An Overview on Data Science

So before we get into what is data science let us first understand what is data actually, and how it is important for business, e-Commerce, for security, for identity of someone, even for scientific purpose or research and for even much more.

So data is nothing but a piece of information , the information that we are collecting could be anything it can be your date of birth, your body weight, your eyes or hair colour, your meal list, what you are searching in your mobile or computer, the places you visit, so we can say anything around you either connected to you or around you can be data. 

But if someone is novice he will ask, how all these things can be data ? Answer is Data is everywhere but what type of data is our need and which type of data is not our need makes the all difference.

Lets understand this clearly through an example- Suppose you want to do some shopping on Amazon, and you decided to buy a new mobile phone, you fixed the budget, then features that you want in the that you are looking for, also battery performance, camera quality, style but unfortunately you did not find a phone matching to your view point, so you decided to search another day, when you searched another day you find that Amazon recommending some phones based on your past search activities, and hopefully you find the type of phone that you are looking for. So how did Amazon find that the type of phone you are looking for ? Answer is when you searched for the phone and its specifications the amazon stored your search data and then it processed that data, and according to that it recommended you lots of phones of same specifications, so here data is the type of text that you have typed and what you are looking for.

What is Data Science ?

So basically data science is the science of data, in more details data science is a multidisciplinary subject which includes statistics, computer science, ML (machine Learning) and domain expertise to get knowledge  or insights of data. Though it is a multidisciplinary subject the end product of data science is to develop a data product.

Now if you are thinking what data product is, let me tell you by taking the same above example, so after when you tried to search for a phone of same specifications, according to your previous data amazon recommended you phones type you are looking for, so you can say that by taking your previous search data the e-commerce company changed that data into a data product which helped the user to get his product and by it company sold that product, so here we can that data product is nothing but a program used to solve problem.

If you are looking for definition then here it is :"Changing the data of a company into a product to solve a problem is called a data product"

since we have seen that data science is a combination of subjects, now lets take a close why these subjects are very important in the field of dat science and also what role they play,

Statistics

In data science statistics is very important because, you are dealing with a large size of data or Big Data, statistics helps to find mean, median, mode from the data and with these it helps the  data scientists to analyze and understand the data. Now there are three type statistics used in data science,

• Descriptive Statistics : This type of statistics helps the data scientists to consolidate or summarize the data for further analysis.
• Inferential Statistics : It helps to find the relationship between the samples of data collected.
• Regression Analysis : This helps to find the relationship between multiple variables. 

Machine Learning

ML helps the data scientists to solve the complex calculations, an to develop algorithms and predict a variable.

Domain Expertise

Domain expertise is nothing but the knowledge of data set. Suppose if the data set is related to healthcare the healthcare is the domain expertise, if the data set is related to science the domain expertise is science, if the data set is related to business then domain expertise is business etc.

Data Visualization is also a part of data science, it has its own field called data analytics, in which you have to represent the data in graphical forms to get insights from the data.