Summary
Samenvatting data driven management
- Course
- Institution
samenvatting data driven management (combinatie van Engels en Nederlands)
[Show more]
Seller
Follow
janehillewaere
Content preview
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
The benefits of buying summaries with Stuvia:
Guaranteed quality through customer reviews
Stuvia customers have reviewed more than 700,000 summaries. This how you know that you are buying the best documents.
Quick and easy check-out
You can quickly pay through credit card or Stuvia-credit for the summaries. There is no membership needed.
Focus on what matters
Your fellow students write the study notes themselves, which is why the documents are always reliable and up-to-date. This ensures you quickly get to the core!
Frequently asked questions
What do I get when I buy this document?
You get a PDF, available immediately after your purchase. The purchased document is accessible anytime, anywhere and indefinitely through your profile.
Satisfaction guarantee: how does it work?
Our satisfaction guarantee ensures that you always find a study document that suits you well. You fill out a form, and our customer service team takes care of the rest.
Who am I buying these notes from?
Stuvia is a marketplace, so you are not buying this document from us, but from seller janehillewaere. Stuvia facilitates payment to the seller.
Will I be stuck with a subscription?
No, you only buy these notes for $8.04. You're not tied to anything after your purchase.
Can Stuvia be trusted?
4.6 stars on Google & Trustpilot (+1000 reviews)
52510 documents were sold in the last 30 days
Founded in 2010, the go-to place to buy study notes for 14 years now