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 …

Data is important. It is not a secret for anybody. We can even paraphrase famous saying mentioning that “who owns the data, owns the world”. And if you are a business person, you should know like no one else. Your activity can be changed for better if you use Big Data sources. Sales growth, clever marketing strategy - you can achieve it using Big Data. Let’s check it out what is Big Data and how you can make use of it.

Big Data - meaning and particularities

In fact, Big Data…

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.  …

The following links describe a set of free SAS tutorials which help you to learn SAS programming online on your own. It includes tutorials for data exploration and manipulation, predictive modeling and some scenario based examples.

SAS (Statistical analysis system) is one of the most popular software for data analysis. It is widely used for various purposes such as data management, data mining, report writing, statistical analysis, business modeling, applications development and data…

Summary:  Quantum computing is already being used in deep learning and promises dramatic reductions in processing time and resource utilization to train even the most complex models.  Here are a few things you need to know.

So far in this series of articles on Quantum computing we showed that…