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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. These are all things you could find by reading the documentation, but they are interesting enough to write up examples to highlight some interesting things you can do. A lot of these end up being useful for writing systems with immutable data (especially auditing, and sometimes reporting systems). 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.”

Yhat | Blog. A set of top Computer Science Education blogs | Computing: The Science of Nearly Everything. Math ∩ Programming | A place for elegant solutions. Blog Archive TopCoder, Inc. 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. Hmm. 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.