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

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

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

  1. 1. OMM Solutions TECHtalks #18 1< OMM Solutions GmbH > www.tech-talks.eu
  2. 2. Einmal im Monat ist TECHtalk Zeit! First come first served! < OMM Solutions GmbH > 2
  3. 3. Talk: The possibilities of information that can be extracted from seemingly simple data Speaker: Olaf Horstmann 3< OMM Solutions GmbH >
  4. 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. 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. 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. 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. 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. 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. 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. 11. Step 1: Visualisation Data Science on data from a news-website 11 Graphs by David Kriesel dkriesel.com < OMM Solutions GmbH >
  12. 12. Step 2: “Trivial” derivations Data Science on data from a news-website 12 Graphs by David Kriesel dkriesel.com < OMM Solutions GmbH >
  13. 13. Step 2: “Trivial” derivations Data Science on data from a news-website 13 Graphs by David Kriesel dkriesel.com < OMM Solutions GmbH >
  14. 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. 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. 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. 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. 18. Data does not deliver “truth” 18 Remember < OMM Solutions GmbH >
  19. 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. 20. Correlation != Causation 20 Graphs by Tyler Vigen (http://www.tylervigen.com/spurious-correlations)
  21. 21. Correlation != Causation 21 Graphs by Tyler Vigen (http://www.tylervigen.com/spurious-correlations)
  22. 22. Correlation != Causation 22 Graphs by Tyler Vigen (http://www.tylervigen.com/spurious-correlations)
  23. 23. Vielen Dank für Eure Aufmerksamkeit! 23< OMM Solutions GmbH >
  24. 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. 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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