Data Processing

Garant předmětu:

Lecturer:

Tutor:

Supervisor:

Department of Applied Informatics in Transportation

Synopsis:

Introduction to data processing and analysis tools. Practical part of the training - introduction to the working environment, applied examples of data processing from practice, advanced methods of presentation of the results. Seminar papers on open data. Consultation hours for seminar papers. Seminar paper submission and presentation.

Requirements:

Ability to think logically, knowledge of the basics of algorithmization and the fundamentals of any programming language at a level equivalent to a third year of study at a technical university.

Syllabus of lectures:

The lessons are divided into 4 blocks:

Block 1: introduction to R - environment, concept, basics, simple examples, basic libraries, examples and their use (installation of R by students)

Block 2: applied R - applied examples from practice, map library, data retrieval from different sources and their modification (GIS, RDBMS, CSV, etc.)

Block 3: advanced R - interactive presentation module (shiny), other modules by agreement

Block 4: possibilities of Bayesian networks in data analysis

Syllabus of tutorials:

The lessons are divided into 4 blocks:

Block 1: introduction to R - environment, concept, basics, simple examples, basic libraries, examples and their use (installation of R by students)

Block 2: applied R - applied examples from practice, map library, data retrieval from different sources and their modification (GIS, RDBMS, CSV, etc.)

Block 3: advanced R - interactive presentation module (shiny), other modules by agreement

Block 4: possibilities of Bayesian networks in data analysis

Study Objective:

The aim of the course is primarily to familiarize students with the tools for data processing and analysis, to test the most common options used in data processing, including advanced options for presenting the results of analyses. Students will then independently perform data analysis on data from existing open systems.

Study materials:

Presentation of the course created by the guarantor.

Holubová Irena, Kosek Jiří, Minačík Karel and Novák David. BigData and NoSQL databases. Prague: Grada Publishing, 2015, 288s, ISBN 978-80-247-5466-6.

Benjamin S. Horton, Nicholas J. Kaplan, Daniel T. Baumer. Modern Data Science with R. Raylor & Francis, 2017, 556s, ISBN 9781498724487

Internet

Note:

Further information:

No time-table has been prepared for this course

The course is a part of the following study plans: