Regardless of your level of experience with data, you need to know about predictive analytics. This type of analytics involves sifting through existing data to see if there are any patterns to be discerned. If there are, you then look to see if these patterns can be used to determine any trends or even predict how a scenario will turn out. Predictive analytics aren’t…Continue
By Greta Roberts
When beginning a new predictive analytics project, the client often mentions the importance of a “quick win”. It makes sense to think about delivering fast results, in a limited area, that excites important stakeholders and gains support and funding for more predictive projects. A great…Continue
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
Shifting market conditions and increasing competition continue to hit pressure points for retailers as a whole. Grocery is no exception. The age of omni-channel is well underway providing more options for consumers. New store formats seem to pop up on a regular basis. Shopper loyalty hangs in the balance and all signs point to its continued erosion. Intercontinental competition is about to escalate.
One approach to overcome some of these challenges is…
Added by Tony Agresta on December 11, 2015 at 6:03am — No Comments
In a recent interview on WGN, Bob Mariano, the CEO of Roundy’s was asked the question “What makes a great grocery store?” His response focused on customer care: “A great grocery store is made up of great people that care about their customers and go out of their way to make them feel appreciated.” Today, Mariano’s competes against global companies like Trader Joes and others and plans to merge with The Kroger Company in late 2015. But they still regard themselves as…Continue
Added by Tony Agresta on December 7, 2015 at 7:30am — No Comments
This is the 2nd part of the series. Read the first part here: Logistic Regression Vs Decision Trees Vs SVM: Part I
In this part we’ll discuss how to choose between Logistic Regression , Decision Trees and Support Vector Machines. The most correct answer as mentioned in the …Continue
Added by Aatash Shah on November 19, 2015 at 1:00am — No Comments
Classification is one of the major problems that we solve while working on standard business problems across industries. In this article we’ll be discussing the major three of the many techniques used for the same, Logistic Regression, Decision Trees and Support Vector Machines [SVM].
All of the above listed algorithms are used in classification [ SVM and Decision Trees are also used for regression, but we are not discussing that today!]. Time and again I have seen people asking which…Continue
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
Greta Roberts, CEO
Talent Analytics, Corp.
Human Resources Feels Pressure to Begin Using Predictive Analytics
Today’s business executives are increasingly applying pressure to their Human Resources departments to “use predictive analytics”. This pressure isn’t unique to Human Resources as these same business leaders are similarly pressuring Sales, Customer Service,…
Added by Mike Kennedy on October 29, 2015 at 11:30am — 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
While doing a daily reading exercise about the innovations in the field of big data, I came across this interesting case study.
One of the world’s largest companies which provide refrigerated trucks wanted to increase their operational efficiency along with productivity. With this set objective they collaborated with a risk management application. The results were amazing. There was a reduction in the number of accidents by 15%. On further reading, I came across even…
Added by Aureus Analytics on September 24, 2015 at 11:31pm — No Comments
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
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
This is a continuation of the ‘how to become a data scientist conversation’ (see “So You Want to be a Data Scientist” at…Continue
In this blog, I will be discussing some distinct types of data involved in feedback. The types that I will be covering are as follows: 1) structural; 2) event; 3) quantitative; 4) contextual; and 5) systemic. In 2014, I recall reading a number of blogs about three types of data: prescriptive, descriptive, and predictive. There was a data scientist apparently on tour lecturing extensively about these three types. I don't recall the individual's name. Well, prescription, description, and…Continue
Added by Don Philip Faithful on July 5, 2015 at 4:56am — No Comments
I am a newbie to Bigdata and would like to highlight some significant advantages if incorporated in a company's supply-chain management strategies, expecting the reader's views and suggestions.
Because, in recent past I have developed a online supply-chain management systems in which sellers and customers are matched using an algorithm. It acted as a decision support system and I needed to dig deeper on the available data to get more insights over the data pattern (even for…Continue