Gives an overview of life cycle stages for data science. Tell which prerequisite stages you have completed and know what to do next. Managers learn which steps can be contracted out and how to hire; job seekers learn how to tell if a prospective company is really ready to do what they say they're going to do, and how to learn and practice for the specific type of work you want to be doing. I offer a taxonomy of what specifically is accomplished in each stage as an alternative to ambiguous job titles.
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Describes a organized process for investigating systems which used to work and now don't, with particular applications to web technology stacks. Especially helpful for computer scientists who are not familiar with system engineering and understanding emergent behavior; good training for developers going into operational roles. Shows that some data you can collect will usefully partition your possible problem space, and other data is “associated” with the problem but not actually useful. Also includes guidance on how to teach this skill to the junior members of your team.
slides (sadly, I do not have audio or video for this one)
How does data science differ from software development? What are appropriate ways to run an agile data science team? Discusses prioritization of projects by estimating benefits assuming a successful technical completion, testing a minimal implementation as quickly as possible, and working with the business and engineering.
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As an entrepreneur or executive at a company which has no existing data science function, how can you hire your first person and get a successful outcome? How to understand your needs and goals. Also goes into the use of a take-home exercise and presentation for hiring in more detail than my 2018 talk.
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I'd be happy to discuss creating a talk on your preferred topic for your conference or gathering.
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If you have somehow arrived here looking for my old work on US airspace congestion or running Hadoop clusters, let me know and I'll do my best to find it for you in the archives.