Predictive analytics tools are a key asset in detecting natural disasters. With higher accuracy than other weather detection sensors, they can detect early signs of an oncoming calamity to prevent mistakes like the one that happened in 2016.…

How accurate is predictive analytics? Is it worth using for my business? How can forecasting and prediction help me in such an uncertain environment? These are all valid questions and they are they are questions your business (and your fellow business owners) must grapple with to understand the value of planning and analytical tools.

While predictive…

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Wouldn’t it be nice to have a sneak-peek into the future of your business to make informed decisions and eliminate guesswork? With the help of predictive modeling, this is…

Advanced Analytics helps to discover insights by applying machine learning to the analysis process. This enables improved decision-making and efficiency of the business. …

Summary: This is a discussion of social injustice, real or perceived, promulgated or perpetuated by machine learning models.  We propose a simple solution based on wide spread misunderstanding of what ML models can do.

This is a discussion of social injustice, real or perceived, promulgated or perpetuated by…

As a data scientist in an organization you frequently find yourself in a couple of situations:

Summary:  A little history lesson about all the different names by which the field of data science has been called, and why, whatever you call it, it’s all the same thing.

A little reminiscence, or for those of you who are only recently data scientists, a little history lesson. 

Our profession of…

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 portal, white paper…. you name it, unpredictable weather is on the Top-5…

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…

In the article, Data Science Should Monitor Big Brother by Arjan Haring, one important…

Finding out the difference between data scientists, data engineers, software engineers, and statisticians can be confusing and complicated. While all of them are linked to data in a way, there is an underlying difference between the work they do and manage. 

The growth of data  and its usage across…

Summary:  Which is more important, the data or the algorithms?  This chicken and egg question led me to realize that it’s the data, and specifically the way we store and process the data that has dominated data science over the last 10 years.  And it all leads back to Hadoop.

Recently I was challenged to speak on the role of data in data…

Today, data scientists are generally divided among two languages — some prefer R, some prefer Python. I will not try to explain in this article which one is…

Here is our list of featured articles and resources posted since Monday.

• AI’s Ethical Dilemma – An Unexpectedly Urgent Problem 
• Artificial Intelligence to Amplify FinTech …