Rise of the Data Scientist | FlowingData - The Diigo Meta page

• 13 Aug 09
  

  Todd Suomela

  data statistics visualization career

• 24 Jul 09
  

  catelijne coopmans
  • data fans
  • fry gets into this as its one of his arguments for an interdisciplinary field –
    whereas in a collaboration, a person would have to explain to another what he
    wanted, have some misunderstandings along the way, and then wait.
  • 5 more annotations...
  • • The only problem of being amazing is it makes others uncomfortable in their own
      skin

    • I like Fry’s approach but in this time and age you’d have to accept that
      these skills are not necessarily mastered by the same person.

      
      

      The best mathematician could not the best graphic artist who is not
      necessarily the best interface designer who is not always a subject-matter
      expert, etc.

      
      

      Some people have all those skills and then some, but that’s more the
      exception than the rule.

    • What about “storyteller?” Not trying to trivialize the issue at all, but the
      ability to effectively communicate the relevance and import of the findings
      would seem to be the skill that ties it all together. Completing the analysis
      isn’t the end of the project, getting the HiPPO’s sign-off is. If all the effort
      doesn’t go toward meeting an organizational goal, it’s wasted.

    • Can we predict a theory [like special theory of relativity] using
      data?
      Can data help us to prove a theorem like Fermat’s Last Theorem?

    • the reason data visualization isn’t popular might also be because it’s so
      exclusive/scientific/precise that people don’t want to touch/enjoy/appreciate
      it!
• 

  Yap Xiong

  Article in the Flowing Data blog by Nathan Yau, which addresses the predicted rise of the "data scientist" (someone who can take information & visualize/ present it a simple way)

  flowing data blog visualization data scientist article list: article

  • Data Scientist
  • able to extract information from large datasets and then present something of use to non-data experts.
  • 6 more annotations...
  • • Visualization - graphs, tools, etc.
    • skills and talents from often disjoint areas of expertise:
    • We're seeing data scientists - people who can do it all - emerge from the rest of the pack.
    • Oftentimes, visualization projects are disjoint processes and involve a lot of waiting
    • Terabytes of data are coming in whether it be from websites or from sales across the country, but in an area where Excel is the tool of choice (or force), there are limitations, hence all the tools, applications, and consultancies to help out.
    • the more you learn, the more you can do, and the higher in demand you will be as the amount of data grows and the more people want to make use of it.
• 10 Jul 09
  

  Fadzlan

  career statistics

• 07 Jul 09
  

  Isk Aldur

  statistics

• 03 Jul 09
  

  kristin wolff

  flowingdata datamining data visualization

• 20 Jun 09
  

  RoobZZ Tex
• 16 Jun 09
  

  Roger Chen

  data mining research academia career

  • As we've all read by now, Google's chief economist Hal Varian commented in January that the next sexy job in the next 10 years would be statisticians. Obviously, I whole-heartedly agree.
  • you'd know that by statisticians, he actually meant it as a general title for someone who is able to extract information from large datasets and then present something of use to non-data experts.
  • 1 more annotations...
  • • • Statistics - traditional analysis you're used to thinking about
      • Data Munging - parsing, scraping, and formatting data
      • Visualization - graphs, tools, etc.
• 12 Jun 09
  

  Damon Snyder

  data dataminig bi design web analytics

• 11 Jun 09
  

  Takuya Homma
  • • Statistics - traditional analysis you're used to thinking about
    • Data Munging - parsing, scraping, and formatting data
    • Visualization - graphs, tools, etc.
  • They have a combination of skills that not just makes independent work easier and quicker; it makes collaboration more exciting and opens up possibilities in what can be done. Oftentimes, visualization projects are disjoint processes and involve a lot of waiting. Maybe a statistician is waiting for data from a computer scientist; or a graphic designer is waiting for results from an analyst; or an HCI specialist is waiting for layouts from a graphic designer.
  • 2 more annotations...
  • • This need for data scientists is quite evident in business applications where educated decisions need to be made swiftly. A delayed decision could mean lost opportunity and profit. Terabytes of data are coming in whether it be from websites or from sales across the country, but in an area where Excel is the tool of choice (or force), there are limitations, hence all the tools, applications, and consultancies to help out. This of course applies to areas outside of business as well.

    • 
      

      Even if you're not into visualization, you're going to need at least a subset of the skills that Fry highlights if you want to seriously mess with data. Statisticians should know APIs, databases, and how to scrape data; designers should learn to do things programmatically; and computer scientists should know how to analyze and find meaning in data.

      
      

      Basically, the more you learn, the more you can do, and the higher in demand you will be as the amount of data grows and the more people want to make use of it.

• 08 Jun 09
  

  nytwm1

  datamining

• 06 Jun 09
  

  Andrew Murphie

  data arc innovation

• 04 Jun 09
  

  Carlos Santos

  datamining visualization statistics toread

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