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Training Students to Extract Value from Big Data Summary of a Workshop (2015) / Chapter Skim
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2 The Need for Training: Experiences and Case Studies
Pages 8-12

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From page 8...
... • There may be a mismatch between some industry needs and related academic pursuits: current studies of recommendation systems, such as off-line score prediction, do not always correlate well with important industry metrics, such as sales and user engagement. (Guy Lebanon)
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From page 9...
... Ghani showed a promotional video for the University of Chicago summer program and described its applicant pool. Typically, half the applicants are computer science or machine learning students; one-fourth are students in social science, public policy, or economics; and one-fourth are students in statistics.
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From page 10...
... THE NEED FOR TRAINING IN BIG DATA: EXPERIENCES AND CASE STUDIES Guy Lebanon, Amazon Corporation Guy Lebanon began by stating that extracting meaning from big data requires skills of three kinds: computing and software engineering; machine learning, statis tics, and optimization; and product sense and careful experimentation. He stressed that it is difficult to find people who have expertise and skills in all three and that competition for such people is fierce.
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From page 11...
... In addition, methods that perform well on training and test data sets do not perform well in real environments when a user makes a single A/B comparison.1 Lebanon proposed several new ideas to address those characteristics: • Study the correlations between existing evaluation methods and increased user engagement in an A/B test. • Develop new off-line evaluations to account for user context better.
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From page 12...
... Evaluating that type of binary-rating data requires a different set of tools and models, and scaling up from standard data sets to industry data sets remains challenging.
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Key Terms

  • guy lebanon
  • machine learning
  • rayid ghani
  • practical data
  • data scenarios

  • industry metrics
  • nonlinear optimization
  • amazon corporation
  • school district
  • software engineering

  • summer program
  • user engagement
  • sufficient access
  • business goals
  • industry data

  • standard data
  • data whereas
  • data requires
  • evaluating data
  • anonymized data

  • test data
  • massive data
  • proprietary data
  • limited data
  • selection size

  • real environments
  • extracting meaning
  • kinds referred
  • learning students
  • online evaluation


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