310-2221/01 – Industrial Data Analysis (ApD)

Gurantor department	Department of Mathematics and Descriptive Geometry	Credits	3
Subject guarantor	Ing. Petra Schreiberová, Ph.D.	Subject version guarantor	Ing. Petra Schreiberová, Ph.D.
Study level	undergraduate or graduate	Requirement	Compulsory
Year	3	Semester	winter
		Study language	Czech
Year of introduction	2021/2022	Year of cancellation
Intended for the faculties	FMT	Intended for study types	Bachelor

Instruction secured by
Login	Name	Tuitor	Teacher giving lectures
SKN002	Ing. Petra Schreiberová, Ph.D.

Extent of instruction for forms of study
Form of study	Way of compl.	Extent
Full-time	Graded credit	0+3

Subject aims expressed by acquired skills and competences

- Understand the issues of data analysis. - Synthesise and organise information in data. - Interpret, understand statistical results. - Know some standard methods for professional analyses. - Understand the limitations of these approaches, consider alternatives, extensions, etc..

Teaching methods

Tutorials

Summary

Předmět studenty seznámí se základy statistické analýzy v rozsahu potřebném pro zpracování měření či datových souborů a časových řad. Absolvent tohoto předmětu by měl být schopen: - formulovat otázky, které je možné zodpovědět pomocí dat, a k tomu účelu si osvojit principy sběru, zpracování dat a prezentace relevantních údajů - naučit se prakticky analyzovat časové řady s využitím běžně používaných přístupů a vybrat vhodnou metodu pro efektivní analýzu - navrhovat a vyhodnocovat závěry (inference) a predikce pomocí dat

Compulsory literature:

Martinez, W. L.: Exploratory data analysis with MATLAB. Boca Raton, Fla.: Chapman&Hall/CRC, c2005. ISBN 1-58488-366-9. Škňouřilová, P., Briš, R.: Statistics I, VŠB – TUO, Ostrava 2007.

Recommended literature:

PÄRT-ENANDER, E.: The Matlab handbook. Harlow:Addison-Wesley, 1997. ISBN 0-201-87757-0.

Way of continuous check of knowledge in the course of semester

Zápočet - odevzdání 2 programů

E-learning

Other requirements

There are no more requirements.

Prerequisities

Subject has no prerequisities.

Co-requisities

Subject has no co-requisities.

Subject syllabus:

- Matlab - Statistical methods, descriptive statistics - Point estimators, interval estimators - Statistical hypothesis testing - Correlation and regression analysis - Time Series Analysis

Conditions for subject completion

Full-time form (validity from: 2022/2023 Winter semester)

Task name	Type of task	Max. number of points (act. for subtasks)	Min. number of points	Max. počet pokusů
Graded credit	Graded credit	100	51	3

Valid from	Valid until	Mandatory attendence participation
Jul 11, 2022 12:31:15 PM	Feb 14, 2023 2:22:39 PM	80 %

Mandatory attendence participation: - participation is mandatory for every program presentation

Show history

Conditions for subject completion and attendance at the exercises within ISP: In order to complete the credit, students create a program.

Show history

Valid from	Valid until	Conditions for subject completion and attendance at the exercises within ISP
Feb 10, 2023 2:25:36 PM	Feb 14, 2023 2:22:39 PM	In order to complete the credit, students pass the credit test.

Occurrence in study plans

Academic year	Programme	Form	Study language	Tut. centre	Year	Type of duty
2024/2025	(B0712P130001) Environmental technology	P	Czech	Ostrava	3	Compulsory	study plan
2023/2024	(B0712P130001) Environmental technology	P	Czech	Ostrava	3	Compulsory	study plan
2022/2023	(B0712P130001) Environmental technology	P	Czech	Ostrava	3	Compulsory	study plan

Occurrence in special blocks

Block name	Academic year	Form of study	Study language	Year	W	S	Type of block	Block owner

Assessment of instruction

Předmět neobsahuje žádné hodnocení.