BME VIK - Üzleti intelligencia laboratórium

vissza a tantárgylistához nyomtatható verzió

Business Intelligence Laboratory

A tantárgy neve magyarul / Name of the subject in Hungarian: Üzleti intelligencia laboratórium

Last updated: 2019. március 6.

Budapest University of Technology and Economics
Faculty of Electrical Engineering and Informatics

MSc, Applied Informatics Main specialization

Course ID	Semester	Assessment	Credit	Tantárgyfélév
VIAUMB00	3	0/0/3/f	4

3. Course coordinator and department Dr. Ekler Péter,

Web page of the course https://www.aut.bme.hu/Course/ENVIAUMB00

4. Instructors

Name:	Status:	Department:
Dr. Ekler Péter	Associate Professor	Automation and Applied Informatics
Dr. Dudás Ákos	Associate Professor	Automation and Applied Informatics
Dr. Goldschmidt Balázs	Associate Professor	Control Engineering and Information Technology

5. Required knowledge Database management, web technolgies, object oriented programming

6. Pre-requisites

Kötelező:

NEM
(TárgyEredmény( "BMEVIAUMB09", "jegy" , _ ) >= 2
VAGY
TárgyEredmény("BMEVIAUMB09", "FELVETEL", AktualisFelev()) > 0)

A fenti forma a Neptun sajátja, ezen technikai okokból nem változtattunk.

A kötelező előtanulmányi rend az adott szak honlapján és képzési programjában található.

Ajánlott:

Mandatory: ENVIAUMA02 Business Intelligence

7. Objectives, learning outcomes and obtained knowledge Practice the materials of Business Intelligence Subject.

8. Synopsis

	Laboratories
1.	Relational and NoSQL databases.
2.	ETL processes, data extraction, cleansing and loading.
3.	Building data warehouses, extracting statistical measures.
4.	Business intelligence system built using Hadoop.
5.	Creating reports and complex dashboards, visualization of the data.
6.	Developing Business Intelligence solutions using BI suites and SDKs available on the market.
7.	Modeling business processes with BPEL, BPMN.
8.	Creating responsive reporting and dashboard UIs.
9.	Big Data systems, development using industry-standard BI suites.

9. Method of instruction Laboratory work

10. Assessment

- Attendance at the laboratories (including arrival on time),

- Completion of the laboratory exercises as instructed, which includes at home preparation for the laboratory. At the beginning of the class, the instructor may check verify the preparations with a written or oral entrance test. If the entrance test is not satisfactory, the laboratory cannot be completed.

- The laboratory exercises must be completed as instructed by the instructor. The completed exercises must be documented.

- The final grade is the average of the grades received for each laboratory.

13. References, textbooks and resources

- Alex Holmes: Hadoop in Practice, Second Edition, 2014.

- John Russel: Cloudera Impala, 2013.

- Phil Simon: Too Big to Ignore: The Business Case for Big Data, 2013.

- Stephen Few: Information Dashboard Design: Displaying Data for At-a-Glance Monitoring, 2013.

- Ralph Kimball, Margy Ross, Warren Thornthwaite, Joy Mundy, Bob Becker: The Kimball Group Reader: Relentlessly Practical Tools for Data Warehousing and Business Intelligence, 2010.

- Howard Dresner: The Performance Management Revolution: Business Results Through Insight and Action, 2007.

14. Required learning hours and assignment

Lectures	42
Prepare for lectures	38
Prepare for mid-term exam	0
Laboratory docs	40
Sum	120

15. Syllabus prepared by

Name:	Status:	Department:
Dr. Ekler Péter	Associate Professor	Automation and Applied Informatics
Dr. Dudás Ákos	Associate Professor	Automation and Applied Informatics
Dr. Goldschmidt Balázs	Associate Professor	Control Engineering and Information Technology

Budapest University of Technology and Economics, Faculty of Electrical Engineering and Informatics

Üzleti intelligencia laboratórium