hanifmahboobi

General Background and Overview

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.
Models and Issues in Data Stream Systems
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
Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
[Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep

A curated list of AWS resources to prepare for the AWS Certifications

A curated list of awesome AWS resources you need to prepare for the all 5 AWS Certifications. This gist will include: open source repos, blogs & blogposts, ebooks, PDF, whitepapers, video courses, free lecture, slides, sample test and many other resources.

For more about AWS and AWS Certifications and updates to this Gist you should follow me @leonardofed

| Title | Description

	estimatePi <- function(numDraws){
	r <- .5 #radius... in case the unit circle is too boring
	x <- runif(numDraws, min=-r, max=r)
	y <- runif(numDraws, min=-r, max=r)
	inCircle <- ifelse( (x^2 + y^2)^.5 < r , 1, 0)
	return(sum(inCircle) / length(inCircle) * 4)
	}

	{
	"cells": [
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"About Challenge - Data science challenge is a regression problem where \"target\" prediction is to be done on test data set of 1000 records. Training set is consist of 5000 records with 254 features + 1 target.\n",
	"\n",
	"Data Files - \n",
	"Training File (codetest_train.txt) - 5000 records with 254 features + 1 target\n",

	import boto3

	def role_arn_to_session(**args):
	"""
	Usage :
	session = role_arn_to_session(
	RoleArn='arn:aws:iam::012345678901:role/example-role',
	RoleSessionName='ExampleSessionName')
	client = session.client('sqs')
	"""