Summary:  True prescriptive analytics requires the use of real optimization techniques that very few applications actually use.  Here’s a refresher on optimization with examples of where and how they’re best used.

Predictive analytics and optimization have gone hand in hand since the very beginning.  But in…

Wondering how the words, fashion, weather and predictive analytics are connected?

Here’s a poser – what is one of the biggest challenges before the global fashion industry today? Weather. You wouldn’t have guessed it, right?

Pick up any fashion magazine, read any fashion…

A smoothly running sensor data analytics tool may be just as difficult to manage as a symphony orchestra. Because every musician in an orchestra – and every part of an IoT system – needs to work properly and ‘harmonize’ with the others. But how do conductors make their orchestras work so nicely and sound so heavenly instead of creating a mismanaged cacophony? Obviously, there’s a lot of practice involved. But besides that, they definitely know what pitfalls they need to avoid. Which is why,…

R has spread deep into the private sector and can be found in the production pipelines at some of the most advanced and successful enterprises. 

Learn the fundamentals of data analysis in the second edition of Data Analysis with R, authored by data scientist…

Sales prediction is an important part of modern business intelligence.  First approaches one can apply to predict sales time series are such conventional methods of forecasting as ARIMA and  Holt-Winters. But there are several challenges while using these methods. They are: multilevel daily/weekly/monthly/yearly seasonality,  many exogenous  factors which impact sales, complex trends in different time periods. In such cases, it is not easy to apply conventional methods. Of course, there is…

A long, long time ago (maybe 10 years) the data analytics industry was fairly easy to define and track. Back in that pre-historic era SAS was considered the gold standard of analytics companies with a comprehensive range of solutions addressing the demands of many industries.  Given the relative paucity of data, analytics tended to focus on those industries that generated usable data.  Companies that were part of the analytics universe back then would have included:

• risk…

Summary:  A year ago we wrote about the emergence of fully automated predictive analytic platforms including some with true One-Click Data-In Model-Out capability.  We revisited the five contenders from last year with one new addition and found the automation movement continues to move forward.  We also observed some players from last year have now gone in different directions.  …

“If (there) was one thing all people took for granted, (it) was conviction that if you feed honest figures into a computer, honest figures (will) come out. Never doubted it myself till I met a computer with a sense of humor.”

― Robert A. Heinlein, The Moon is a Harsh Mistress

This post is the first in a series of articles in which we will explain what Machine Learning is. You don’t have to have formal training or…

Overview

This is a project I've been working on for some time to help improve the missed opportunity rate (no-show rate) at all medical centers. It demonstrates how to extract datasets from an SQL server and load them directly into an R environment. It also demonstrates the entire machine learning process, from engineering new features, tuning and training the model, and finally measuring the model's performance. I would like to share my results and methodology as a guide to help…

In this post, we consider different approaches for time series modeling. The forecasting approaches using linear models, ARIMA alpgorithm, XGBoost machine learning algorithm are described. Results of different model combinations are shown. For probabilistic modeling the approaches using copulas and Bayesian inference are considered.

INTRODUCTION

Time series analysis, especially forecasting, is an important problem of modern…

A popular phrase tossed around when we talk about statistical data is “there is correlation between variables”. However, many people wrongly consider this to be the equivalent of “there is causation between variables”. It’s important to explain the distinction: Correlation means that once we know how one variable changes we can make reasonable deductions about how other variables change There are several variants of correlation:

1. Positive…

One of the most typical tasks in machine learning is classification tasks. It may seem that evaluating the effectiveness of such a model is easy. Let’s assume that we have a model which, based on historical data, calculates if a client will pay back credit obligations. We evaluate 100 bank customers and our model correctly guesses in 93 instances. That may appear to be  a good result – but is it really? Should we consider a model with 93% accuracy as adequate?

It depends. Today, we…

In the previous post of our Understanding machine learning series, we presented how machines learn through multiple experiences. We also explained how, in some cases, human beings are much better at interpreting data than machines. In many tasks machines still can’t replace humans, who understand surrounding reality better and can make more accurate decisions.

Machines can be given a…

In a prior post I outlined some thoughts on the outlook for the data analytics sector and referenced a database I prepared of analytics companies.  At the time the list comprised about 400 names categorized into a number of sectors and segments.

I’ve continued to update the list since that time and it now comprises about 800 companies.…

PREAMBLE:  

This video was built as a result of our internal hackathon using Teradata Listener to absorb real time small messages from Transformers and other devices on the Power Grid in Southern California. The video demonstrates a real time predictive analytic showcasing proactive repairs of the power grid to reduce costs and avoid disruptions of power service.…

For companies newly endeavoring in establishing capabilities in Data Science, it is important to keep a few crucial points in mind. Clean data, applicable models, and business intuition are all key to success. Do not remove any of them from the equation. Data Science is essentially about identifying and/or creating the cleanest possible data set, then searching mathematically for patterns within it. The goal should be to help business users make important data-driven…