The demand and the supply gap for a data scientist are ever-increasing. In fact, in one of its surveys, IBM predicts increment in data science jobs to be 364,000 to 2720,000 in 2020 which is only going upwards in the subsequent years. Python, as a programming language, is immensely popular for building data science-based applications owing to its simplicity, and large community support and ease of deployment.

Our Data Science with Python online course has been…

Summary: Not enough labeled training data is a huge barrier to getting at the equally large benefits that could be had from deep learning applications.  Here are five strategies for getting around the data problem including the latest in One Shot Learning.

For at least the last two years we’ve been in an…

There has been much hype surrounding deep learning and data science learning in recent times, and one of the cornerstones of deep learning is the neural network. In this article, we will look at what a neural network is and get familiar with the relevant terminologies.

In simplest terms, a neural network is an interconnection of neurons. Now the question arises, what is a neuron? To understand neurons in deep learning, we first…

“Arguably the most significant development in information technology over the past few years, blockchain has the potential to change the way that the world approaches big data, with enhanced security and data quality just two of the benefits afforded to businesses using Satoshi Nakamoto’s landmark technology.”…

This is a tutorial to show how to implement dashboards in R, using the new "flexdashboard" library package.

this new library leverages these libraries and allows us to create some stunning dashboards, using interactive graphs and text. What I loved the most, was the “storyboard” feature that allows me to present content in Tableau-style frames. Please note that for this you need to create RMarkdown (.Rmd) files and insert the code using the…

A data scientist needs to be Critical and always on a lookout for something that misses others. So here is some advice that one can include in the day to day data science work to be better at their work:

1. Beware of the Clean Data Syndrome

You need to ask yourself questions even before you start working on the data. **Does this data make sense?** Falsely assuming that the data is clean could lead you towards wrong Hypotheses. Apart from that, you can discern a…

Data has been preserved in various formats for a long time. After the boom of computers and electronic, most of the data storage migrated to the digital domain. And in the contemporary world data is preferred to be stored online.

With millions of people daily adding to the already , data runs in humongous numbers and is humbly termed as . The source of this data may vary and need not necessarily deal with online…

"There is no evidence that anybody has been converted by a pie chart."

So said Martin Palmer, secretary general of the Alliance of Religions and Conservation, on BBC’s Beyond Belief.

He went on to say, "People are converted by stories, by narrative, by emotion, by an appeal to the heart."

This was a discussion about climate change and the Pope’s encyclical on the issue.…

Behind every successful person, there is tons of coffee.

Successful or not or on way to success, coffee is the beverage of choice for many of us. Today a coffee drinker faces a ‘choice overload’ problem – there are too many varieties, blends, and roasts to choose from. Is there a way to simplify this problem? How can data science help us to streamline our choices without sacrificing individual preferences?

Data science refers to knowledge discovery from large volumes of…