The methodology described here has broad applications, leading to new statistical tests, new type of ANOVA (analysis of variance), improved design of experiments, interesting fractional factorial designs, a better understanding of irrational numbers leading to cryptography, gaming and Fintech applications, and high quality random number generators (and when you really need them). It also features exact arithmetic / high performance computing and distributed algorithms to compute millions of…

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Data science uses many different probability distributions, but some are used more than others. This one picture shows an overview of five probability distributions data scientists will  find the most useful. See below the image for more information about the distributions.

Further Information:…

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We all know how horror movies work: there is someone evil in the neighborhood (like Michael Myers in “Halloween” or Freddy Krueger in “Nightmare on Elm Street” or Tom Cruise in “War of the Worlds”) and the soon-to-be-victim, we’ll say in this case, an unassuming CEO, wanders into a dark space (basement, closet, wood shed, attic) where the evil doer is waiting for them with a new agenda for the digital enterprise they can’t assimilate and they perish.

Slash! Cut! Gouge! Scream!…

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Machine Learning (ML) models are increasingly being used to augment human decision making process in domains such as finance, telecommunication, healthcare, and others. In most of the cases, users do not understand how these models make predictions. The lack of understanding makes it difficult for policy makers to justify their decisions. Most of the ML models are black boxes that do not explain on its own why it reached a specific recommendation or a decision. This forces the users to say…

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Role of Augmented Analytics in Future !!  Insight generation is changing (and That’s a Good Thing)

 

Have you ever been in situation when you need quick insight generation but due to lack of resources and information you lost large amount of revenue? We live in data world, not only data but Big Data. Datasets are being generated in very large volume, variety and very high speed. Just forget about handling this type of data with your Traditional BI system. Due…

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Filters are the key thing in Computer Vision(Processing image data). You would have probably used different kinds of filters like the blur filter, vintage filters, etc  in photo editing apps. Ever thought about how those filters work? . How do they give the…

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ELAINE Symbolic AI offers community tool to cure “TL;DR” Syndrome

 

The idea of applying Natural Language tool to advance human intelligence is not new. Examples can be found among popular search engines and chat-bots. These applications generally require Machine Learning ahead of lengthy preparations with “human in the loop” training datasets. These pre-requisites are cost intensive in terms of time, labor, infrastructure and skill.

 

A few years…

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Summary:  How to measure the degree and value of AI adoption among companies or even countries is hard.  Here’s a beginning proposal on how to get started.

 

We talk a great deal about whether there are enough data scientists to go around, whether our advancements in AI techniques are better than others, if…

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AI in healthcare is something that is revolutionizing the industry and medical treatment that we as the patients receive. But AI, in general, is making inroads into virtually every field and aspect of society. Healthcare AI companies like NVIDIA healthcare and Google DeepMind Health are breaking new ground, with innovations that are helping to save lives. Let's dive into the world of AI so that you can have a better understanding of what it is all about and where it is going.…

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You are familiar with the term data lake. A data lake is a repository used to store unlimited volume of data. These days, most of the cloud service providers allow us to host scalable data lakes for storing data as it arrives. For using these data lakes, it is not required to structure the data and we can run different types of applications on it. Usually they are applications for big data analytics and machine learning. These applications need entire data to be present in one data lake and… Continue

This post is based on two insightful threads I read online (References below)

Based on these, I address the question of ‘The difference between Statistics and Data Science’. Traditionally, most people, including me, would say that ‘statistics came first and Data Science builds upon statistics’. This chain of thought is valid but as you see…

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Monday newsletter published by Data Science Central. Previous editions can be found here. The contribution flagged with a + is our selection for the picture of the week. To subscribe, follow this link.  …

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I live in Jacksonville, Florida, where the odds of a hurricane on any given day are "improbable". But does that mean I shouldn't stock up on hurricane supplies and have an emergency preparedness plan? Far from it. Hurricane Irma blew through my city in 2017, causing around $85 million in damage and the worst…

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“We are too busy building the solution to bother the customer to identify, validate, value and prioritize their requirements.” – Name not disclosed to protect the ignorant

I see this habit all the time with experienced people; the tendency to jump right into solution mode when dealing with customers. Many of these experienced “experts” have seen the “problem” so many times before, that they just “know” the right solution. Heck, sometimes these “experts” even cut off or…

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In one great video.

Enjoy the video!

Summary: It's no secret that Python-Pandas is central to data management for analytics and data science today. Indeed, what we're seeing now is Pandas being extended to handle ever-larger data. Underappreciated is that Pandas is a tunable platform, supporting its own datatypes as well as those from numerical library Numpy. Together, these comprise…

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Summary: The Gartner Magic Quadrant for Data Science and Machine Learning Platforms is just out the big news is how much more capable all the platforms have become.  Of course there are also some interesting winner and loser stories.

 

The Gartner Magic Quadrant for Data Science and Machine Learning Platforms is just out for 2020.  The really big news is how many excellent choices are now available.  In a remarkable move, the whole field of…

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Which masters or PhD course should I choose for Data Science or Artificial Intelligence?

 

This is one of the most common questions I am asked

 

Here is my view – and the usual caveats apply – i.e. the viewpoint is personal and is not associated with any company or institution I am…

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“Begin with an end in mind” – Stephen Covey “7 Habits of Highly Effective People”

No truer words have ever been spoken.  A simple phrase that makes everyone’s life easier and more productive.  Our data science team tries to live by that simple phrase, but it’s difficult because we want to jump right into “solution mode” …we have seen so many similar problems, that we already know and understand how to solve that problem.  And that’s an…

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Knowledge acquisition is about building generalization capabilities. In machine learning world, generalization refer to the model's ability to make accurate predictions from never before seen data. Well generalized models possess intelligence to work on data from new scenarios. This is true for human intelligence also. Children start learning from examples and initially they fail to respond properly to unforeseen situations. Gradually they acquire generalization skills to respond to all… Continue