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

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TECHtalks #18
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www.tech-talks.eu

2. Einmal im Monat ist TECHtalk Zeit! First come first served!
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3. Talk: The possibilities of information that can be
extracted from seemingly simple data
Speaker: Olaf Horstmann
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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
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...things to remember
Prequisit
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5. Data from a fitness-tracker
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Images by Strava; https://www.strava.com/heatmap#7.00/-120.90000/38.36000/hot/all
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6. Runners
Data from a fitness-tracker
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Images by Strava; https://www.strava.com/heatmap#7.00/-120.90000/38.36000/hot/all

7. Cyclists
Data from a fitness-tracker
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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
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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
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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
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Quelle: http://www.dkriesel.com/spiegelmining

11. Step 1: Visualisation
Data Science on data from a news-website
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Graphs by David Kriesel dkriesel.com
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12. Step 2: “Trivial” derivations
Data Science on data from a news-website
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Graphs by David Kriesel dkriesel.com
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13. Step 2: “Trivial” derivations
Data Science on data from a news-website
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Graphs by David Kriesel dkriesel.com
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14. Step 3: More in depth analysis
Data Science on data from a news-website
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Graphs by David Kriesel dkriesel.com
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15. Step 4: Somewhat unrelated interpretations
Data Science on data from a news-website
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Graphs by David Kriesel dkriesel.com
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16. Step 5: Further unrelated interpretations
Data Science on data from a news-website
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Graphs by David Kriesel dkriesel.com
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17. Step 6: Combination of multiple datasources
Data Science on data from a news-website
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Graphs by David Kriesel dkriesel.com; instagram.com; twitter.com
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18. Data does not deliver “truth”
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Remember
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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
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https://blogs.scientificamerican.com/guest-blog/9-bizarre-and-surprising-insights-from-data-science/

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

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

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

23. Vielen Dank für Eure Aufmerksamkeit!
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24. Ihr persönlicher Ansprechpartner
Fragen oder Interesse?
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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

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www.omm-solutions.de
OMM Solutions GmbH
Vor dem Lauch 4
70567 Stuttgart
Geschäftsführer
Martin Allmendinger
Malte Horstmann
Olaf Horstmann
Kontakt
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