The possibilities of information that can be extracted from seemingly simpel data

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OMM Solutions GmbH

Olaf discusses data science and shows some examples, where you can apply it,

Tecnologia

OMM Solutions
TECHtalks #18
1< OMM Solutions GmbH >
www.tech-talks.eu

Einmal im Monat ist TECHtalk Zeit! First come first served!
< OMM Solutions GmbH > 2

Talk: The possibilities of information that can be
extracted from seemingly simple data
Speaker: Olaf Horstmann
3< OMM Solutions GmbH >

• Data Science != Big Data != Machine Learning != AI
• Data Science = Extracting information from any data
• Big Data = TeraBytes to PetaBytes of data
• Machine Learning = Training algorithms to classify or predict specific data
• AI = Training an algorithm to make correct decisions on a wide range of different situations
• Data does not deliver “truth”
• Every piece of extracted information is just an assumption/thesis
4
...things to remember
Prequisit
< OMM Solutions GmbH >

Data from a fitness-tracker
5
Images by Strava; https://www.strava.com/heatmap#7.00/-120.90000/38.36000/hot/all
< OMM Solutions GmbH >

Runners
Data from a fitness-tracker
6< OMM Solutions GmbH >
Images by Strava; https://www.strava.com/heatmap#7.00/-120.90000/38.36000/hot/all

Cyclists
Data from a fitness-tracker
7< OMM Solutions GmbH >
Images by Strava; https://www.strava.com/heatmap#7.00/-120.90000/38.36000/hot/all

Sometimes they reveal streets that are not on the map ...
Data from a fitness-tracker
8< OMM Solutions GmbH >
Images by Strava; https://www.strava.com/heatmap#7.00/-120.90000/38.36000/hot/all

… somewhere in Afghanistan
Data from a fitness-tracker
9< OMM Solutions GmbH >
Images by Strava; https://www.strava.com/heatmap#7.00/-120.90000/38.36000/hot/all

Example: “Spiegel Mining” by David Kriesel
• 70.000 Spiegel Online articles
• time-frame ~2 years
• everything saved to a database
• analysis based on feature-extraction
Data Science on data from a news-website
10< OMM Solutions GmbH >
Quelle: http://www.dkriesel.com/spiegelmining

Step 1: Visualisation
Data Science on data from a news-website
11
Graphs by David Kriesel dkriesel.com
< OMM Solutions GmbH >

Step 2: “Trivial” derivations
Data Science on data from a news-website
12
Graphs by David Kriesel dkriesel.com
< OMM Solutions GmbH >

Step 2: “Trivial” derivations
Data Science on data from a news-website
13
Graphs by David Kriesel dkriesel.com
< OMM Solutions GmbH >

Step 3: More in depth analysis
Data Science on data from a news-website
14
Graphs by David Kriesel dkriesel.com
< OMM Solutions GmbH >

Step 4: Somewhat unrelated interpretations
Data Science on data from a news-website
15
Graphs by David Kriesel dkriesel.com
< OMM Solutions GmbH >

Step 5: Further unrelated interpretations
Data Science on data from a news-website
16
Graphs by David Kriesel dkriesel.com
< OMM Solutions GmbH >

Step 6: Combination of multiple datasources
Data Science on data from a news-website
17
Graphs by David Kriesel dkriesel.com; instagram.com; twitter.com
+
< OMM Solutions GmbH >

Data does not deliver “truth”
18
Remember
< OMM Solutions GmbH >

• Statistics have shown, that employees that used Chrome or Firefox performed better on
employment assessment metrics and stayed on longer than those who did not.
• => “Users of the Chrome and Firefox browsers are better employees.”
• The ones with “relatively feminine” names killed an average of 42 people, the ones with
“relatively male” name killed only 15 on average.
• => “Female-named hurricanes are more deadly.”
• American men 45 to 82 who skip breakfast showed a 27 percent higher risk of coronary heart
disease over a 16-year period.
• => “Skipping breakfast causes coronary heart disease”
...your turn to guess
Correlation != Causation
19
https://blogs.scientificamerican.com/guest-blog/9-bizarre-and-surprising-insights-from-data-science/

Correlation != Causation
20
Graphs by Tyler Vigen (http://www.tylervigen.com/spurious-correlations)

Correlation != Causation
21
Graphs by Tyler Vigen (http://www.tylervigen.com/spurious-correlations)

Correlation != Causation
22
Graphs by Tyler Vigen (http://www.tylervigen.com/spurious-correlations)

Vielen Dank für Eure Aufmerksamkeit!
23< OMM Solutions GmbH >

Ihr persönlicher Ansprechpartner
Fragen oder Interesse?
< OMM Solutions GmbH > 24
Olaf Horstmann
Technology & Innovation
OMM Solutions GmbH
Vor dem Lauch 4
70567 Stuttgart
Germany
oh@omm-solutions.de
+49 (0)711 75 86 46 04

25< OMM Solutions GmbH >
www.omm-solutions.de
OMM Solutions GmbH
Vor dem Lauch 4
70567 Stuttgart
Geschäftsführer
Martin Allmendinger
Malte Horstmann
Olaf Horstmann
Kontakt
Telefon: +49 711 6747 051-0
E-Mail: info@omm-solutions.de
Umsatzsteuer-ID: DE295716572
Sitz der Gesellschaft: Stuttgart
Amtsgericht Stuttgart, HRB 749562
Impressum

