F# for Scientists Jon Harrop
Publisher: Wiley-Interscience

Jon Harrop, the owner of Flying Frog Consultancy, has released his F# book, F# for Scientists. Sep 25, 2007 - So it was not that surprising to discover that one of the reasons of all the praise received by Dr. Jan 21, 2010 - I actually got down to writing some more code for my n-body model in F#. I got down to reading some of “F# for Scientists” and a couple of really interesting blog posts on building an Asteroids game for the Xbox using F#. I'm looking forward to the release of his F# for scientists book. Here is the catch: The F# language itself was designed to allow scientists to write complex algorithms with few lines of code. May 31, 2006 - But since F# and C# are different languages what is the difference anyway? Jun 30, 2011 - I get that when starting up a new lab that there will be inevitable speed bumps. I haven't got my hardcopy just yet, but I read a draft of this book, and was really very impressed. Oct 9, 2008 - F# was born in Microsoft Research, and its main creator is Don Syme. Much of that is With an interactive widget architecture that's 100% language-agnostic, these days IPython is used by many other programming language communities2, including Julia, Haskell, F#, Ruby, Go, and Scala. Although is a functional language, it supports object programming, too. Sure, this didn't stop me from swearing when the freezer I ordered came with plugs that didn't fit into the receptacles available. After years of development, now it's mature. Jon Harrop's book OCaml for Scientists is its set of examples. Importance of those paradigms) such as parallel processing (which used to be almost exclusively FORTRAN with extensions and is now often functional programming languages like F#). Aug 28, 2008 - I can't believe I missed posting about this - F# for Scientists is now out! Feb 20, 2009 - But the engineering and science types need a little love too, which with imperative programming such as C#, Java is not really able to supply. Jan 19, 2014 - As I noted in a recent post on reproducing data projects, notebooks have become popular tools for maintaining, sharing, and replicating long data science workflows.