Subject » BMEVIAUMB09
Business Intelligence Laboratory
Üzleti intelligencia labor
A tantárgyleírás hatályossága
Hatályosság kezdete:
2026. March 21.
Hatályosság vége:
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| Subject name (Hungarian, English) |
Üzleti intelligencia labor
Business Intelligence Laboratory
|
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Subject code | BMEVIAUMB09 | ||||||||||||
| Subject type | — | ||||||||||||
| Training Level | — | ||||||||||||
| Course types and hours (weekly/semester) |
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| Assessment type | félévközi érdemjegy | ||||||||||||
| Credits | 5 | ||||||||||||
| Subject coordinator |
DR. Ekler Péter
position: egyetemi docens
contact:
ekler.peter@vik.bme.hu
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| Responsible department |
Automatizálási és Alkalmazott Informatikai Tanszék
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| Faculty | Villamosmérnöki és Informatikai Kar | ||||||||||||
| Subject website | https://www.aut.bme.hu/Course/VIAUMB00 | ||||||||||||
| 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
The course will consist of 4 computer labs (4x45p) (one independent) at the beginning of the semester and one (independent) project assignment
- The topics of the labs are as follows:
- Using open source BI tools, data loading, reporting
- ELK based development
- MSSQL based business intelligence solution development, PowerBI in practice
- Data analysis, use of statistical and data mining tools
Attendance at labs will be monitored by the lab leader.
Consultation on the project assignment will be provided at least 1 time during the semester by prior arrangement, the lab leader can be contacted separately with specific questions.
The project assignment must be presented to the lab leader at the end of the semester at a fixed time.
Topic of the project assignment: development of your own BI solution: connecting data source(s), building ETL processes, displaying and calculating reports and decision KPIs
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 and home project
Tanulástámogató anyagok
Online források
Morgan Kaufmann, Business Intelligence Guidebook: From Data Integration to Analytics, 2014.; Joshua N. Milligan, Learning Tableau 10 - Second Edition: Business Intelligence and data visualization that brings your business into focus, 2016.; 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:
During term time:
- Attendance at timetabled classes,
- Successful completion of laboratory exercises. Students who are not sufficiently prepared will not be allowed to participate in the laboratory exercise and will have to make up for it. An additional condition is the successful completion of the laboratory and the acceptance of the report documenting this by the instructor in charge of the measurement. The document of the lab work must be completed by the end of the 2nd week of the lab.
- Achieve a minimum score of 40% by completing the project assignment.
- The mid-semester grade will consist of a 50-50% split of the lab protocol grade and the project assignment score.
Additional possibilities:
One laboratory work can be made up during the semester.
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: EN BMEVIAUMA24 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.