One of the ways physicians can gain some forewarning of impending heart failure is through the detection of excess fluid in the lungs, and MIT researchers have developed a new machine learning tool that could offer them a helping hand. The algorithm is able to detect severe cases of this condition with a high level of accuracy, and the researchers behind it are hopeful it could be adapted to assist with the management of other conditions, too. Learn more here: https://bit.ly/2HQaKR4

Dear team, I am working on a research based on content-based package selection in the telecom industry. So I want to get a data set based on customer usage of their package. I mean internet usage, voice usage etc. Can you please give me a support. Anyone have that type of a data set, can you please share with me.

I have a Data Cloud Lab and Github too, Ready to contribute someone like you. please send me your resume at darrajit@gmail.com. I will find out your solution. Thank you

While practical benefits of the Real-time data analytics over historic data analytics are obvious, it is also essential to understand and cater for its risks, challenges and limitations.

Agree, with the time researchers has found various tools and techniques that today one can do real time data analysis, but again it depends on the person, how (s)he will be using the tools and techniques with respect to the creativity. As to achieve there could be multiple ways, to use right weights & parameters are the most effective part of it.Thanks for sharing this valuable information.

Many times, I have seen data science professionals who know very well what is p-values but they struggle to explain it. I have also seen many professionals who don’t want to get into technical details but want to know more and understand why it is so important.

Gulp is used by software development teams as the streaming build system with which a number of tasks can be programmed and automated in an effective way.

This article is very informative and the steps which are been soon can be easily approcable .A lit more effective and efficient way the author tries to summarise the step here.

Dear colleagues,I publlished an article with the above title in the online newsletter of the Society of Petroleum Engineers. This is the latest in the series of such short pieces to explain machine learning foundational principles to petroleum engineers and to encourage its application.I thought it might equally be of benefit to someone here.The link to the article is here: https://pubs.spe.org/en/twa/twa-article-detail/?art=7313I will appreciate your feedback.

That's a good article and very well explained.I was using EML without realizing it... in form of Random Forest Algorithm.

It is really very excellent and eye-catching website. Thanks Nr. Chris for your kind gestures and support to grant me full membership status. I will try my level best to do some extraordinary things for Data Science. Inshaa Allah (God Willing).

The Next Generation Digital Learning Environment (NGDLE) is considered as a ecosystem - a learning situation comprising of learning apparatuses and segments that stick to normal guidelines. While the customary Learning Management System (LMS) gives regulatory capacities, the NGDLE is planned to straightforwardly bolster learning.

What is the correlation between Data Science and Econometrics.I have been thinking to find out if Data Science can stand alone without Econometrics. Will it rather be wise to say Econometrics forms a better modelling blocks on which Data Science thrive. I will like to know the correlation between this two fields. Thanks

While working on various dataset to train a Machine Learning model. What is it, that you look for? What is the most important part of the EDA phase? There are certain things which, if they are not done in the EDA phase, can affect further statistical / Machine Learning modelling. One of the answers is to, find “Outliers”.

One more thing which i missed in the article, that presence of outliers affects the distribution of the data. Let's examine what can happen to a data set with outliers. For the sample data set: 1, 1, 2, 2, 2, 2, 3, 3, 3, 4, 4 We find the following: mean, median, mode, and standard deviation Mean = 2.58 Median = 2.5 Mode = 2 Standard Deviation = 1.08 If we add an outlier to the data set: 1, 1, 2, 2, 2, 2, 3, 3, 3, 4, 4, 400 The new values of our statistics are: Mean = 35.38 Median = 2.5 Mode = 2 Standard Deviation = 114.74 It can be seen that having outliers often has a significant effect on the mean and standard deviation and hence affecting the distribution. We must take steps to remove outliers from our data sets. One of the easiest way to handle this is log transformation.Hope this information will help you.