The most famous example is Google. If you search for "cricket" while logging in to Google's service, you will find that if you search for the same keyword without logging in to Google, you will see that the results will be a little different. Have you ever wondered why this happens? The reason is that Google knows a lot about your likes and dislikes. And accordingly he creates a personalized search result for you and presents it to you. Google does this through artificial intelligence which can be said to be a part of data science.Much of what you do, where you go, where you eat, what you shop at, what kind of shopping you do, can now be tracked and is being done. Let's talk about location. The smart phone in your hand is recording everything you do. You may not know or may be aware that Google is collecting this information from you with some additional benefits. Of course, Google has a business purpose and it is also making a lot easier for you.Google's machine is learning about you with this information. Knows you
This is an example. Big data is now being used in data science. Big data is a lot of big data. The bigger the data, the bigger the data that can't be analyzed on your computer. In other words, big data is the data that cannot be analyzed with a normal consumer level computer.Such data is now being created all the time. Because the flow of information has increased and with it the cost of computer storage has decreased. As a result, everyone is now generating data at a massive rate (such as web site clicks, site visits, tweets or Facebook status, etc.) and it is now easier to save that data at a lower cost.
However, there are exceptions. Such as the flight record of the plane which can record the value of thousands of parameters including the position, speed, wind pressure of the plane per second from which it is said that it is possible to make the flight more safe. But it is not easy to collect such data in real time. Because transferring gigabytes of data via satellite is hugely expensive. You can estimate it by comparing it with the data cost of your mobile.
One aspect of data science is machine learning. While machine learning is not an easy task to implement, it is quite simple. Let's talk about a baby. Think about how the child learns the language. If it is said that a child needs to be taught a language, not a child, then how do you implement it so that he can understand your language? Hard work. But this is machine learning. Machine learning is the way a machine learns.
To give an example - approximately 10 percent of patients who go to the ICU after a hospital operation (assume) die. Suppose your hospital has such a target patient information - the patient's age, type of disease, other clinical information. By learning from this data, can we create a statistical model that can determine the probability of a new patient dying?If this can be done then we can pay close attention to the patient in advance or take action. Here, using machine learning, ‘features’ or ‘factors’ are being extracted that are related to the death of the patient.
Popular examples of machine learning are Google Now or Ok Google and Apple's Siri Assistant. They can talk to you on your voice and answer small questions.
Another gift of machine learning we are going to get in the coming days is the self-driving car.
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