What differentiates the best data scientists from others? It is their focus on application of data science. The best data scientists I know of, see data science and its application every where they look. They look at this world as an outcome of flow of data and information. On the other hand, most beginners often ask the question – how do we apply our learning on real life problems? In this post (and another one following this), I have picked up a real life dataset (Stock Markets in India) and showed how I would use this data to come out with useful insights. I hope that you will find this useful. The idea is show the vast opportunities present in data science in a simple yet powerful manner. If you can think of more examples like this – let me know in comments below! For the best results, I would strongly recommend to build the application yourself as you follow the tutorial.

MLaaS is neither new nor rocket science or an unknown service. In today’s time there are hundreds of companies in this domain which are working as a service provider of MLaaS (SPMLaaS). Machine learning is into so many services and applications as on date and we may not even aware of them or most of them. In the area of FinTech, Medical, Law and almost every service which needs/has repeated actions/steps every time has made use of it as a service knowingly or unknowingly. Feature engineering as an essential to applied machine learning. Using domain knowledge to strengthen your predictive model or prescriptive model out of prediction can be both difficult and expensive. To help fill the information gap on feature engineering, MLaaS hands-on can help and support beginning-to-intermediate data scientists how to work with this widely practiced phenomena.

Objective: To obtain an atomic classification based on clustering techniques using non-supervised learning algorithms.
Design: The sample of atoms used in the experiments is defined using a set of atomic elements with known properties that are not null for all the individuals of the sample. Different clustering algorithms are used to establish relationships between the elements, getting as result a cluster of atoms related with each other by the numerical values of some of their structural properties.
Results:Sets of elements related with the atom that represents each cluster.

In this article I am going to talk about the non-parametric techniques used for survival analysis. To comprehend this article effectively, you’ll need basic understanding of probability, statistics and R. If you have any questions regarding the concept or the code, feel free to comment, I’ll be more than happy to get back to you. Survival analysis is a set of methods to analyze the ‘time to occurrence’ of an event. The response is often referred to as a failure time, survival time, or event time. These methods are widely used in clinical experiments to analyze the ‘time to death’, but nowadays these methods are being used to predict the ‘when’ and ‘why’ of customer churn or employee turnover as well.

If your IT help desk is struggling to meet employee demands, one common knee-jerk reaction is to assume you don’t have the proper staffing levels or skill sets. But often, the problems you’re IT department might be experiencing have nothing to do with your employees. Instead, the problem could very well lie in the technologies the help desk uses to support end users. Today, we’re going to identify three promising technologies that you can implement today that can greatly improve the IT help desk experience.

This is the first of 3 posts to cover imputing missing values in Python using Pandas. The slowest-moving of the series (out of necessity), this first installment lays out the task and data at the risk of boring you. The next 2 posts cover group- and regression-based imputation.

We review the application of new age Machine Learning algorithms for better Customer Analytics in Lending and Credit Risk Assessment.

In this series I want to explore some introductory concepts from statistics that may occur helpful for those learning machine learning or refreshing their knowledge. Those topics lie at the heart of data science and arise regularly on a rich and diverse set of topics. It is always good to go through the basics again?—?this way we may discover new knowledge which was previously hidden from us, so let’s go on. The first part will introduce fundamentals of probability theory.

We’re excited to announce community.rstudio.com, a new site for discussions about RStudio, the tidyverse, and friends.

Throughout this blog series, we’ve focused on some of the big features we added in RStudio 1.1. It’s not just the big things that matter, though; it’s sometimes the little ones that make the most difference in your day-to-day work. Towards that end, we spent a chunk of time during RStudio 1.1’s development implementing small but significant improvements to the core IDE features you use every day. Many of these were based on requests and ideas from the R community – we’re very thankful for everyone’s input and perspective!