sloria

The Best of the Best Practices (BOBP) Guide for Python

A "Best of the Best Practices" (BOBP) guide to developing in Python.

In General

Values

• "Build tools for others that you want to be built for you." - Kenneth Reitz
• "Simplicity is alway better than functionality." - Pieter Hintjens

debasishg

General Background and Overview

1. Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
2. Models and Issues in Data Stream Systems
3. Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
4. Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
5. [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t

staltz

The introduction to Reactive Programming you've been missing

(by @andrestaltz)

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This tutorial as a series of videos

If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.

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prakhar1989

Rich Hickey on becoming a better developer

Rich Hickey • 3 years ago

Sorry, I have to disagree with the entire premise here.

A wide variety of experiences might lead to well-roundedness, but not to greatness, nor even goodness. By constantly switching from one thing to another you are always reaching above your comfort zone, yes, but doing so by resetting your skill and knowledge level to zero.

Mastery comes from a combination of at least several of the following:

chroth7

React <-> D3 Resources

Thanks for all the stars.

Due to unexpected demand, I move this to a proper github repo, see here: https://github.com/chroth7/reactD3resources

I would very much like to get your PRs there, thank you!

fredbenenson

layout	title	description	tags
default	SQL Style Guide	A guide to writing clean, clear, and consistent SQL.	data process

Purpose

hgavert

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.

cube-drone

Automation for the People

Long ago, the first time I read "The Pragmatic Programmer", I read some advice that really stuck with me.

"Don't Use Manual Procedures".

This in the chapter on Ubiquitous Automation. To summarize, they want you to automate all the things.

The trouble was that I hadn't much of an idea how to actually go

AustinRochford

zpalexander

[[ -s "$HOME/.profile" ]] && source "$HOME/.profile" # Load the default .profile

[[ -s "$HOME/.rvm/scripts/rvm" ]] && source "$HOME/.rvm/scripts/rvm" # Load RVM into a shell session *as a function*

export NVM_DIR="$HOME/.nvm"
  . "$(brew --prefix nvm)/nvm.sh"

 #  ---------------------------------------------------------------------------
#
#  Description:  This file holds all my BASH configurations and aliases