hkilter

Setting up OS X for Data Science

I had to reinstall my laptop and at the same time I had new team member joining to the team. Therefore I started to write this as a tutorial or check list on how to setup a new MacBook Pro OS X for typical data science development. This is geared towards Scala based development and Spark as that's what we do at the moment. However, I'll start slightly more generally and will add some other things too. Let's start from the basics...

OS X

OS X is great for data science. However, it's missing configurations and apps that you need. Let's get started.

We need a good package manager, text editor, github source control, code editors and so on. But first will look at the command line, Terminal.

Terminal

Open up Terminal. If you don't know where to find it, open Spotlight search and type Terminal into it. Now, right click on it's icon in the Dock. Select Options - Keep in Dock. This way, it's always there when you need it. And you'll need it.

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Project Title

One Paragraph of project description goes here

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisites

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// Simple function to send Weekly Status Sheets to contacts listed on the "Contacts" sheet in the MPD.

// Load a menu item called "Project Admin" with a submenu item called "Send Status"
// Running this, sends the currently open sheet, as a PDF attachment
function onOpen() {
  var submenu = [{name:"Send Status", functionName:"exportSomeSheets"}];
  SpreadsheetApp.getActiveSpreadsheet().addMenu('Project Admin', submenu);  
}

function exportSomeSheets() {

hkilter

# sources:
# http://www.jgoodwin.net/?p=1223
# http://orgtheory.wordpress.com/2012/05/16/the-fragile-network-of-econ-soc-readings/
# http://nealcaren.web.unc.edu/a-sociology-citation-network/
# http://kieranhealy.org/blog/archives/2014/11/15/top-ten-by-decade/
# http://www.jgoodwin.net/lit-cites.png


###########################################################################
# This first section scrapes content from the Web of Science webpage. It takes

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AsciiDoc Writer’s Guide

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## Our simulated scientist will declare 
## significance only if he/she gets 
## 2 replications with p<0.05:
stringent<-FALSE
## Set the above to FALSE if you want to 
## have the scientist publish a single
## expt. as soon as it's significant:
#stringent <- FALSE

## num of scientists to simulate:

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// Use Gists to store code you would like to remember later on
console.log(window); // log the "window" object to the console

hkilter

#!/usr/bin/env ruby
# 
# This script is a wrapper around pandoc that uses pandoc's
# builtin citeproc support to generate a markdown bibliography
# from bibtex.
#
# Inspired by Jacob Barney's [bib2mkd][] script.
#
# [bib2mkd]: http://jmbarney.dyndns.org/?/linux/bib2mkd/
#