9 popular ways to perform Data Visualization in Python
The beauty of an art lies in the message it conveys. At times, reality is not what we see or perceive. The endless efforts from the likes of Vinci and Picasso have tried to bring people closer to the reality using their exceptional artworks on a certain topic/matter. Data scientists are no less than artists. They make paintings in form of digital visualization (of data) with a motive of manifesting the hidden patterns / insights in it. It is even more interesting to know that, the tendency of human perception, cognition and communication increases when he / she gets exposed to visualized form of any content/data. There are multiple tools for performing visualization in data science. In this article, I have demonstrated various visualization charts using Python.

Learning about classes in R with plot.bike()
A useful feature of R is its ability to implement a function differently depending on the ‘class’ of the object acted on. This article explores this behaviour with reference to a playful modification of the ‘generic’ function plot() to allow plotting of cartoon bicycles. Although the example is quite simple and fun, the concepts it touches on are complex and serious.

Analyzing R-Bloggers’ posts via Twitter
For those who don’t know, every time a new blog post gets added to R-Bloggers, it gets a corresponding tweet by @Rbloggers, which gets seen by Rbloggers’ ~20k followers fairly fast. And every time my post gets published, I can’t help but check up on how many people gave that tweet some Twitter love, ie. ‘favorite’d or ‘retweet’ed it. It’s even more exciting than getting a Facebook ‘like’ on a photo from Costa Rica! Seeing all these tweets and how some tweets get much more attention than others has gotten me thinking. Are there some power users who post almost all the content, or do many blogs contribute equally? Which posts were the most shared? Which blog produces the highest quality posts consistently? Are there more posts during the weekdays then weekends? And of course the holy grail of bloggers – is there a day when it’s better to post to get more shares?

Top 10 data mining algorithms in plain English
Today, I’m going to explain in plain English the top 10 most influential data mining algorithms.

Effect Size Statistics in Logistic Regression
Effect size statistics are expected by many journal editors these days. If you’re running an ANOVA, t-test, or linear regression model, it’s pretty straightforward which ones to report. Things get trickier, though, once you venture into other types of models. Many of the common effect size statistics, like eta-squared and Cohen’s d, can’t be calculated in a logistic regression model. So now what do you use?