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Summary Business Intelligence for Data Science Lecture 5 | Master Data Science and Society $3.21
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Summary Business Intelligence for Data Science Lecture 5 | Master Data Science and Society
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  • Course
  • Business Intelligence for Data Science
  • Institution
  • Tilburg University (UVT)

Summary of Business Intelligence for Data Science, based on the lectures from the Master’s program in Data Science and Society at Tilburg University

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Document information

  • December 20, 2024
  • 4
  • 2024/2025
  • Summary

Subjects

  • business intelligence
  • data science
  • data science and society
  • sql
  • bi
  • tilburg university
  • summary
  • samenvatting

Written for

  • Tilburg University (UVT)
  • Data Science & Society
  • Business Intelligence for Data Science
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Business Intelligence for Data Science
OLAP Systems characteristics
On-Line Analytical Processing (OLAP)

 Data analytics
 Multidimensional data processing:
o Prediction models
o Regression analysis models
o Trend analysis and outliers
 OLAP transactions represent ad-hoc queries and the ability to encompass several thousand
operational data records using various aggregation functions on large datasets.
 OLAP transactions are performed on DW databases




Multidimensionality is a characteristic of every OLAP system, regardless of architecture and
technological solutions.

Desirable features:

 Creation of an unlimited number of dimensions and levels of data aggregation.
 Application of an unlimited number of analytical functions.
 The ability to handle empty or missing values in large data tables efficiently.
 Flexibility in designing different dimensional structures.

Approaches to dimensionality realization:

1) Using hypercube
2) Star schema of DW database

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