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Data Science has become a hub of opportunities now a days. It is taking care of each domain of the industry whether it is IT, Electronics, Mechanical, Medical or research. Anyone from any background can go for data science today. Data Science is a combination of programming and mathematics. You don’t need to be an expert in mathematics, but you should know basics of it. These are the prerequisite that you must see before going into the detail of data science:

Linear Algebra
Statistics and Probability
Calculus (Partial Derivatives)
Programming (Any One)
R Programming
Mathematics is one of the most beautiful subjects in this world. If you are eager to learn these topics of mathematics and a little bit of any of the programming, then only you can go for Data Science. You might be learning mathematics from your school time, but did you ever thought about the real time use case of the topics that you learn in math. In data science you will understand the real meaning of math that how it is helping to make your software and applications more and more smart.

In this blog we are going to see the life cycle of a data scientist. Data Scientist job is considered as one of the most highly paid job in industry today. But there are lot of domains inside a data science life cycle. You could become any one of them or you could be a full fledge data scientist as well. So let’s see the life cycle of data science first :

Here, I am using very basic terms to make you understand the life cycle of data science. And you might find few different life cycles on internet as well. But the meaning is almost same everywhere. I have divided data science into 5 major parts. Let’s talk about each and every part of life cycle in detail :

Data Collection
This is the first phase of data science life cycle. Before doing anything the first thing you need is data. So how and from where you will get the dataset ?

Data Collection means to collect the data and being a data science engineer, it becomes your responsibility to gather data from different resources. And you should be aware of the different techniques to gather data.

Data could be available on any website or in database or a file or it could be an API.

Techniques of data collection :

So these are the few techniques which are used to gather data and you cannot rely on a single technique because data could be available on any website or it might be stored in database or some web services are providing you the dataset.

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