Nowadays, industries are privileged by the opportunity to apply data science to reach new heights in their efficiency, productiveness, and overall success. The range of these opportunities is pervasive starting with advanced calculations for the business to customer service…

I have used synthetic data sets many times for simulation purposes, most recently in my articles Six degrees of Separations between any two Datasets and How to Lie with p-values. Many applications (including the data…

Guest blog by  Michael Grogan.

Here is how we can use the maps, mapdata and ggplot2 libraries to create maps in R.



In this particular example, we’re going to create a world map showing the points of Beijing and Shanghai, both cities in China. For this particular…

If you still can’t figure out what exactly Hadoop is, don’t worry, you’re not alone. But hopefully this multi-part blog series will help! Hadoop is very difficult to understand if, like most people, you’re gathering bits and pieces of info here and there. And, sorry Wikipedia, but your page isn’t very helpful if you aren’t a…

You may have figured out already that statistics isn't exactly a science. Lots of terms are open to interpretation, and sometimes there are many words that mean the same thing—like "mean" and "average"—or sound like they should mean the same thing, like significance level and confidence level.  

Although they sound very similar, significance level and confidence level are in fact two completely different concepts. Confidence levels and confidence…

Introduction

Co-relation does not equal causation – is a mantra drilled into a Data Scientist from an early age

That’s fine ..

But very few talk of the follow-on question ..

How exactly do you determine causation?

This problem is…

There are a lot of on-going controversies on the impact of Artificial Intelligence on designing and development. While, designing is a bit of a complex process; as the humans only have the capability to set the context and create what the user’s really are looking for. But researchers and designers are constantly working on the technology in an attempt to find out how AI will affect the future of designing industry. …

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.  

Announcements

• Earn a Data…

They say that the best ideas sometimes come to you while you are in the shower, and this idea of how to explain two important Neural Network concepts – Backpropagation and Stochastic Gradient Descent – actually did come to me as I was trying to set the perfect water temperature for my morning shower.

As I was struggling to adjust the two shower handles – one handle that controlled scolding hot and the other handle that controlled flash freezing – it occurred to me that I was a simple…

With intense urbanization, the transportation industry works round the clock to suffice the demands of…

Every industry in the world is moving towards data-driven decision making, then one of the most popular and …

Here is our selection of featured articles and technical resources posted since Monday:

Resources

• TensorFlow 1.x vs 2.x. – summary of changes
• The problem that Google Solved with Quantum Supremacy…

Overview of changes TensorFlow 1.0 vs TensorFlow 2.0

Earlier this year, Google announced TensorFlow 2.0, it is a major leap from the existing TensorFlow 1.0. The key differences are as follows:

Ease of use: Many old libraries (example tf.contrib) were removed, and some consolidated. For example, in TensorFlow1.x the model could be made using Contrib, layers, Keras or estimators, so many options for the same task confused many new users.…

The new era of BigData and advances in technology have made significant transitions towards the high functionality of IoT devices. The popularity of IoT devices has led to more easier methods for BigData collection, analysis, and distribution at a rapid rate. According to a report by…

Bayesian Machine Learning (part -6)

Probabilistic Clustering – Gaussian Mixture Model

Continuing our discussion on probabilistically clustering of our data, where we left out discussion on part 4 of our Bayesian inference series. As we have seen the modelling theory of…