Data visualization plays a very important role in communicating results to executives. You may get all the fancy formulas and other mechanics to get the results, but executives want to see results that are meaningful to them. Based on the raw data you get from a variety of sources, you can get some statistical properties and the analysis you've made, and then end up with a truly meaningful and very accurate visual form. When the data enters the visual form, these insights are easily attracted, and obviously when we gain insight, it is faster to take action.
Data visualization pipeline
So now, the entire pipeline is easy to build and the process on that pipeline is fully automated. From the data inside the data platform to the location of the drawing or visualization chart, the entire process is fully automated. The main reason we can automate this is because the various skill sets working on this pipeline, such as data engineers, data architects who ensure data pipelines and data flow through the platform, and people like statisticians, make these visual instruments Like the board's data analysts, there are some business people who are taking some action and insights from the information they get from these visualizations.
Machine learning using R
New changes in the field of data science have not caught people's attention because it can build predictive algorithms. Algorithms can get data from the past and do things for the future. In the case of machine learning, this analysis takes another form. We call it predictive analysis. They are part of many of the necessary skills around statistical thinking. These are necessary to understand the data and are very good at writing and understanding algorithms. So when you combine the knowledge of statistics, mathematics, and computer science, it can help you create algorithms. Therefore, it is closely related to computer statistics and algorithms from the world of computer science.
Geeks get what they deserve
There are many use cases for machine learning, and these use cases have spread to data science. This area is now gaining the trust it deserves. In the early days of machine learning, people in these fields were once considered a geek because these are very niche areas. There is not much content about these areas, but now everyone is able to do machine learning. Even developers from a full-code background can create machine learning models by calling a certain number of APIs.
As a result, data science has moved from simple digital computing to visualization and machine learning.