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The possibilities of information that can be extracted from seemingly simpel data

1. OMM Solutions TECHtalks #18 1< OMM Solutions GmbH > www.tech-talks.eu

2. Einmal im Monat ist TECHtalk Zeit! First come first served! < OMM Solutions GmbH > 2

3. Talk: The possibilities of information that can be extracted from seemingly simple data Speaker: Olaf Horstmann 3< OMM Solutions GmbH >

4. • Data Science != Big Data != Machine Learning != AI • Data Science = Extracting information from any data • Big Data = TeraBytes to PetaBytes of data • Machine Learning = Training algorithms to classify or predict specific data • AI = Training an algorithm to make correct decisions on a wide range of different situations • Data does not deliver “truth” • Every piece of extracted information is just an assumption/thesis 4 ...things to remember Prequisit < OMM Solutions GmbH >

5. Data from a fitness-tracker 5 Images by Strava; https://www.strava.com/heatmap#7.00/-120.90000/38.36000/hot/all < OMM Solutions GmbH >

6. Runners Data from a fitness-tracker 6< OMM Solutions GmbH > Images by Strava; https://www.strava.com/heatmap#7.00/-120.90000/38.36000/hot/all

7. Cyclists Data from a fitness-tracker 7< OMM Solutions GmbH > Images by Strava; https://www.strava.com/heatmap#7.00/-120.90000/38.36000/hot/all

8. Sometimes they reveal streets that are not on the map ... Data from a fitness-tracker 8< OMM Solutions GmbH > Images by Strava; https://www.strava.com/heatmap#7.00/-120.90000/38.36000/hot/all

9. … somewhere in Afghanistan Data from a fitness-tracker 9< OMM Solutions GmbH > Images by Strava; https://www.strava.com/heatmap#7.00/-120.90000/38.36000/hot/all

10. Example: “Spiegel Mining” by David Kriesel • 70.000 Spiegel Online articles • time-frame ~2 years • everything saved to a database • analysis based on feature-extraction Data Science on data from a news-website 10< OMM Solutions GmbH > Quelle: http://www.dkriesel.com/spiegelmining

11. Step 1: Visualisation Data Science on data from a news-website 11 Graphs by David Kriesel dkriesel.com < OMM Solutions GmbH >

12. Step 2: “Trivial” derivations Data Science on data from a news-website 12 Graphs by David Kriesel dkriesel.com < OMM Solutions GmbH >

13. Step 2: “Trivial” derivations Data Science on data from a news-website 13 Graphs by David Kriesel dkriesel.com < OMM Solutions GmbH >

14. Step 3: More in depth analysis Data Science on data from a news-website 14 Graphs by David Kriesel dkriesel.com < OMM Solutions GmbH >

15. Step 4: Somewhat unrelated interpretations Data Science on data from a news-website 15 Graphs by David Kriesel dkriesel.com < OMM Solutions GmbH >

16. Step 5: Further unrelated interpretations Data Science on data from a news-website 16 Graphs by David Kriesel dkriesel.com < OMM Solutions GmbH >

17. Step 6: Combination of multiple datasources Data Science on data from a news-website 17 Graphs by David Kriesel dkriesel.com; instagram.com; twitter.com + < OMM Solutions GmbH >

18. Data does not deliver “truth” 18 Remember < OMM Solutions GmbH >

19. • Statistics have shown, that employees that used Chrome or Firefox performed better on employment assessment metrics and stayed on longer than those who did not. • => “Users of the Chrome and Firefox browsers are better employees.” • The ones with “relatively feminine” names killed an average of 42 people, the ones with “relatively male” name killed only 15 on average. • => “Female-named hurricanes are more deadly.” • American men 45 to 82 who skip breakfast showed a 27 percent higher risk of coronary heart disease over a 16-year period. • => “Skipping breakfast causes coronary heart disease” ...your turn to guess Correlation != Causation 19 https://blogs.scientificamerican.com/guest-blog/9-bizarre-and-surprising-insights-from-data-science/

20. Correlation != Causation 20 Graphs by Tyler Vigen (http://www.tylervigen.com/spurious-correlations)

21. Correlation != Causation 21 Graphs by Tyler Vigen (http://www.tylervigen.com/spurious-correlations)

22. Correlation != Causation 22 Graphs by Tyler Vigen (http://www.tylervigen.com/spurious-correlations)

23. Vielen Dank für Eure Aufmerksamkeit! 23< OMM Solutions GmbH >

24. Ihr persönlicher Ansprechpartner Fragen oder Interesse? < OMM Solutions GmbH > 24 Olaf Horstmann Technology & Innovation OMM Solutions GmbH Vor dem Lauch 4 70567 Stuttgart Germany oh@omm-solutions.de +49 (0)711 75 86 46 04

25. 25< OMM Solutions GmbH > www.omm-solutions.de OMM Solutions GmbH Vor dem Lauch 4 70567 Stuttgart Geschäftsführer Martin Allmendinger Malte Horstmann Olaf Horstmann Kontakt Telefon: +49 711 6747 051-0 E-Mail: info@omm-solutions.de Umsatzsteuer-ID: DE295716572 Sitz der Gesellschaft: Stuttgart Amtsgericht Stuttgart, HRB 749562 Impressum

The possibilities of information that can be extracted from seemingly simpel data

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