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Using Data Effectively: Beyond Art and Science

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Video and slides synchronized, mp3 and slide download available at URL https://bit.ly/2Qc0N3W.

Hilary Parker talks about approaches and techniques to collect the most useful data, analyze it in a scientific way, and use it most effectively to drive actions and decisions. This talk provides actionable insights about how to apply the lens of design thinking to help use data more effectively in everyday job. Filmed at qconsf.com.

Hilary Parker is a Data Scientist at Stitch Fix where she focuses on what sorts of data to collect from clients in order to optimize clothing recommendations, as well as building out prototypes of algorithms or entirely new products based on new data sources. She is a co-founder of the Not So Standard Deviations podcast, a bi-weekly data science podcast that has over half a million downloads.

Publicada em: Tecnologia
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Using Data Effectively: Beyond Art and Science

  1. 1. InfoQ.com: News & Community Site • Over 1,000,000 software developers, architects and CTOs read the site world- wide every month • 250,000 senior developers subscribe to our weekly newsletter • Published in 4 languages (English, Chinese, Japanese and Brazilian Portuguese) • Post content from our QCon conferences • 2 dedicated podcast channels: The InfoQ Podcast, with a focus on Architecture and The Engineering Culture Podcast, with a focus on building • 96 deep dives on innovative topics packed as downloadable emags and minibooks • Over 40 new content items per week Watch the video with slide synchronization on InfoQ.com! https://www.infoq.com/presentations/ data-techniques
  2. 2. Purpose of QCon - to empower software development by facilitating the spread of knowledge and innovation Strategy - practitioner-driven conference designed for YOU: influencers of change and innovation in your teams - speakers and topics driving the evolution and innovation - connecting and catalyzing the influencers and innovators Highlights - attended by more than 12,000 delegates since 2007 - held in 9 cities worldwide Presented at QCon San Francisco www.qconsf.com
  3. 3. “You’ve told me everything NOT to do, but how will I know what to do?” -Anonymous Roger Peng student
  4. 4. “Data analysis is hard, and part of the problem is that few people can explain how to do it. It’s not that there aren’t any people doing data analysis on a regular basis. It’s that the people who are really good at it have yet to enlighten us about the thought process that goes on in their heads.”
  5. 5. “The demand for this “right” brain thinking is increasing and in era of increased automation, the need for the “art” of data science will be the increasing cry of business.”
  6. 6. https://r4ds.had.co.nz/explore-intro.html
  7. 7. You re-run the analysis and get different results. Someone else can’t repeat the analysis. You can’t re-run the analysis on different data. An external library you’re using is updated, and you can’t recreate the results. You change code but don’t re-run downstream code, so the results aren’t consistently updated. You change code but don’t re-execute it, so the results are out of date. You update your analysis and can’t compare it to previous results. You can’t point to code changes that resulted in a different analysis results. A second analyst can’t understand your code. Can you re-use logic in different parts of the analysis? You change the logic used in analysis, but only in some places where it’s implemented. Your code is not performing as expected, but you don’t notice. Your data becomes corrupted, but you don’t notice. You use a new dataset that renders your statistical methods invalid, but you don’t notice. You make a mistake in your code. You use inefficient code. A second analyst wants to contribute code to the analysis, but can’t do so. Two analysts want to combine code but cannot. You aren’t able to track and communicate known next steps in your analysis. Your collaborators can only make requests in email or meetings, and they aren’t incorporated into the project.
  8. 8. “An engineer who thinks they’re going to be reprimanded is disincentivized to give the details necessary to get an understanding of the mechanism, pathology and operation of the failure.” – John Allspaw
  9. 9. “the purpose of this paper is to established the opinions for developing the technical artifact, rather than developing the narrative of an analysis.”
  10. 10. Software is the invisible writing that whispers the stories of possibility to our hardware. You are the storyteller. Grady Booch
  11. 11. “ “ “ “ https://multithreaded.stitchfix.com/blog/2017/12/13/latentsize/
  12. 12. “Data analysis is hard, and part of the problem is that few people can explain how to do it. It’s not that there aren’t any people doing data analysis on a regular basis. It’s that the people who are really good at it have yet to enlighten us about the thought process that goes on in their heads.”
  13. 13. “You’ve told me everything NOT to do, but how will I know what to do?” Anonymous Roger Peng student
  14. 14. “The demand for this “right” brain thinking is increasing and in era of increased automation, the need for the “art” of data science will be the increasing cry of business.”
  15. 15. One thing that is clear is that sketches enable designers to handle different levels of abstraction simultaneously… Clearly this is something important in the design process. We see that designers think about the overall concept and at the same time think about detailed aspects of the implementation of that concept. Nigel Cross
  16. 16. Design ability is, in fact, one of the three fundamental dimensions of human intelligence. Design, science, and art form an ‘and’ not an ‘or’ relationship to create the incredible human cognitive ability.” Nigel Cross
  17. 17. the capacity to understand or feel what another person is experiencing from within their frame of reference, i.e., the capacity to place oneself in another's position.
  18. 18. Watch the video with slide synchronization on InfoQ.com! https://www.infoq.com/presentations/ data-techniques

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