Venu Kanaparthy vsingh58

## LDA_SparkDocs
/*
This example uses Scala.  Please see the MLlib documentation for a Java example.

Try running this code in the Spark shell.  It may produce different topics each time (since LDA includes some randomization), but it should give topics similar to those listed above.

This example is paired with a blog post on LDA in Spark: http://databricks.com/blog
Spark: http://spark.apache.org/
*/

import scala.collection.mutable

## ds-training.md

      
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If you were to give recommendations to your "little brother/sister" on things
that they need to do to become a data scientist, what would those things be?

I think the "Data Science Venn Diagram" (http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram) is a great place to start. You need three things to be a good data scientist:

Statistical knowledge
Programming/hacking skills
Domain expertise

Statistical knowledge


## 35-hour_workweek_with_python.ipynb

      
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## Dynamic-Time-Series-Modeling.ipynb

      
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## FB_quicklook.ipynb

      
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## file.md

      
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    Datasets for Graph Hack 2014.

All stores are Neo4j 2.1.3.
Transportation

Flights

What is related, and how?
Flight	ORIGIN Airport


## Graphing
{
 "metadata": {
  "name": "Three new matplotlib plots"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {

## gist:17d7c3b5af2c2d31d3ba

      
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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&amp;rep


## 0.setup.sh
# using four part files to construct "minitweet"
cat rawtweets/part-0000[1-3] > minitweets

# change log4j properties to WARN to reduce noise during demo
mv conf/log4j.properties.template conf/log4j.properties
vim conf/log4j.properties # Change to WARN

# launch Spark shell REPL
./bin/spark-shell

## 0.textrank_init.py
import nltk
nltk.download()

## use nltk.download() within a Python prompt to
## download the `punkt` data

## Anaconda is recommended, to pick up NumPy, NLTK, etc.
## http://continuum.io/downloads

## this also requires TextBlob/PerceptronTagger
	/*
	This example uses Scala. Please see the MLlib documentation for a Java example.

	Try running this code in the Spark shell. It may produce different topics each time (since LDA includes some randomization), but it should give topics similar to those listed above.

	This example is paired with a blog post on LDA in Spark: http://databricks.com/blog
	Spark: http://spark.apache.org/
	*/

	import scala.collection.mutable
	{
	"metadata": {
	"name": "Three new matplotlib plots"
	},
	"nbformat": 3,
	"nbformat_minor": 0,
	"worksheets": [
	{
	"cells": [
	{
	# using four part files to construct "minitweet"
	cat rawtweets/part-0000[1-3] > minitweets

	# change log4j properties to WARN to reduce noise during demo
	mv conf/log4j.properties.template conf/log4j.properties
	vim conf/log4j.properties # Change to WARN

	# launch Spark shell REPL
	./bin/spark-shell
	import nltk
	nltk.download()

	## use nltk.download() within a Python prompt to
	## download the `punkt` data

	## Anaconda is recommended, to pick up NumPy, NLTK, etc.
	## http://continuum.io/downloads

	## this also requires TextBlob/PerceptronTagger