This list tries to cover from basic introductory to deep analytical data science materials. You’ll also find various curriculums for learning Data Science. Foundational in both theory and technologies, the Open Source Data Science Masters (OSDSM) breaks down the core competencies necessary to making use of data. All kinds of topics will be covered, including Python, machine learning, specific topics such as statistics, databases, linear algebra / programming, etc., courses, tutorials and a long list of blogs which you can follow and refer to.
Programming
Impatient Perl
This is the 22 February 2013 version of Impatient Perl. This document is for people who either want to learn perl or are already programming in perl and just do not have the patience to scrounge for information to learn and use perl. This document should also find use as a handy desk reference for some of the more common perl related questions.
F# Succinctly
This book is aimed primarily at IT professionals who want to get up to speed quickly on F#. A working knowledge of the .NET Framework and some knowledge of either C# or Visual Basic would be nice, but it’s not necessary.
Distributed Systems for Fun and Profit
This text is focused on distributed programming and systems concepts you’ll need to understand commercial systems in the data center. You’ll learn many key protocols and algorithms including some new exciting ways to look at eventual consistency.
On Lisp
On Lisp is a comprehensive study of advanced Lisp techniques, with bottom-up programming as the unifying theme. It gives the first complete description of macros and macro applications. The book also covers important subjects related to bottom-up programming, including functional programming, rapid prototyping, interactive development, and embedded languages.
Automate the Boring Stuff with Python
In Automate the Boring Stuff with Python, you’ll learn how to use Python to write programs that do in minutes what would take you hours to do by hand-no prior programming experience required.
A Field Guide to Genetic Programming
Genetic programming (GP) is a collection of evolutionary computation techniques that allow computers to solve problems automatically. Since its inception twenty years ago, GP has been used to solve a wide range of practical problems, producing a number of human-competitive results and even patentable new inventions.
Introduction to the Objective Caml Programming Language
This document is an introduction to ML programming, specifically for the Objective Caml (OCaml) programming language from INRIA. OCaml is a dialect of the ML (Meta-Language) family of languages, which derive from the Classic ML language designed by Robin Milner in 1975 for the LCF (Logic of Computable Functions) theorem prover.
Matters Computational: Ideas, Algorithms, Source Code
This is a book for the computationalist, whether a working programmer or anyone interested in methods of computation. The focus is on material that does not usually appear in textbooks on algorithms.