As we wrote about in September of last year, the supercomputing world was being dominated by Chinese entrants. For the first time in 5 years, the U.S. no longer sported a top 3 fastest supercomputer. The Titan supercomputer at Oak Ridge National Laboratory clocked a measly 17.6 petaflops, running up against the top two Chinese systems, clocking in at 93 and 33.8 petaflops respectively. And those differences aren’t just numerical bragging rights — they represent the ability to blaze the trail on AI,
Nearly every business leader (99% according to an HBR study) recognizes the need to build a data driven organization. However, so many struggle to use data for a variety of reasons, a big one of them being that data exists in all sorts of places. This is especially the case with startups, who use a number of out of the box tools that don’t connect to each other, and that only offer so much granularity and flexibility. The solution? A data warehouse.
A great article by Matt Gershoff that covers risk, reward, opportunity costs, and just generally interesting thoughts on experimentation. Basically, "if you just want to pick between A and B, do you really need to run standard significance tests at a 90% or 95% confidence levels?" Short answer, no. The article covers the logic as well as math behind why (and when) to just "go-with-it" (maybe even flipping a coin). Why is this article (and approach) helpful? Because it can help with what type of approach to take based on the problem.
Data science is the field of applying advanced mathematical and statistical techniques to large sets of data to uncover insights that can improve the performance of different parts of a business. Data science is used in growth to uncover commonalities between types of users that are either successful or unsuccessful with a product. Marketers and growth teams use data science findings to improve the performance of their product, user acquisition funnel, user retention and more. These are the best articles on how to use data science to grow your business. From how to analyze data using R, pandas for Python, regression, model building, SQL and more, you'll learn how to apply data science concepts to growth.
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