Let's take an example: Given a school result of a student, we tend to determine, he is either brilliant or not so brilliant. This is data science in everyday life.
But, does our prediction always come true? Obviously no, because a student can develop his liking in specialized field of study and do excellent. Same goes in the field of Data Science, some extra factors tends to change the future, which is different from what we have predicted.
Now, lets get technical. In real-world scenario, how data science is applied.
Let's say you got a job in banking sector and your manager ask you, why are the customers leaving the bank. What would you do? Here, comes data science.
Sample Pandas Data frame of Bank Customer
You would take all the past data of people (take a sample from millions of customer) and you would analyze the data, for what is common reason that customer are leaving and what is the difference between the Customer, who are staying and who are leaving.
Now, next step is, given data of present customer base, you will apply tools(technically algorithm,models)
to predict who are the customer, are likely to leave the bank. This is Churn-modelling, a famous example in the world of Data Science.
So, for now i will leave it up to you guys! Next time we will discuss about difference between Data Science, Machine Learning, Deep Learning and Artificial Intelligence.
See, you guys in the next post. Happy Learning!!😃
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