Academic Structure:

The Specialization in Data Intelligence oriented to Big Data is a structured course. The course is structured in ten (10) subjects and an Integrative Final Work. Nine (9) of the subjects are compulsory and one (1) is optional, it means, the student can choose one from a list of five (5) where some other elective subjects may be added to the list if they were previously approved by the Honorable Academic Council.
The subject assessment is defined by the professor in charge although it always exists a written proof of this (exam, monograph, work).

Curriculum:
– Duration:

The stipulated time to obtain the Specialization Degree in Data Intelligence oriented to Big Data is between one (1) year and 4 (four) years since enrollment.

– Number of subjects:

10 plus an Integrative Final Work

BASIC AREA
The Basic Area contents can be included in the subjects of any course of studies. During the enrollment process, the academic background of applicants will be studied in order to give the corresponding equivalencies. In those cases where the course of studies training was not enough to pass these subjects, the applicant should study the suggested material and sit for an exam.

Icono_PDF_16_x_16 Programming

Dr. Waldo Hasperué, Dra. Laura De Giusti

Icono_PDF_16_x_16 Statistics

Dra. Laura Lanzarini

Icono_PDF_16_x_16 Data base

Mg. Pablo Thomas, Mg. Rodolfo Bertone

FUNDAMENTALS

Icono_PDF_16_x_16 Data capture and storage

Mg. Oscar Bría, Mg. Javier Bazzocco

Icono_PDF_16_x_16 Data mining

Dra. Laura Lanzarini

Icono_PDF_16_x_16 Automatic learning

Dr. Guillermo Leguizamón, Dr. Franco Ronchetti

Icono_PDF_16_x_16 Big Data Visualization

Dra. Silvia Castro

Icono_PDF_16_x_16 Intelligent Data Analysis in Big Data environments

Dr. José Ángel Olivas Varela

Icono_PDF_16_x_16 Big Data Concepts and Applications

Dr. Waldo Hasperué

ELECTIVE AREA
(only one subject)

Icono_PDF_16_x_16 Text mining

Dr. Marcelo Errecalde

Icono_PDF_16_x_16 Applications of Data Intelligence

Dr. Aurelio Fernández Bariviera

Icono_PDF_16_x_16 Time Series

Dr. Aurelio Fernández Bariviera

Icono_PDF_16_x_16 Statistical learning

Dra. Laura Lanzarini

Icono_PDF_16_x_16 Parallel processing applied to Big Data

Ing. Armando De Giusti, Dr. Enzo Rucci

Integrative Final Work

It should be individual, show the student’s overall learning in the Specialty and clearly state the bibliographical research task carried out and the resulting creative contributions to the chosen topic.

Icono_PDF_16_x_16 APPENDIX V: Layout for Final Work Proposals and APPENDIX IV: Final Work Layout