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
Lectures42
Prepare for lectures38
Prepare for mid-term exam0
Laboratory docs40
Sum120
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