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DTSTART:20200923T030000
DTEND:20200923T050000
SUMMARY;CHARSET=UTF-8:Summer Academy Session: Data Science (in the Real Wor
 ld)
URL:https://www.sfg.at/e/summer-academy-session-data-science-in-the-real-wo
 rld/
DESCRIPTION;CHARSET=UTF-8:Data Science (in the Real World)\nData is the new
  oil. Similar to raw oil directly from the well\, raw data also cannot be 
 used to fuel machine learning algorithms. Instead\, it needs to be careful
 ly refined and the precious useful information needs to be separated from 
 irrelevant noisy information. In this installment of the workshop series w
 e shed light on the importance of data quality assessments\, data preproce
 ssing and knowledge transfer between domain experts and data scientists.\n
 \nData Science (in the Real World)\n\n23.09.2020 15:00 &#8211\; 17:00 | La
 nguage: English | Register free of charge for session\n\n\nTarget group:\n
 People interested in data science (researchers\, practitioners\, …)\nAbs
 tract:\nData is the new oil. Similar to raw oil directly from the well\, r
 aw data also cannot be used to fuel machine learning algorithms. Instead\,
  it needs to be carefully refined and the precious useful information need
 s to be separated from irrelevant noisy information. In this installment o
 f the workshop series we shed light on the importance of data quality asse
 ssments\, data preprocessing and knowledge transfer between domain experts
  and data scientists. In addition we will discuss a selection of pitfalls 
 and even paradoxical data science results. We will not only acknowledge th
 eir existence but aim to also provide practical advice on how to handle si
 tuations like skewed datasets\, for example cases where there are only a f
 ew examples in a dataset of a potentially undesired phenomenon.\nAfter the
  event you will know:\n\n\n\nData quality concerns &amp\; KPIs\n\n\n\n\n\n
 \nCode &amp\; data books\n\n\n\n\n\n\nParadoxical data science results\n\n
 \n\n\n\n\nData cleaning &amp\; preprocessing\n\n\n\n\n\n\nData validation\
 n\n\n\n\n\n\nModel debugging\n\n\n\n\n\n\nNon-linear correlation &amp\; co
 rrelation does not imply causation\n\n\n\n\n\n\nFeature selection &amp\; o
 utlier detection\n\n\n\n\n\n\nMachine learning with skewed &amp\; imbalanc
 ed data\n\n\n\n\nSpeaker\n\n\n\n\n\nRoman Kern\nResearch Area Manager Know
 ledge Discovery\n\n\n\n\n\n\n\nOliver Pimas\nBig Data Lab\n\n\n\n\n\n\n\nM
 atthias Böhm\nResearch Area Manager Data Management\n\n\n\n\n
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