Data Visualization is no longer an art. With evolving cognitive frameworks, multifaceted imaging, and intelligence; data visualization is exploring different horizons to perceive vast chunks of complex data. As a digital replacement for visual communication, Data Visualization has made it easier for companies to make decisions.

The state of things is transformed into data, which is convenient for electronic devices to store. On the contrary, the stored data is visualized, which is convenient for intuitive understanding of the process state of things.

Data has become an essential topic in the corporate world these days. Everyone needs to talk about the knowledge and quality that can be extracted from the results. There is a good explanation for this—data is among the essential tools available to today's advertisers, agencies, marketers, media organizations, and much more.

Dear All,Recently I have completed a course on the Internet of Things (IoT) by Cisco.Cisco Networking Academy transforms the lives of learners, educators, and communities through the power of technology, education, and career opportunities. Available to anyone, anywhere. I found it very useful.

Deep learning is a part of AI which was founded on artificial neural organizations, as neural organization will reflect the human thoughts. Deep learning which a subset of Machine Learning is is making revolutions in each field it comes into play. It decreases the human work and accomplishes the work precisely than people do. It emulates the human Brain with artificial neural organizations and dom

Thanks for the article, nicely explained along with covering the basic aspects.Also I was working on the dataset (from kaggle) on to predict covid through X-Rays, but seems using CNN or others not getting good score or prediction.After reading the article, seems got more details, and will try to implement in the EDA part and then train the model. Hope this should increase the prediction probability.

Have you come across Data Science jargon and buzzwords such as Artificial intelligence (AI); Machine Learning (ML); Deep Learning (DL); Neural Network (NN) etc.. want to know more about them and how each of them differ? If so then checkout this article, which will give you a brief idea what these are.

Brilliant work.Very well-written article. Yes, I have heard a lot about this "buzzword" around me and thinking it is the only "buzzword" you need to adopt and survive in the IT industry.Is it true?

The application of piecewise hazard model in mortality data becomes more useful in survival methods. This method is used to find the number and location cut points and to estimate the hazard model. The objective of this paper is to illustrate the piecewise constant hazard model and to find significant factors for under-five child mortality.

In this article, Data Security acts as an important segment. This consists of certain standard levels and various technologies that are used for data protection. This helps to secure data from accidental destruction and altering disclosures. Overall, in this article Aurora Serverless data security is majorly pointed. The AWS creates Aurora, which is nothing but a cloud-native execution of RDBMS. Finally, the implementation of Aurora is “serverless”.

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.