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Samenvatting Informatica Master Bedrijfskunde
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Samenvatting Informatica Master Bedrijfskunde
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Samenvatting informatica voorbedrijfsbeleid
1. H1: INLEIDING 5
1.1 BELANG IT 5
1.2 OPPORTUNITEITEN 6
1.3 RISICO’S 6
1.4 ENKELE FEITEN 6
1.5 HET BELANG VAN COORDINATIE 6
1.6 BUSINESS/ IT ALIGNMENT 7
1.7 ONDERSCHEID TAAK BUSINESS EN IT 7
1.8 BUSINESS – IT ALIGNMENT 7
2. H2: BASISBEGRIPPEN 8
2.1 A. BEGRIPPEN: INFORMATICA 9
2.2 HET SCHRIJVEN EEN PROGRAMMA 9
2.2.1 ONTWERPEN VAN DE OPLOSSING 10
3. H3: ALGORITMES 10
3.1 BEGRIP ALGORITME 10
3.2 ALGORITME REPRESENTATIES 10
3.3 BEGRIP: PROGRAMMEERTAAL 11
3.4 ALGORITME REPRESENTATIES IN DIT VAK 11
3.5 PSEUDOCODES & FLOWCHARTS 12
3.6 PRIMITIEVEN FC / PC 12
3.7 FLOWCHART – PRIMITIEVEN 13
3.8 ALGORITME REPRESENTATIES: FLOWCHARTS 13
3.8.1 SEQUENTIE 13
3.8.2 SELECTIE (VOORWAARDELIJKE OPDRACHT) 13
3.8.2.1 If then/als dan 13
3.8.2.2 If then else/ als dan anders 14
3.8.3 ITERATIE – HERHALINGSOPDRACHT OF LOOP 14
3.8.3.1 While/ zolang 14
3.8.3.2 Repeat/ herhaal 15
3.8.3.3 For/ voor 15
3.9 NESTED IF’S 16
4. H4: FUNCTIONALITIES 16
4.1 MODEL 16
4.1.1 WAAROM BOUWEN WE MODELLEN? 17
4.1.2 BPMN 17
4.1.3 ELEMENTEN 19
1
,4.1.3.1 Flow objects 19
4.1.3.2 Connectors 19
4.1.3.3 Pool 19
4.1.4 SUB PROCESS 21
4.1.5 MESSAGE FLOW 21
4.1.6 MESSAGE EVENT 21
4.1.7 GATEWAY 21
4.2 PROCESS MODELING 24
4.2.1 INLEIDING 24
4.2.2 WAT IS EEN PROCES? 25
4.2.3 PROCESS MODELING 25
4.2.3.1 Process mining 25
4.2.4 BPMN RECAP 26
4.2.4.1 Events 27
4.2.4.1.1 Time events 27
4.2.4.2 Artifcats: data 28
4.2.4.3 Artifacts: annotation 28
5. H5: PROJECT MANAGEMENT 29
6. H6: DATABASES 29
6.1 KLASSEN 29
6.1.1 DATA MODELING 29
6.1.2 KLASSENDIAGRAM 29
6.1.3 KLASSE 29
6.1.4 OBJECT 30
6.1.5 ASSOCIATIE 30
6.1.6 MULTIPLICITEIT 30
6.1.7 ATTRIBUTEN 31
6.1.8 ASSOCIATIEKLASSE 32
6.1.9 MEERVOUDIGE ASSOCIATIES 32
6.2 DEEL 2: DATABASES 33
6.2.1 DATABASE: INLEIDING 33
6.2.2 VAN KLASSE NAAR TABEL 33
6.2.2.1 First normal form (1NF) 34
6.2.2.2 keys 34
6.2.2.2.1 Primary key 34
6.2.2 ASSOCIATIES MAPPEN MET MULTIPLICITEIT 1 35
6.2.3 ASSOCIATIES MAPPEN 35
6.2.3.1 Instances 36
OPLOSSING: 37
7. H7: MONTE CARLO SIMULATIE 37
7.1 GESCHIEDENIS VAN MONTE CARLO 37
7.2 INTUITIEF VOORBEELD 38
7.3 MONTE CARLO SIMULATIE 39
7.3.1 STAPPEN 39
7.3.1.1 Stappenplan gebruik MC 39
2
,7.3.2 MCS: ASSUMPTIES 40
7.3.3 PREREQUISITE: RNG 41
7.3.4 VERDELINGEN: OP INTUITIEF NIVEAU 41
7.3.4.1 sampling op basis van RNG en de CDF 41
7.3.4.2 verdelingen: uniform 42
7.3.4.3 Verdelingen: driehoeks 42
7.3.4.4 Verdelingen: normale 42
7.3.5 VERDELING KIEZEN 42
7.3.6 STEEKPROEFGROOTTE KIEZEN 43
7.4 ADDENDUM SWOT ANALYSE 43
7.4.1 STERKTES 43
7.4.2 ZWAKTES 44
7.4.3 KANSEN 44
9. H9: DATA SCIENCE 45
9.1 RELEVANTIE 45
9.2 FUNDAMENTELE CONCEPTEN 45
9.2.1 TERMINOLOGIE 45
9.2.2 DATA 46
9.2.2.1 Data als strategisch goed 46
9.2.2.3 Welke types beslissingen kunnen we ondersteunen met data science? 46
9.2.2.4 Een model 47
9.2.2.4.1 Een model leren 47
9.2.2.4.2 Trainen: gewichten vinden op basis van trainingdata 47
9.2.2.4.3 complexe functies approximeren met neurale netwerken 48
9.2.2.4.4 Learning 49
9.3 TAKEN, METHODES EN TOEPASSINGEN 49
9.3.1 DATA SCIENCE: TAKEN, METHODES EN TOEPASSINGEN 49
9.3.1.1 Variabelen 50
9.3.3.2 Supervised learning 51
9.3.3.3 Regression (‘Regressie’) 51
9.3.3.4 Classification (‘Classificatie’) 51
9.3.4 METHODES 52
9.3.4.1 Wat is een goed model? 52
9.3.5 TRAINING VS DEPLOYMENT 53
9.4 VAN THEORIE NAAR PRAKTIJK MET CRISP-DM 54
9.4.1 CHURN PREDICITION 54
9.4.2 CONCLUSIE 57
9.4.3 MEER ZAKEN OM IN REKENING TE BRENGEN 57
9.4.5 EXTRA: A NOTE ON DEEP LEARNING (NIET KENNEN) 58
8. H8: INFORMATICA IN EEN BUSINESS CONTEXT 58
8.1 MANAGEMENTNIVEAUS 58
8.2 TPS (= TRANSACTION PROCESSING SYSTEMS) 59
8.3 SYSTEMS FOR BUSINESS INTELLIGENCE (BI) 61
8.4 HOE INFORMATIETECHNOLOGIE BEDRIJFSPROCESSEN ONDERSTEUNT 63
8.4.1 ENTREPRISE APPLICATIONS 63
8.4.1.1 Entreprise application architecture 64
3
, 8.4.1.2 Enterprise resource planning systems 64
8.4.1.3 Supply Chain Management Systems 64
8.4.1.4 Customer relationship Management systems 65
8.4.1.5 Uitdagingen rond Enterprise applications 65
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