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All Blog Posts Tagged 'R' (101)

Open Source Deep Learning Frameworks and Visual Analytics

Deep Learning gets more and more traction. It basically focuses on one section of Machine Learning: Artificial Neural Networks. This article explains why Deep Learning is a game changer in analytics, when to use it, and how Visual Analytics allows business analysts to leverage the analytic models built by a (citizen) data scientist.

What is Deep Learning and Artificial Neural Networks?

Deep Learning is the modern buzzword for artificial neural networks, one of many concepts…

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Added by Kai Waehner on April 23, 2017 at 9:00am — 1 Comment

Hybrid content-based and collaborative filtering recommendations with {ordinal} logistic regression (1): Feature engineering

I will use {ordinal} clm() (and other cool R packages such as…

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Added by Goran S. Milovanović on April 14, 2017 at 11:00pm — No Comments

R for hackers

Last Sunday at Trivadis Tech Event, I talked about R for Hackers. It was the first session slot on Sunday morning, it was a crazy, nerdy topic, and yet there were, like, 30 people attending! An emphatic thank you to everyone who came!

R a crazy, nerdy topic, - why that, you'll be asking? What's so nerdy about using R?

Well, it was about R. But it was neither an introduction ("how to get things done quickly with R"), nor was it even about data science. True, you…

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Added by Sigrid Keydana on March 24, 2017 at 2:30am — No Comments

Simulating data to combat illegal fishing in R

Illegal, Unreported and Unregulated (IUU) fishing is becoming a major issue around the world  . In general, IUU fishing is a broad term encapsulating many different scenarios (i.e. illegal: breaking laws, unreported: Not reporting catch, which may not be illegal, Unregulated: fishing in ways or places where there are no laws). For the purposes of this blog,…

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Added by Grant Humphries on March 9, 2017 at 2:30am — No Comments

Useful R Packages that Aligns with The CRISP DM Methodology

As we all know CRISP DM stands for Cross Industry Standard Process for Data Mining is a process model that outlines the most common approach to tackle data driven problems. Per the poll conducted by KDNuggets in 2014 this was and “is” one of the most popular and widest used methodology. This method of gleaning insights out of the data is very dear to the industry experts and data miners.

As the title suggest I will align some of the most useful R packages with this most popular and…

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Added by Sunil Kappal on February 6, 2017 at 8:00am — 1 Comment

Screencast on debugging in R using wrapper functions

Added by John Mount on February 4, 2017 at 3:30pm — No Comments

List of Companies using R

R is a free programming language for data analysis, statistical modeling and visualization. It is one of the most popular tool in predictive modeling world. Its popularity is getting better day by day. In 2016 data science salary survey conducted by O'Reilly, R was ranked second in a category of programming languages for data science (SQL ranked first). In another popular KDnuggets Analytics software survey poll, R scored top rank with 49% vote. These survey polls answers the question about…

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Added by Deepanshu Bhalla on January 1, 2017 at 9:30am — No Comments

Getting Started with Regression in R

Regressions are widely used to estimate relations between variables or predict future values for a certain dataset.

If you want to know how much of variable "x" interferes with…

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Added by Renata Ghisloti Duarte Souza Gra on December 27, 2016 at 10:00am — No Comments

What are the Big Guys Using?

Summary:  The largest companies utilizing the most data science resources are moving rapidly toward more integrated advanced analytic platforms.  The features they are demanding are evolving to promote speed, simplicity, quality, and manageability.  This has some interesting implications for open source R and Python widely taught in schools but significantly less necessary with these more sophisticated platforms.

 …

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Added by William Vorhies on December 20, 2016 at 8:38am — 6 Comments

Who Made the News? Text Analysis using R, in 7 steps

This post covers the following tasks using R programming:

  • cleans the texts,
  • sorts and aggregates by publisher names
  • creates word clouds and word…
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Added by Ann Rajaram on November 26, 2016 at 3:30am — 5 Comments

R for SQListas (1): Welcome to the Tidyverse

R for SQListas, what's that about?

