Big Data Problems: Crash Course Statistics #39



There is a lot of excitement around the field of Big Data, but today we want to take a moment to look at some of the problems it creates. From questions of bias and transparency to privacy and security concerns, there is still a lot to be done to manage these problems as Big Data plays a bigger role in our lives.

Special thanks to Dr. Sameer Singh, the University of Washington, and the University of California Irvine for the content provided in this video.

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32 thoughts on “Big Data Problems: Crash Course Statistics #39”
  1. I didn't know how much I needed to here this until right now… thank you so much. It'll definitely give my research more meat

  2. I noticed some slippery speech here. Was compass wrong about different races offending at different rates ? It wasn't clear if compass was 200% actual rates or if black defendants actually were offending at twice the rate.

  3. Generalizations is what is expected from big data, so just because you don't agree with what the algorithm says doesn't mean that it is wrong. The algorithm might spew politically wrong results but statistically, it is right.

  4. I am so sorry Adriene Hill. I might seem a little ungrateful but your enthusiasm on the Economics crash course was so contagious. It really made me interested in Economics and made the videos so much fun. Really loved them! These are great as well!

  5. A lot of us have never stopped looking at the total disregard for privacy since they created all this. You are only as silly as your leash.

  6. I think I was being tracked but after examining the information gathered a determination was made that I’m boring AF😞

  7. You guys are really helping me understanding different subjects for my media-exam. Will defiantly use your videos for my other subjects as well. Thank you!

  8. Conspicuously absent is talk about buying and selling user data to create audience-targetted political propaganda…

  9. One of the biggest issues in big data is outsourcing algorithms, which gives companies plausible deniability when the program does something that could be considered unfair with the data.

  10. …there's an interesting perspective on university science, that should call attention to the persistent belief than science can be advanced pointwise, instead of uniformly: it's called "functional analysis" where the researcher assigns data values to a metric field basis e.g. EMF(x,y,z,t), whereas 'reality→reality' outputs the same as the inputs, to be understood…

  11. …big-data-analysis hides-while-pretending-to-solve the basic flaw in designing-for-the-test instead of designing-for-the-spec: researchers/reviewers/readers, users, don't-get-to-know whether enough-data was included/anonymized-excluded; like those old NAVY recruitment ogive tests, a box between two men may carry a lopsided transmission—so, you're a cook…

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