real cases that allow the understanding and mastery of fundamentals and advanced techniques for the search, filtering and abstraction of information in large data collections applying different frameworks.
Lecture	The fundamentals and advanced techniques for searching, filtering and abstraction of information in large data collections will be presented, explaining how to interpret the evaluation results obtained from predictive models or advanced algorithms based on artificial intelligence.

Lecture Practicals using information and communication technologies (ICTs) in computer rooms Assignments

Description To solve the doubts that arise when solving problems solved in class or proposed (both theory and practical), it is recommended to use the doubts forum or request a tutorial with the professor.

	Description	Qualification
Lecture	A test will be given to evaluate the concepts covered in the theory sessions.	20%
Practicals using information and communication technologies (ICTs) in computer rooms	Different practical exercises will be delivered where the concepts seen in the practical sessions will be developed.	60%
Assignments	Different works will be carried out applying the concepts seen in the resolution of real problems.	20%
Other comments and second call

Basic	Manuel Castejón, Apuntes de la asignatura, , 2022 Srinivasa & Muppalla, Guide to High Performance Distributed Computing, Springer, 2014 Jeff Smith, Machine learning systems. Designs that scale, Manning, 2018 Odersky, Spoon, Venners & Sommers, Programming in Scala 5th Edition, Artima, 2021
Complementary