The Endeavour — The blog of John D. Cook

I help people make decisions in the face of uncertainty. Sounds interesting. I’m a data scientist. Not sure what that means, but it sounds cool. I study machine learning. I’m into big data. Even though each of these descriptions makes a different impression, they’re all essentially the same thing. There are distinctions. “Decision-making under uncertainty” emphasizes that you never have complete data, and yet you need to make decisions anyway. “Data science” stresses that there is more to the process of making inferences than what falls under the traditional heading of “statistics.” Despite the hype around the term data science, it’s growing on me. Machine learning, like decision theory, emphasizes the ultimate goal of doing something with data rather than creating an accurate model of the process that generates the data. “Big data” is a big can of worms. Bayesian statistics is much older than what is now sometimes called “classical” statistics.

Math ∩ Programming | A place for elegant solutions Bartosz Milewski's Programming Cafe Philadelphia Software Developer “Postgres for Developers” – Notes from PGConf NYC 2014 April 8th, 2014 — Code Examples I saw a talk by one of the core Postgres developers, which showed a bunch of interesting tricks to handle business rules in Postgres specific SQL. Example 1: Array Aggregation “array_agg” can be used to combine rows, which sort of resembles a pivot table operation (this is the same set of values that would be passed as arguments to other aggregation functions) If you use the above table as a common table expression, you can also rename the columns in the with block. Example 2: Named Window Functions I’m not sure yet whether this is just syntactic sugar or has real value, but you can set up named “windows.” By way of explanation, a lot of times when you start using aggregate functions (min, max, array_agg, etc), you end up using window functions, which resemble the following: These allow you do calculate aggregate functions (like min/max) without combining all the rows. PGConfNYC Keynote Notes (Gilt)

GigaSpaces Matt Jaynes, Founder of DevOps University, wrote a great post on Hacker News, What is Continuous Deployment, in which he points out one of challenges that many people gloss over when they embark on their DevOps journey. In a nutshell, Matt was saying that, "You should get your house in order before aspiring to do automation or CD." In other words, if your testing is not fully automated or is taking too long to process, then having continuous deployment may not be right for you. The thing that gets me so interested in this entire DevOps movement is that it forces you to think holistically about software development, business cycles and organizational culture. With regards to Jaynes’ post, even if you can build a car faster, lack of proper QA may lead to shipping defective parts, and you will still be shipping slowly. In short, you need to get your house in order before launching continuous deployment right into production. What is an Application-Centric Approach to DevOps? 1. 2. 3.

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