Samenvatting
Samenvatting data driven management
- Instelling
- UC Leuven-Limburg
samenvatting data driven management (combinatie van Engels en Nederlands)
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Voorbeeld 4 van de 85 pagina's
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Voorbeeld van de inhoud
Data driven managementExamen: multiple choice (60%) zonder giscorrectie
Inhoudsopgave
1 Data Fundamentals ................................................................................................................................ 4
1.1 Data ....................................................................................................................................................... 4
1.1.1 Data in the 70’s & 80’s....................................................................................................................... 4
1.1.2 Big data producers: sensors .............................................................................................................. 4
1.1.3 Big data producers: IOT ..................................................................................................................... 5
1.1.4 Big data producers: the internet ....................................................................................................... 5
1.1.5 Big data producers: databases .......................................................................................................... 5
1.1.6 5 V’s of Big Data................................................................................................................................. 5
1.2 Use Cases ............................................................................................................................................... 7
1.3 Data Value Chain ................................................................................................................................. 10
1.3.1 Data value chain: from production to impact ................................................................................. 10
1.4 Data products ...................................................................................................................................... 13
1.4.1 Data product – definition ................................................................................................................ 13
1.4.2 Example 1: strawberry harvest prediction ...................................................................................... 14
1.4.3 Example 2: self-driving cars ............................................................................................................. 14
1.4.4 Example 3: smart thermostat .......................................................................................................... 14
1.4.5 Product format ................................................................................................................................ 15
1.4.6 Consumption archetype .................................................................................................................. 16
1.4.7 Data products specificities............................................................................................................... 17
1.5 Implementation ................................................................................................................................... 18
1.5.1 Frequency ........................................................................................................................................ 18
1.5.2 Pipelines .......................................................................................................................................... 19
1.5.3 Governance ..................................................................................................................................... 21
1.6 Recap lesson 1 ...................................................................................................................................... 22
2 Chapter II - Descriptive analysis ............................................................................................................ 23
2.1 Types of data........................................................................................................................................ 23
2.1.1 Example: hotel reviews data ........................................................................................................... 23
2.1.2 Terminology..................................................................................................................................... 24
2.1.3 Types of data ................................................................................................................................... 24
2.2 Descriptive analytics ............................................................................................................................ 26
.......................................................................................................................................................................... 26
2.2.1 Univariate analysis........................................................................................................................... 27
2.2.2 Bivariate........................................................................................................................................... 28
2.2.3 Multivariate analysis........................................................................................................................ 30
2.3 Before you start ................................................................................................................................... 30
1
, 2.3.1 Context ............................................................................................................................................ 30
2.3.2 Bias .................................................................................................................................................. 31
2.4 Recap lesson 2 ...................................................................................................................................... 33
3 Quadrant analysis ................................................................................................................................. 34
4 Data visualization ................................................................................................................................. 37
4.1 Goal of data visualization .................................................................................................................... 37
4.2 Dimensions, metrics & aggregation ..................................................................................................... 37
4.2.1 Definitions ....................................................................................................................................... 37
4.3 History .................................................................................................................................................. 40
4.4 Visual perception ................................................................................................................................. 41
4.4.1 Principles of visual perception......................................................................................................... 41
4.5 Common visualizations ........................................................................................................................ 43
4.6 Simple text ........................................................................................................................................... 44
4.7 Line graph ............................................................................................................................................ 44
4.8 Heatmap .............................................................................................................................................. 45
4.9 Waterfall .............................................................................................................................................. 45
5 Data Storytelling................................................................................................................................... 45
5.1 KPIs....................................................................................................................................................... 45
5.1.1 Advantages of KPIs .......................................................................................................................... 47
5.2 Dashboarding....................................................................................................................................... 48
5.2.1 Data value chain .............................................................................................................................. 48
5.3 The story of Ignaz Semmelweis ............................................................................................................ 51
5.4 Data storytelling .................................................................................................................................. 52
5.5 Best practices ....................................................................................................................................... 54
5.5.1 Structure the data story .................................................................................................................. 54
5.5.2 Provide context ............................................................................................................................... 55
5.5.3 Selecting the right data ................................................................................................................... 56
5.5.4 Use the right visuals to tell the data story ....................................................................................... 57
5.5.5 Use text............................................................................................................................................ 60
5.6 examples .............................................................................................................................................. 61
6 Data Quality ......................................................................................................................................... 64
6.1 Article: data quality (toledo) ................................................................................................................ 64
7 Gastles VRTNws: inleiding tot de datajournalistiek ............................................................................... 66
7.1 Wat is datajournalistiek? ..................................................................................................................... 66
7.2 Waarom datajournalistiek? ................................................................................................................. 66
7.3 Het doel van datajournalistiek ............................................................................................................. 67
2
, 7.4 Wat doet een datajournalist? .............................................................................................................. 67
8 Advanced Analytics .............................................................................................................................. 67
8.1 Four types of analytics ......................................................................................................................... 67
8.2 Artificial intelligence ............................................................................................................................ 71
8.3 Exercise: AI cases (toledo) .................................................................................................................... 74
9 Artificial intelligence ............................................................................................................................. 75
9.1 History .................................................................................................................................................. 75
9.2 Algorithms............................................................................................................................................ 77
9.3 AI generation 1 – Search ...................................................................................................................... 77
9.4 AI generation 2 – Machine learning ..................................................................................................... 79
9.5 AI generation 3 – Deep learning .......................................................................................................... 82
3
, 1 Data Fundamentals
1.1 Data
1.1.1 Data in the 70’s & 80’s
• Floppy disk
• 1.44 MB – 2.88 MB
• How big are they?
1.1.2 Big data producers: sensors
• Bijvoorbeeld aan een rood licht zitten sensoren die continu data genereren als er auto’s over
de sensoren rijden.
• Ook bij auto’s die rijden wordt er constant data getrackt
• In de haven van antwerpen staat een Inose, dit is een digitale neus die elke 2 seconden stoffen
gaan detecteren in de lucht, zwafel en Co2.
4
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