Jyoti Nigania

Hi,i am writing blogs for our platform House of Bots on Artificial Intelligence, Machine Learning, Chatbots, Automation etc after completing my MBA degree. ...

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Hi,i am writing blogs for our platform House of Bots on Artificial Intelligence, Machine Learning, Chatbots, Automation etc after completing my MBA degree.

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Know Why Data Science Moved Into Visualization and Machine Learning?

By Jyoti Nigania |Email | Dec 18, 2018 | 45537 Views

Data visualization plays a very important part when it comes to communicating the results to the executives. You may have all your fancy formulae and other mechanics at coming to the results, but the executives want to see the results which make sense to them. From the raw data that you have from the various sources, you can get out with certain statistical properties and the sort of analysis that you do and that can then finally be represented in a really meaningful and very precise visual form. And the moment the data comes to visual forms, the insights are very easy to draw and obviously when we have the insights, taking actions becomes even quicker.
The Data Visualization Pipeline
So now, this entire pipeline is easy to build and the processes on this entire pipeline are fully automated. The point from where you get the data inside your data platforms to the point where it goes to a plot or a visual chart, this entire process is completely automated. And the primary reason we are able to automate this is because of the various skill sets that work on this pipeline, like data engineer, the data architect who is making sure that the data pipeline and the data is flowing through the platform and there are people like statisticians, data analysts who are making these visual dashboards and also there are business folks who are taking some actions and insights out of the information that comes out of these visual charts.
Machine Learning Using R
What is new in the world of data science and was not in the limelight earlier because of its sophistication in being able to build predictive algorithms. Algorithms which can take data from the past and do things coming in the future. In the case of machine learning, analytics take another form. We call this predictive analysis. And they are some of the many required skills around the statistical ideas. These are needed to learn about the data and on top of it is very good at writing and understanding algorithms. So when you combine your knowledge of statistics, mathematics, and computer science in particular - it helps you create an algorithm. So, it is closely related to computational statistics as well as algorithms which come from the computer science world.
There have been a number of use cases for machine learning which have come in popularity of data science. This field has got its due credit now. People in these fields used to be treated as a sort of geek in the early days of machine learning, as these were very niche fields. There was not much content to learn about these domains, but nowadays everybody is able to do machine learning. Even a developer who is from a complete coding background is able to create a machine learning model by calling a certain number of APIs. So, Data Science has moved into visualization and Machine learning from mere number crunching.

Source: HOB