Tool differences between data science and analytics? can anybody bring out the key difference between these two subject ?

I think there any many difference and fundamentally there is a think line among-st these both ...apart from its defination

How does R shiny works? How can we integrate it with some dashboard and make it to productions? any tips any example to share?

The Shiny web framework is fundamentally about making it easy to wire up input values from a web page, making them easily available to you in R, and have the results of your R code be written as output values back out to the web page.

NLP and NLG how different are they? can we bring out some key difference and way it works..some examples?

NLGOnce a chatbot, smart device, or search function understands the language it’s “hearing,” it has to talk back to you in a way that you, in turn, will understand. That’s where NLG comes in. It takes data from a search result, for example, and turns it into understandable language. So whenever you ask your smart device, “What’s it like on I-93 right now?” it can answer almost exactly as another human would. It may say something like, “There is an accident at exit 36 that has created a 15-minute delay,” or “The road is clear.” NLG is used in chatbot technology, as well. In fact, chatbots have become so advanced; you may not even know you’re talking to a machine. Using NLP, NLG, and machine learning in chatbots frees up resources and allows companies to offer 24/7 customer service without having to staff a large department.

Which of these measures are used to analyze the central tendency of data?

Which is Best—the Mean, Median, or Mode? When you have a symmetrical distribution for continuous data, the mean, median, and mode are equal. In this case, analysts tend to use the mean because it includes all of the data in the calculations. However, if you have a skewed distribution, the median is often the best measure of central tendency. When you have ordinal data, the median or mode is usually the best choice. For categorical data, you have to use the mode.

Redundancy has a negative connotation when the duplication is unnecessary or is simply the result of poor planning.Length of test run is what drives you to want to act on this data. But we are seeing things covered redundantly. That is nearly always going, but when you can couple it to a single redundant test, you have an opportunity to delete that test to reduce the elapsed time of a test suite.

Informative one.

Static testing: During Static testing method, the code is not executed, and it is performed using the software documentation.Dynamic testing: To perform this testing the code is required to be in an executable form.

Data Marting involves hardware & software cost, network access cost, and time cost.

Partitioning is done for various reasons such as easy management, to assist backup recovery, to enhance performance.

DMQL is based on Structured Query Language (SQL).

Data Mining Query Language (DMQL) is used for Schema Definition.