Business Intelligence Laboratory

Üzleti intelligencia laboratórium

A tantárgyleírás hatályossága

Hatályosság kezdete:

2026. March 21.

Hatályosság vége:

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Subject name (Hungarian, English)

Üzleti intelligencia laboratórium

Business Intelligence Laboratory

Subject code

BMEVIAUMB00

Subject type

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Training Level

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Course types and hours (weekly/semester)

Course type	lecture	tutorial	laboratory
hours (weekly)	0	0	3
type (linked/independent)	—	—	autonomous course

Assessment type

félévközi érdemjegy

Credits

Subject coordinator

DR. Ekler Péter

position: egyetemi docens

contact: ekler.peter@vik.bme.hu

Responsible department

Automatizálási és Alkalmazott Informatikai Tanszék

https://www.aut.bme.hu

Faculty

Villamosmérnöki és Informatikai Kar

Subject website

https://www.aut.bme.hu/Course/ENVIAUMB00

Primary curriculum type

—

Direct prerequisites – Strong prerequisite

none

Direct prerequisites – Weak prerequisite

none

Direct prerequisites – Parallel prerequisite

none

Direct prerequisites – Milestone prerequisite

none

Direct prerequisites – Exclusion

none

Objectives

Programme

	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.

Practice the materials of Business Intelligence Subject.

Learning outcomes

Ez a tantárgy a KKK rendeletben meghatározott, következő kompetenciák fejlesztését szolgálja:

Knowledge

No learning outcomes recorded.

Skills

No learning outcomes recorded.

Attitudes

No learning outcomes recorded.

Autonomy and responsibility

No learning outcomes recorded.

Oktatási módszertan

Laboratory work

Tanulástámogató anyagok

Online források

- 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.

Recommended preliminary knowledge for completing the subject

Knowledge type competencies

(azon előzetes ismeretek összessége, amelyek megléte nem kötelező, de a tantárgy eredményes teljesítését nagyban elősegíti)

Database management, web technolgies, object oriented programming

Skill type competencies

(azon előzetes képességek és készségek összessége, amelyek megléte nem kötelező, de a tantárgy eredményes teljesítését nagyban elősegíti)

nincs

Recommended (non-compulsory) preliminary competencies

(azon ajánlott (nem kötelező) előzetesen megszerzendő kompetenciák összessége, amelyek jelentősen hozzájárulnak a tantárgy eredményes teljesítéséhez)

Database management, web technolgies, object oriented programming

General rules

Requirements: - 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.

Assessment methods

In-term assessments

No detailed assessments provided.

Weight of in-term assessments

No weights provided.

Exam-period assessments

No detailed assessments provided.

Weight of exam elements

No weights provided.

Grade calculation

No grade thresholds provided.

Attendance requirements

No attendance requirements provided.

Rules for retake and resubmission

Not provided.

Short description

Not provided.

Detailed description

Not provided.

Recommended courses

Mandatory: ENVIAUMA02 Business Intelligence

Workload to complete the subject

No workload breakdown provided.

Validity of subject requirements

Requirements valid from:

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Requirements valid until:

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Curriculum placement

No curriculum placements recorded for this subject version.