hanifmahboobi

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')
    """

hanifmahboobi

| Title | Description

hanifmahboobi

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

---

hanifmahboobi

{
 "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",

hanifmahboobi

1. 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

hanifmahboobi

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)
}