Dear Aspiring Data Scientist,

Before you start using ‘low code’ or ‘drag & drop’ data science tools, please learn the fundamentals.

Why aspire to be ‘Citizen Data Scientist’…

Despite their advantages, Dynamic Shiny Modules can destabilize the Shiny environment and cause its reactive graph to be rendered multiple times. In this blog post, I present how to remove deleted module leftovers and make sure that your Shiny graph observers are rendered just once.

While working with advanced Shiny applications, you have most likely encountered the need for using Shiny…

Python is one of the powerful, high-level, easy to learn programming language that provides a huge number of applications. Some of its features, such as being object-oriented and open source, having numerous IDE’s, etc. make it one of the most in-demand programming languages of the…

What makes a great CEO?

First of all, the answer to that question depends on who raises it: An investor will likely come up with a different answer than an employee. What if you asked a CEO? He might give you his definition. Ask another CEO, and he will likely give you a slightly or even completely different view on this subject.

Ask five people, and you will get five answers

It seems nearly impossible to find an…

How machines process and understand human language

Everything we express (either verbally or in written) carries huge amounts of information. The topic we choose, our tone, our selection of words, everything adds some type of information that can be interpreted and value extracted from it. In theory, we can understand and even predict human behaviour using that information.…

This article explains how to select important variables using boruta package in R. Variable Selection is an important step in a predictive modeling project. It is also called 'Feature Selection'. Every private and public agency has started tracking data and collecting information of various attributes. It results to access to too many predictors for a predictive model. But not every variable is important for prediction of a particular task. Hence it is essential to…

Contributed by Bin Lin. He took NYC Data Science Academy 12 week full-time Data Science Bootcamp programbetween Jan 11th to Apr 1st, 2016. The post was based on his…

Have you ever wondered how to segment your customers? Customer segmentation is a really useful technique to group similar customers together and understand what works for that. You can then tailor your offering and marketing messages to the specific segments. If you do it right, you should be able to see a healthy increase in sales. After all, companies like Amazon target their customers on an individual level so you should at least be targeting them on a segment level.…

Regression is the first technique you’ll learn in most analytics books. It is a very useful and simple form of supervised learning used to predict a quantitative response.

Originally published on Ideatory…

Ggplot2 is the most elegant and aesthetically pleasing graphics framework available in R. It has a nicely planned structure to it. This tutorial focusses on exposing this underlying structure you can use to make any ggplot. But, the way you make plots in ggplot2 is very different from base graphics making the learning curve steep. So leave what you know about base graphics behind and follow along. You are just 5…

Statistical analysis and data mining were the top skills that got people hired in 2014 based on LinkedIn analysis of 330 million LinkedIn member profiles. We live in an increasingly data-driven world, and businesses are aggressively hiring experts in data storage, retrieval, and analysis. Across the globe, statistics and data analysis skills were highly valued. In the US, India, and France, those skills are in particularly high demand.

What is data science?

Data scientist…

Buzz words are one of my least favorite things, but as buzz words go, I can appreciate the term “Data Lake.” It is one of the few buzz words that communicates a meaning very close to its intended definition. As you might imagine, with the advent of large scale data processing, there would be a need to name the location where lots of data resides, ergo, data lake. I personally prefer to call it a series of redundant commodity servers with Direct-Attached Storage, or hyperscale computing with…