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Talk Big Data Conference Munich - Data Science needs real Data Scientists.
 

Talk Big Data Conference Munich - Data Science needs real Data Scientists.

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How to hire a real Data Scientist? Data Science and Big Data are hypes. It has become very sexy to be a Data Scientist. More and more self-appointed Data Scientist are found on the market. To be sure ...

How to hire a real Data Scientist? Data Science and Big Data are hypes. It has become very sexy to be a Data Scientist. More and more self-appointed Data Scientist are found on the market. To be sure to get a real one you have to test him/her.

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    Talk Big Data Conference Munich - Data Science needs real Data Scientists. Talk Big Data Conference Munich - Data Science needs real Data Scientists. Presentation Transcript

    • Laboratory for Web Science Dept. Informatics University of Applied Sciences of Switzerland (FFHS)  http://lwsffhs.wordpress.com http://lws.ffhs.ch  Follow @blattnerma  Dr. Marcel Blattner Laboratory for Web Science, Dr. Marcel Blatter
    • What is a real Data Scientist? Laboratory for Web Science, Dr. Marcel Blatter
    • “Knowing the name of something does not mean to know something” ― Richard P. Feynman Laboratory for Web Science, Dr. Marcel Blatter
    • data driven tasks – what Data Scientists should do business relevant questions knowledge generation / visualisation fetch’n store data Data Scientist testing / model assessment data normalization feature engineering / modelling Laboratory for Web Science, Dr. Marcel Blatter
    • data driven tasks – what Data Scientists should do business relevant questions fetch’n store data Data Scientist testing / model assessment data normalization feature engineering / modelling number crunching knowledge generation / visualisation Laboratory for Web Science, Dr. Marcel Blatter
    • data driven tasks – what Data Scientists should do knowledge generation / visualisation fetch’n store data Data Scientist testing / model assessment data normalization feature engineering / modelling number crunching human interpretation business relevant questions Laboratory for Web Science, Dr. Marcel Blatter
    • skill-cloud and a data-hero Laboratory for Web Science, Dr. Marcel Blatter
    • skill-cloud and a data-hero DH Laboratory for Web Science, Dr. Marcel Blatter
    • skill-cloud and a data-hero DH This guy lives in the land of OZ Laboratory for Web Science, Dr. Marcel Blatter
    • the birth of a self-appointed data-hero Laboratory for Web Science, Dr. Marcel Blatter
    • …do not hire a self-appointed data-hero… self-appointed data-hero’s recommender engine Laboratory for Web Science, Dr. Marcel Blatter
    • …do not hire a self-appointed data-hero… self-appointed data-hero’s recommender engine You did like this Laboratory for Web Science, Dr. Marcel Blatter
    • …do not hire a self-appointed data-hero… self-appointed data-hero’s recommender engine You did like this …then you might like that one as well Laboratory for Web Science, Dr. Marcel Blatter
    • data science is interdisciplinary…you need a team T E A M HAC=hacking/tech. AN=analytics/math/stats ST/KO=strategic/communicator Laboratory for Web Science, Dr. Marcel Blatter
    • data science is interdisciplinary…you need a team T E A M common language is key HAC=hacking/tech. AN=analytics/math/stats ST/KO=strategic/communicator Laboratory for Web Science, Dr. Marcel Blatter
    • data scientists should be scientists Methodology objectivity falsifiability reproducibility Laboratory for Web Science, Dr. Marcel Blatter
    • data scientist – test it! Challenge the candidate •  Real business data •  Kaggle competition •  Artificially generated data Laboratory for Web Science, Dr. Marcel Blatter
    • data scientist – test it! Yule-Simpson effect Laboratory for Web Science, Dr. Marcel Blatter
    • data scientist – test it! curse of dimensionality Laboratory for Web Science, Dr. Marcel Blatter
    • data scientist – test it! bias-variance tradeoff Low Variance OUGH r major consequence: much of it you have. f (say) 100 variables 2100 − 106 examples you figure out what further information, flipping a coin. This at different form) by years ago, but even ng stem from failing mbody some knowlt’s given in order to lized by Wolpert in cording to which no all possible functions . How then can we he functions we want awn uniformly from functions! In fact, ness, similar exam- High Variance High Bias Low Bias Figure 1: Bias and variance in dart-throwing. Laboratory for Web Science, Dr. Marcel Blatter quirks in the data. This problem is called overfitting, and is
    • data scientist – test it! the curse of big data correlations by chance Laboratory for Web Science, Dr. Marcel Blatter
    • Summary •  •  •  •  Don’t hire a self-appointed data-hero Build a data science team Challenge potential candidates Be skeptic Laboratory for Web Science, Dr. Marcel Blatter