This article was written by Tavish Srivastava.

Overview

• Time Series Analysis and Time Series Modeling are powerful forecasting tools
• A prior knowledge of the statistical theory behind Time Series is useful before Time series Modeling
• ARMA and ARIMA…

To all Data Analysts big and small:

Many Corporates typically have Self service BI and DWH solutions ( I am asking only about those

who did NOT build an inhouse solution) : 

-When is programming needed in most leading Self Service configurations?

-When do analysts and Business executives require coding and programming when the Self…

Introduction:

Here’s a fun fact; An average human being (probably an adult) makes close to 30,000 conscious decisions every day. This isn’t entirely true though, in fact, I just made that number up. I could be right because if you think about it, how many decisions would you say you make on a day to day basis? Depending on who you are the above obviously varies widely and you know best. We all…

This the second part of Reinforcement Learning (Q-learning). If you would like to understand the RL, Q-learning, and key terms please read Part 1.

In this part, we will implement a simple example of Q learning using the R programming language from scratch. It is expected from you to understand the basics of R programming and complete the reading of Part 1 of this article.

Import libraries

We are coding the algorithms using the R base package…

Have you heard about AI learning to play computer games on their own and giving tough competitions to expert Human gamers?

A very popular example being Deepmind whose AlphaGo program defeated the South Korean Go world champion in 2016. Other than this there are other AI agents developed with the intent of playing Atari games like…

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…

A Video Game? In R Shiny?

For the last two years, RStudio has been organizing a competition to showcase the power and flexibility of Shiny as a framework for creating applications. Lately I’ve been devoting my career to making Shiny apps more beautiful, and…

Have you ever experience a situation where you want to import and combine hundred of datasets? If you do this manually, then it will take too much time. On the other hand, we can use a simple R programming to solve this problem in easily and quickly. Therefore, I would like to share two methods of combining all small files in a directory into a single dataset…

The Ultimate R Cheat Sheet simply put makes it easy to learn R. The Ultimate R Cheat Sheet saves you time by providing hyperlinks to the documentation and package-level cheat sheets for the most important packages in R. The latest update doubles its ultimateness by providing a second page that includes Special Topics:

• Time Series, Forecasting, and Financial…

The Ultimate R Cheatsheet

We are developing a revolutionary new system for teaching Business Analysis with R (Business Analysis with R is a new course we are developing at Business Science University). The system is revolutionary for a number of reasons (we’ll get to these in a minute). The cornerstone of our teaching process is the Data Science with R Workflow that was originally taught by Hadley Wickham and Garrett Grolemund in the excellent…

Matching and merging 2 files is task I find myself doing all of the time. Historically, I've used VLOOKUP in MS Excel and just worked around any limitations. Finally, I bit the bullet and wrote an R Function that does the trick faster, and with more flexibility. 

(Image added…

Python and R are the two most commonly used languages for data science today. They are both fully open source products and completely free to use and modify as required under the GNU public license.

But which one is better? And, more importantly, which one should you learn?

Both are widely used and are standard tools in the hands of every data scientist.

The answer may surprise you – because as a professional data scientist, you should be ready to deal with…

TensorFlow 1.x Deep Learning Cookbook

By Antonio Gulli, Amita Kapoor

Take the next step in implementing various common and not-so-common neural networks with Tensorflow 1.x

In this book, you will learn how to efficiently use TensorFlow, Google's open…

What is NLP?

Natural Language Processing (NLP) can be simply defined as teaching an algorithm to read and analyze human (natural) languages just like the human brain does, but a lot faster than a human could, more accurately and on very large amounts of data.

It is a great skill to have if you are an aspiring data scientist or data analyst because has…

Introduction

Did you know that you can write R and Python code within your T-SQL statements? Machine Learning Services   in SQLServer eliminates the need for data movement. Instead of transferring large and sensitive data over the network or losing accuracy with sample csv files, you can have your R/Python code execute within your database. Easily deploy your R/Python code with SQL stored procedures making them accessible in your…

R Deep Learning Essentials

By Joshua F. Wiley

Get everything you need to know to enter the world of deep learning when it comes to R with this book. Get started from the packages you need to have for your side,…