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Techy References for Coding

Techy References for Coding

 

In my last post I was asked to supply some of the sources I consult for coding and IT-like stuff.

Here is the list. It is not exhaustive but these are the areas that post the most helpful things in my opinion. If anyone has more please add them to the list in the comments area!

Open Blogs/Sites:

Digital Scholarship in the Humanities

A List Apart

Revolutions

Information & Visualization (no longer updated)

Enterra Insights

CITO Research

Try R

Idre at UCLA

Pragmatic Perspectives

Data Science Central /

Nettuts+

Python

Eisen Lab

Data science Insights

Analytics Bridge

LinkedIn Group/Blogs:

Advanced Analytics

Advanced Business Analytics, Data Mining, and Predictive Modeling

AIIM Global Community of Information Professionals

Big Data, Analytics and Data Science Training

Business Analytics

Data Science Central

NLP People

Taxonomy Community of Practice

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4 comments

Here are a few that you might want to add to the list:

Rosetta Code is a chrestomathy for coding languages (awesome): http://rosettacode.org/wiki/Main_Page

Quick-R is a good place to get started with some R-script examples: http://www.statmethods.net/

RDataMining is greta resource for mining with R: http://www.rdatamining.com/home

R Programming Wikibook: https://en.wikibooks.org/wiki/R_Programming

Data Mining Algorithms with R Wikibook: https://en.wikibooks.org/wiki/Data_Mining_Algorithms_In_R

Rob Hyndman's site is full of useful stuff you can do with TeX and R: http://robjhyndman.com/hyndsight/

TeXample is a great resource for doing cool stuff with TeX, LaTeX and TikZ: http://www.texample.net/

LaTeX Templates has a multitude of templates for TeX and LaTeX: http://www.latextemplates.com/

LaTeX Wikibook is probably the LaTeX resource that I use most: https://en.wikibooks.org/wiki/LaTeX

 

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These are excellent resources, thanks for sharing. I think there are a host of tools and resources out there for powerful data analysis and scripting. The challenge for many Digital Humanities and Social Science scholars are the massive barriers to entry--even in spite of all the amazing new and old resources available online for learning to code and learning to analyze data. What are other's thoughts on this?

Also, another great site for basic data analysis work is: http://schoolofcode.org.

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Learning

Try Ruby (what it says on the tin)

Try Git (if you want to learn how to use git on the command line)

Stack Overflow (intimidating to newcomers, but frequently provides quick responses to short questions)

Project Euler (math-oriented code problems.  Will make you a better programmer, but not for the faint of heart)

Learn X in Y Minutes (quick overviews of different programming languages' key features)

The Node Beginner Book (an excellent and comprehensive primer on Node.js)

Node School (fun interactive Node.js tutorials that run in your terminal)

Reference

Cassandra vs MongoDB vs CouchDB vs Redis vs Riak vs HBase vs Couchbase vs Neo4j vs Hypertable vs ElasticSearch vs Accumulo vs VoltDB vs Scalaris (an overview of the benefits and disadvantages of various non-SQL database systems)

Pro Git (free, comprehensive, and good book about using Git, command line-focused)

Stream Handbook (A primer and reference on using streams in Node.js)

Most of the O'Reilly books are great, though lately I've been enjoying Functional Javascript and MongoDB: The Definitive Guide

Software

Stanford's Natural Language Processing software page (Free and open source!)

Various placeholder image services (for when you don't have real images for your website or app yet)

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