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Samenvatting Data-Analyse Business Intelligence HBO 2026 - SQL Statistiek Power BI ETL KPI Dashboard Machine Learning CRISP-DM

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COMPLETE SAMENVATTING DATA-ANALYSE & BUSINESS INTELLIGENCE HBO 2026 Alle 12 hoofdstukken: • 4 soorten analyse (descriptief/diagnostisch/predictief/prescriptief) • Big Data (5 V's: Volume, Velocity, Variety, Veracity, Value) • Meetniveaus (nominaal/ordinaal/interval/ratio) + data-kwaliteit • Beschrijvende statistiek (gemiddelde, mediaan, standaarddeviatie, normaalverdeling) • Datavisualisatie (grafiekkeuze, best practices, data-ink ratio Tufte) • SQL basics: SELECT, FROM, WHERE, JOIN (INNER/LEFT/RIGHT/FULL), GROUP BY, COUNT/SUM/AVG • Databaseontwerp (ERD, primary/foreign key, normalisatie 1NF-3NF) • Excel essentials (VLOOKUP, INDEX+MATCH, draaitabellen, voorwaardelijke opmaak) • BI-stack (data sources → ETL → warehouse → BI tool → dashboard) • Data warehousing (OLTP vs. OLAP, star schema Kimball, data lake) • Dashboards en KPI's (SMART, leading vs. lagging, best practices) • Machine Learning basics (supervised/unsupervised, regressie, decision tree, K-Means, evaluatiemetrics) • Data-ethiek (AVG, bias, transparantie, anonimisering) • CRISP-DM (6 fasen) HBO: Business IT & Management, Bedrijfskunde, Finance, Marketing Analytics, Informatica

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Samenvatting Data-Analyse en Business Intelli-
gence
Dataverzameling, Visualisatie, SQL, BI-tools en Machine
Learning Basics
Complete Samenvatting voor HBO Bedrijfskunde & Informatica


Versie: 2026
Geschikt voor: HBO Bedrijfskunde, Business IT & Management, Informatica,
Finance, Marketing Analytics
Inhoud: Alle kernthema’s data-analyse + SQL basics + BI-tools + begrippen-
lijst


� Heeft deze samenvatting je geholpen bij je tentamen? Laat dan een
review achter op Stuvia!



Inhoudsopgave
1. Inleiding Data-Analyse
2. Data-typen en Data-kwaliteit
3. Beschrijvende Statistiek
4. Datavisualisatie
5. SQL en Databases
6. Excel voor Data-Analyse
7. Business Intelligence (BI)
8. Data Warehousing en ETL
9. Dashboards en KPI’s
10. Machine Learning Basics
11. Data-ethiek en Privacy
12. Data-Driven Decision Making



Hoofdstuk 1: Inleiding
1.1 Wat is Data-Analyse?
Data-analyse = het proces van inspecteren, opschonen, transformeren en mod-
elleren van data om bruikbare informatie, conclusies en beslissingsondersteuning
te genereren.




1

, 1.2 Data → Informatie → Kennis → Wijsheid
Data (ruwe feiten) → Informatie (gestructureerd, context) → Kennis (inzicht) → Wijsheid (bes

1.3 Soorten Analyse

Type Vraag Voorbeeld
Descriptief Wat is er gebeurd? Maandelijkse omzetrapportage
Diagnostisch Waarom is het Oorzaakanalyse klantverlies
gebeurd?
Predictief Wat gaat er Voorspelling vraagpatroon
gebeuren?
Prescriptief Wat moeten we Optimale prijsstrategie
doen?


1.4 Big Data (5 V’s)

V Beschrijving
Volume Grote hoeveelheid data
Velocity Snelheid van data-instroom
Variety Verschillende datatypen
Veracity Betrouwbaarheid/kwaliteit
Value Waarde die data oplevert




Hoofdstuk 2: Data-typen en Kwaliteit
2.1 Meetniveaus

Niveau Kenmerk Voorbeeld Bewerking
Nominaal Categorieën, Geslacht, kleur Modus
geen rangorde
Ordinaal Rangorde, Opleidingsniveau, Mediaan
afstanden tevredenheid (1-5)
onbekend
Interval Gelijke Temperatuur (°C), Gemiddelde
afstanden, geen IQ
absoluut
nulpunt




2

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Uploaded on
April 23, 2026
Number of pages
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Written in
2025/2026
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