This is the 2-part blog version of a talk I've given at DOAG Conference this week. I've also uploaded the slides (no ppt; just pretty R presentation ;-) ) to the articles section, but if you'd like a little text I'm encouraging you to read on. That is, if you're in the target group for this…

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Added by Sigrid Keydana on November 17, 2016 at 11:30pm — No Comments

R, Python or SAS: Which one should you learn first?

Python, R and SAS are the three most popular languages in data science. If you are new to the world of data science and aren’t experienced in either of these languages, it makes sense to be unsure of whether to learn R, SAS or Python.

kd-nuggets-poll-2014-programming-languages

Don’t fret, by the time you’re done reading this article, you will know without a doubt which language is the right one for you.

Overview…

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Added by Aatash Shah on November 1, 2016 at 9:30pm — 8 Comments

Don’t run afoul of Scoping Rules in R!

Whether you are a veteran programmer with experience dating back to Fortran, or a new college grad with all the latest technologies, if you use R eventually you will have to worry about scoping!

Sure, we all start out ignoring scoping when we first begin using a new language. So what if all your variables and functions are global - you are the only one using them, right?!?! Unless you give up on R, you will eventually grow beyond your own system - either having to share your code with…

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Added by Connie Brett, Ph.D. on September 8, 2016 at 12:30pm — No Comments

[Data Mining] Association Rules in R (diapers and beer)

[Introduction of Association Rules]



Sometimes, the anecdotal story helps you understand the new concept. But, this story is real. About 15 years ago, in Walmart, a sales guy made efforts to boost sales in his store. His idea was simple. He bundled the products together and applied some discounts to the bundled products. (Now, it became common practices in marketing) For example, this guy bundled bread with jam, so that customers easily found them together. Moreover,…

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Added by Gregory Choi on August 22, 2016 at 7:30am — 5 Comments

Map the Life Expectancy in United States with data from Wikipedia with R

Original post is published at DataScience+

Recently, I become interested to grasp the data from webpages, such as Wikipedia, and to visualize it with R. As I did in my previous post, I use rvest package to get the data from webpage and…

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Added by Klodian on August 5, 2016 at 10:30pm — No Comments

Characteristics of Good Visual Analytics and Data Discovery Tools

Visual Analytics and Data Discovery allow analysis of big data sets to find insights and valuable information. This is much more than just classical Business Intelligence (BI). See this article for more details and motivation: "Using Visual Analytics to Make Better Decisions: the Death Pill Example". Let's take a look at important characteristics to choose the right tool for…

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Added by Kai Waehner on July 27, 2016 at 10:00pm — No Comments

Expand Machine Learning Tools (Part2): Toree Scala and Python in Jupyter Notebook

Data Analytics favorite Apache Spark,  is progressing as a reference standard for Big Data, and a “fast and general engine for large-scale data processing”. In our previous post, we detailed how to expand ML tools using a PySpark kernel and leverage the …

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Added by Marc Borowczak on June 9, 2016 at 10:30am — No Comments

Picking an Analytic Platform



Summary:
Picking an analytic platform when first starting out in data science almost always means working with what we’re most comfortable.  But as organizations grow larger there is a need for standardization and for selecting one, or a few analytic tools.

 

Picking an analytic platform when first starting out in data science almost…

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Added by William Vorhies on May 31, 2016 at 7:00am — 1 Comment

San Francisco Police Department Crime Incidents: Part 1-Time Series Analysis

Introduction



The City and County of San Francisco had launched an official open data portal called SF OpenData in 2009 as a product of its official open data program, DataSF. The portal contains hundreds of city datasets for use by developers, analysts, residents and more. Under the category of Public Safety, the portal contains the list of SFPD Incidents since Jan 1, 2003.

In this post I have done an exploratory time-series analysis on the crime incidents dataset to see…

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Added by Vimal Natarajan on May 30, 2016 at 7:42am — No Comments

Multi-Regression in R (Exxon Mobil stock price ~ WTI, Gas, and S&P500)

[Previous Post]

Single regression on Exxon's stock



[Introduction of Multi-regression]



Let's recall our last job. We conducted the single regression on Exxon Mobil's stock along with WTI crude oil spot price. The result was fantastic, which accounts for 25% of the variation of stock movement. Put it in other way, R-square. The problem is "are you happy with the…

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Added by Gregory Choi on May 20, 2016 at 9:05am — 1 Comment

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