Comments
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MR
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SAAs an IT guy with a kid finishing up his PhD in stats I may be biased but I thought it was a great interview. The discussion of the upstream sources, how the data gets priced, and how the landscape would need to change to grow the company were all interesting topics. I did not get the "commercial" feel others did. I'd think whenever you leave the analyst arena and talk to actual company owners you will get some company focus, but I thought it was done well.
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SAAs an IT guy with a kid finishing up his PhD in stats I may be biased but I thought it was a great interview. The discussion of the upstream sources, how the data gets priced, and how the landscape would need to change to grow the company were all interesting topics. I did not get the "commercial" feel others did. I'd think whenever you leave the analyst arena and talk to actual company owners you will get some company focus, but I thought it was done well.
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GCI can certainly understand someone spending 50 - 80% of their time wrangling data after studying Udacity's Data Analyst program. Great interview, and yes more and more companies are hiring data scientists. I have noticed mining companies building data science teams.
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RWVery interesting.
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RMLove quandl. A wonderful resource. This was a great backgrounder interview for a fan like me. I use many free tickers to easily update my excel models. They allow 50000 free data calls a day. Will very possibly pay for premium data at some stage. What a great range of data, and always growing. Bravo Tammer and RV.
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SRSounded rather like a 45min commercial to me. We've had interviews along these same lines before - didn't really add anything that hasn't already been talked about (IMHO).
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ATHow does limiting distribution of these data sets such that the advantage does not become arbed away not qualify the information as MNPI?
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SF...challenge the interviewees.
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SFPossibly the most important "signal" was rhe admission by the interviewer at the beginning that he was an early investor. My one problem with RV is the tendancy to not have interviewers that really c
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MMI would love to know how they go about backing out their estimates of how much capital can be put behind a particular proprietary data set before the advantage gets arbed out.