Mistakes to Avoid in Data Science

/01 Introduction/

001 Avoid common mistakes to excel in data science.mp4

001 Avoid common mistakes to excel in data science-en.srt

/02 Mistakes to Avoid/

015 Not confirming with stakeholders.mp4

014 Not telling a story with the data.mp4

007 Lack of documentation.mp4

008 Relying solely on formal education.mp4

006 Not considering the level of variation.mp4

005 Failing to adopt new tools.mp4

009 Taking too long to share results.mp4

002 Skipping the fundamentals.mp4

011 Overpromising solutions to stakeholders.mp4

010 Including your bias.mp4

012 Building tools from scratch.mp4

001 Communicating with overly technical language.mp4

004 Having a data set that is too small.mp4

003 Moving too quickly.mp4

013 Assuming the knowledge level of stakeholders.mp4

014 Not telling a story with the data-en.srt

015 Not confirming with stakeholders-en.srt

008 Relying solely on formal education-en.srt

005 Failing to adopt new tools-en.srt

007 Lack of documentation-en.srt

011 Overpromising solutions to stakeholders-en.srt

006 Not considering the level of variation-en.srt

012 Building tools from scratch-en.srt

009 Taking too long to share results-en.srt

004 Having a data set that is too small-en.srt

002 Skipping the fundamentals-en.srt

001 Communicating with overly technical language-en.srt

003 Moving too quickly-en.srt

010 Including your bias-en.srt

013 Assuming the knowledge level of stakeholders-en.srt

/03 Conclusion/

001 Get started on the right path.mp4

001 Get started on the right path-en.srt

/

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Mistakes to Avoid in Data Science-description.txt

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