Data Science

Data is the new electricity, we're living in the age of the fourth industrial revolution, the need for Data Analysis is create each day and has risen tremendously in the last decade. A Data Scientist is like a sculptor, and Cyber Radar University provides you the valuable in-formations.

Course Overview

Have you ever wondered, how does Facebook automatically tags the faces of an individual, predicting flight delay? How does a website like Netflix recommend the videos, How do banks identify which customers are likely to be the most loyal, and which are likely to leave for a competitor? Data Science can be defined as a blend of mathematics, business acumen, tools, algorithms, and machine learning techniques that can be of major use in the formation of big business decisions.

In the Data Science online course, one deals with both structured as well as unstructured data. It’s an amalgamation of statistics, tools, and business knowledge. You might be thinking, why should you enroll in a Data Science course? As you know, with the amount of data that is being generated and the evolution in the field of Analytics, it has turned out to be essential for industries.

What will You Achieve

Develop skills for real-career growth

Learn from experts active in their field

Learn by working on real-world problem

24x7 active support

How Data Science will Create more Jobs in the Future

Data Scientists are needed in virtually every job-sector - not just in technology, in order to break into this high-paying, in-demand role - an advanced education is generally required. Cyber Radar University comes up with the tendencies. Undoubtedly, experts are highly educated - it’s an evolving field and Python has become a required skill for 46-percent of jobs in Data Science.

Data Science Explained

AI, data science, and machine learning all work in tandem. Machine learning is the field of data science that feeds computers huge amounts of data so they can learn to make insightful decisions similar to the way that humans do.

For example, most humans learn as children what a flower is without thinking about it. However, the human brain achieves that learning through experience—by collecting data—on which specific features are associated with flowers.

A machine can do the same thing with human help. As humans feed the machine massive quantities of data, it can learn that various petals, stems, and other features are all connected to flowers.

In other words, humans feed training data or raw data to the machine, so it can learn all of the data's associated features. Then, if the training was successful, testing with new data should reveal that the machine can distinguish the features it learned. If not, it needs more or better training.