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Samenvatting Concepten Van Data & Analytics
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Samenvatting Concepten Van Data & Analytics
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Concepten van Data en AnalyticsInhoud
Inleiding ................................................................................................................................................... 3
What is data analytics?............................................................................................................................ 4
Definitie van data analytics ................................................................................................................. 4
Hoe werkt het? .................................................................................................................................... 8
Machine learning voorbeeld ............................................................................................................. 12
Machine learning essentials .............................................................................................................. 22
Samenvatting data analytics ............................................................................................................. 33
Why would you care? ............................................................................................................................ 34
Money money money ....................................................................................................................... 34
Hype or ride that wave ...................................................................................................................... 35
Disruptive technology ....................................................................................................................... 38
The data analytics process ................................................................................................................ 41
Samenvatting why would you care ................................................................................................... 48
Challenges & Pitfalls .............................................................................................................................. 49
Challenges ......................................................................................................................................... 49
Data science outside Krypton ........................................................................................................ 49
Big data .......................................................................................................................................... 52
Pitfalls ................................................................................................................................................ 65
Interpretation ................................................................................................................................ 65
Quality control ............................................................................................................................... 67
Samenvatting challenges en pitfalls .................................................................................................. 68
Exploratory Data Analysis ...................................................................................................................... 69
Intro Predictive Modelling ..................................................................................................................... 69
Neighbours and Clusters ....................................................................................................................... 69
More Mining .......................................................................................................................................... 69
General and specific data mining ...................................................................................................... 69
Process mining................................................................................................................................... 73
Network mining ................................................................................................................................. 76
Text mining ........................................................................................................................................ 79
Computer vison ................................................................................................................................. 79
Samenvatting more mining ............................................................................................................... 79
, 2
Data analytical thinking ......................................................................................................................... 80
Reinforcing evolution of strategic assets .......................................................................................... 80
Putting it all together signet bank ..................................................................................................... 82
Turn business problems into data problems ..................................................................................... 83
Samenvatting data analytical thinking .............................................................................................. 89
, 3
Inleiding
Examen
1. Written exam : Multiple choice questions 25
2. 2h30
3. Nederlandstalig examen
4. Wrap-up vragen kennen + kahoots
5. Verbetering met giscorrectie
6. Kind of questions
− Theoretical questions about the concepts (thoroughly understand the concepts)
“What is spurious correlation”
− Technical questions “Calculate precision and recall from a confusion matrix”
− Small case studies
• “You have this and this data”
• “This is what you want to do”
• “What kind of methods can you use?
Datamining (gegevensdelving, datadelving) is het gericht zoeken naar (statistische) verbanden tussen
verschillende gegevensverzamelingen met als doel profielen op te stellen voor wetenschappelijk,
journalistiek of commercieel gebruik.
, 4
What is data analytics?
Definitie van data analytics
“Describing Data Science is like trying to describe a sunset It should be easy, but somehow capturing
the words is impossible. The field guide to data science (Booz Allen Hamilton 2015)”
= We gaan (opzoek naar) patronen, linken en relaties binnen data en deze gaan we vergelijken en
valideren. We gaan dit doen om bepaalde zaken te begrijpen, hiervoor gaan we kijken naar een
(verplaatsings-)patroon.
Bv. Netflix dat gaat voorspellen welke series/films bij je passen. Technologie zoals je koelkast dat je
een melding geeft om melk te kopen wanneer jij onderweg bent van werk naar huis is iets wat
technisch mogelijk is, maar vaak neemt het tijd om de integratie ervan snel te zien. Dit komt door
meerdere redenen.
Met data analytics wil men (maatschappelijke) waarde creëren
1. We gaan ons afvragen waarom? Om zaken beter te begrijpen :
− Is er een relatie in de muziek die we graag horen?
− Is er een relatie / verband tussen roken en lonkanker?
− Hoe kunnen criminelen rondrijden?
− Is er een patroon in het feit dat chronische mensen hun therapie volgen?
− Welke studenten falen?
− Hoe gedragen voetgangers zich?
− Hoe kan een zelfrijdende auto het verschil zien tussen een vuilbak en een persoon?
2. We gaan bepaalde zaken proberen te voorspellen :
− Predict where someone wants to go (GPS support)
− Predict when someone needs a service
− Predict no-go zones for astma
Voorbeeld : je stapt in je auto en je rijd naar Gent, dan zal je GPS op je telefoon automatisch de weg
naar Gent opzetten. Omdat je regelmatig naar Gent gaat, zal je GSM dit automatisch voorspellen.
Daarnaast zal hij ook aangeven wanneer en waar er file is, terwijl je hierom je niet specifiek vraagt.
Besluit : je telefoon gaat een link leggen met Gent wanneer je in de auto zit, omdat je op regelmatige
basis naar Gent rijd = DATA ANALYTICS
= De integratie van bepaalde informatie. Het is belangrijk want wij willen waarden creëren!
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