I created an R package for exploratory data analysis. You can read about it and install it here.
The package contains several tools to perform initial exploratory analysis on any input dataset. It includes custom functions for plotting the data as well as performing different kinds of analyses such as univariate, bivariate and multivariate investigation which is the first step of any…Continue
Added by Ujjwal Karn on May 18, 2016 at 8:30am — No Comments
Summary: Will Automated Predictive Analytics be a boon to professional data scientists or a dangerous diversion allowing well-meaning, motivated but amateur users try to implement predictive analytics. More on the conversation started last week about new One-Click Data-In Model-Out platforms.
I have always been very much…Continue
At my LinkedIn Profile, I recently got an email from a Dell Recruiter who was looking to interview me for a Marketing Data Scientist position that she was trying to fill. The location was all wrong for me, but the email really got me thinking about marketing data science, and what it was about my LinkedIn profile that had piqued her interest.
I mean, as a small business owner, I wear many hats… In fact, I’d say that about 30% of the work I do with my business is related to marketing…Continue
"Information is the oil of the 21st century, and Analytics is the combustion engine."
The Volume, Variety and Velocity of data coming into your organization continue to reach unprecedented levels. This phenomenal growth means that not only…Continue
Added by Anuj Tripathi on February 26, 2016 at 12:00am — No Comments
By Pasha Roberts, Chief Scientist & Co-founder, Talent Analytics, Corp.
Over the years, our firm has had many discussions with employers on the eve of a new talent analytics project. Often, it is the firm’s first deep-dive look at employee data. Sometimes we act as a strategic sounding board, and sometimes we can help them move directly forward into predictive analytics. It is always interesting.
This article will discuss two analytics approaches that we have…Continue
Added by Mike Kennedy on February 9, 2016 at 4:30am — No Comments
Blog post by Great Roberts. This is a great article to print and/or forward to HR and specifically the data scientist hiring manager in your organization.
I had yet another call today with a brilliant data scientist working inside of a Human Resources Department of a major business. This HR data scientist has both a strong analytics and predictive analytics background. She has a Bachelor’s Degree in Statistics and a Master’s Degree in Predictive Analytics. She excels in R,…Continue
Added by Mike Kennedy on February 2, 2016 at 2:00am — No Comments
Summary: At least one instance of Real Time Predictive Model development in a streaming data problem has been shown to be more accurate than its batch counterpart. Whether this can be generalized is still an open question. It does challenge the assumption that Time-to-Insight can never be real time.
A few months back I was making my way through the latest literature on “real time analytics” and “in stream analytics” and my blood pressure was rising. …Continue
Data Science is the system used to extract insights from data that’s mined from various sources. Using various techniques including predictive modeling, Data Science helps to analyze and interpret vast amounts of data. The people who apply Data Science to manage large amounts of data are called Data Scientists. Let’s see how Data Science correlates with the…Continue
Added by Vaishnavi Agrawal on January 8, 2016 at 11:30pm — No Comments
The fundamental assumption in many predictive models is that the predictors have normal distributions. Normal distribution is un-skewed. An un-skewed distribution is the one which is roughly symmetric. It means the probability of falling in the right side of mean is equal to probability of falling on left side of mean.
This article outlines the steps to detect…Continue
By Greta Roberts, CEO, Talent Analytics, Corp.
Imagine that Chris wants to buy a house and needs a mortgage. He applies online and is sent an email by an intern asking to schedule time to discuss his interest. The intern conducts the initial screening conversation, they schedule him for an in person interview during which time he is interviewed by quite a few folks who ask many questions.…Continue
Added by Mike Kennedy on December 11, 2015 at 12:00pm — No Comments
Unsupervised learning algorithms are machine learning algorithms that work without a desired output label. A supervised machine learning algorithm typically learns a function that maps an input x into an output y, while an unsupervised learning algorithm simply analyzes the x’s without requiring the y’s. Essentially, the algorithm attempts to estimate the underlying structure of the population of x’s (in other…Continue
Added by Aureus Analytics on November 16, 2015 at 10:00pm — No Comments
I have been using the term "mass data assignment" in my blogs. I thought I should offer the community some simulated examples. These are simple simulations: all the data is in one place in an agreeable format. The file contents are meant to be easy to peruse. When I was younger, there was a television series called "Stargate SG-1." I have a number of seasons on DVD. In this series, a special branch of the U.S. Air Force visits offworld sites using stable wormholes: teams enter the wormholes…Continue
Added by Don Philip Faithful on November 14, 2015 at 6:41am — No Comments
"Half the money I spend on advertising is wasted; the trouble is I don't know which half."
John Wanamaker, a department store merchant and marketing pioneer in the late 19th and early 20th century (as well as Postmaster General from 1889 to 1893), is reputed to have made this statement and advertisers have been wrestling with the question ever since.
Enter the science of marketing measurement. In the early days the questions revolved around the…Continue
Added by Gregory Thompson on October 23, 2015 at 1:00pm — No Comments
Life scientists collect similar type of data on daily basis. Statistical analysis of this data is often performed using SAS programming techniques. Programming for each dataset is a time consuming job. The objective of this paper is to show how SAS programs are created for systematic analysis of raw data to develop a linear regression model for prediction. Then to show how PROC SQL can be used to replace several data steps in the code. Finally to show how SAS macros are created on these…Continue
Added by Venu Perla PhD on October 10, 2015 at 9:00am — No Comments
When discussing the use of algorithms, the issue of durability or portability has to be considered. For example, a stock trading algorithm might be used in a missile guidance system. The algorithm would have to operate on an abstract kinetic level rather than for a specific application. I have written in the past about using the same algorithm to study stocks, earthquakes, hurricanes, electro-cardiograms, and attempts at evasion - using my mouse in a game environment. Wouldn't an abstraction…Continue
Added by Don Philip Faithful on October 4, 2015 at 7:17am — No Comments
Most data scientists and statisticians agree that predictive modeling is both art and science yet, relatively little to no air time is given to describing the art. This post describes one piece of the art of modeling called feature engineering which expands the number of variables you have to build a model. I offer six ways to implement feature engineering and provide…Continue
The analytical scene has recently been dominated by the prediction that we would soon experience an important shortage of analytical talent. As a response, academic programs and massive open online courses (MOOCs) have sprung up like mushrooms after the rain, all with the purpose of developing skills for the analyst or its more modern counterpart, the data scientist. However, in the …Continue
Added by Geert Verstraeten on August 27, 2015 at 11:30pm — No Comments
Data modeling is usually one of those subjects that make people's eyes glaze over. It's not really programming, though understanding programming concepts such as objects, inheritance, polymorphism and similar multisyllabic words is usually helpful to do modeling. It's not a business analyst function, though most BAs end up participating in the modeling process. Perhaps the best way of thinking about modeling is to see it as a way to describe a business in clearly defined pieces.
Added by Kurt A Cagle on August 8, 2015 at 2:30pm — No Comments
Today’s marketers are becoming technically savvier. They understand the need to improve customer experiences or implement digital marketing strategies to engage consumers across channels. Customer retention and acquisition, Big Data, social media marketing,…Continue
Added by Larisa Bedgood on July 22, 2015 at 10:10am — No Comments