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How to Become Data Scientist – A Full Guide

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How to Become Data Scientist – A Full Guide

The data scientist is the “Sexiest Work of the 21st Century,” according to the Harvard Business Review. Is this insufficient to read more about data science! In the field of computer space, as companies struggle with petabytes and exabytes of data, the age of big data arose. It was really difficult for data management industries until 2010.

How to Become Data Scientist – A Full Guide

Now that the common frames such as Hadoop and others have solved the storage dilemma, the emphasis is on data care. And data science has a huge part to play here. The development of data science has been extended in different directions today and one can also be ready for the future by understanding what data science is and how we can bring value to it.

What is Data Science?

The first question emerges, then, now What is data science? “Data science means different things, but data science uses data as its heart to address questions. This description is moderately broad since data science must be said to be a moderately large region!

Data science is the science of analyzing raw data using statistics and machine learning techniques with the purpose of drawing conclusions about that information.

So briefly it can be said that Data Science involves:

  • Statistics, computer science, mathematics
  • Data cleaning and formatting
  • Data visualization

It is now common knowledge that the data science is so successful. Now the questions arise: Why do you know about the data science? How do you begin? Where do I begin? What should be the subjects? and so on. Do you have to study all the principles in a book, or do you go online with some classes, or do you practice data science from some projects? So we will cover all these things in depth in this post.

Why Data Science?  (Decide the first objective?)

So you should have a simple aim in your mind before going through the full road map of data science that why do you want to study data science? Is it for the 21st century’s sexiest job?” Is it research projects with the university? Or is it your occupation for a long time? Or do you want the world of data scientists to change your career?

Make a simple target first. Why would you like to learn the science of data? For example, if you want to study data science for your academic college assignments, you just need to learn the first thing about data science. Likewise, you can learn technical and specialized stuff if you want to develop your long-term career. All the preconditions must be discussed in depth. And it’s up to you and it’s your choice to practice data science.

How to Learn Data Science?

Usually, data scientists come from various educational and work experience backgrounds, most should be proficient in, or in an ideal case be masters in four key areas.

  1. Domain Knowledge
  2. Math Skills
  3. Computer Science
  4. Communication Skill

Information of the domain

Most people consider that domain knowledge is not important but very important in data science. Take an example: if you wish to be a data scientist in the banking industry and have even more insight about the banking business, such as stock trading, financial expertise, etc., this will be very good for you and the bank itself will give these types of applicants more priority than a regular applicant.

The skill in math

These three points are incredibly significant, as they help us understand the different machine learning algorithms that play an important part in data science. Similarly, it is also important to consider statistics, as it is a part of the study of results. Probability is also essential for statistics and a criterion for machine learning.

Computer Science

There is much more to learn in computer science. But when it comes to the programming language one of the major questions arises is: 

Python or R for Data Science?

There are various reasons to choose which language for Data Science as both have a rich set of libraries to implement the complex machine learning algorithm, visualization, data cleaning. Please refer to R vs Python in Data Science to know more about this.

But my recommendation is one must have knowledge of both the programming language to become a successful data scientist.

Apart from the programming language the other computer science skills you have to learn are:

  • Basics of Data Structure and Algorithm
  • SQL
  • MongoDB
  • Linux
  • Git
  • Distributed Computing
  • Machine Learning and Deep Learning, etc.

Capacity to interact

It encompasses both the correspondence in writing and orally. In a data science study, the project must be conveyed to others after taking conclusions from the research. This can also be a note that you submit at work to your manager or staff. It might be a blog post at days. This will also be a speech to a party of fellow Members.

Nevertheless, a sort of communication of projects’ results is still part of a data science project. So communication skills are required to become a data scientist.

Tools for Studying

There are plenty of websites and videos for people to try and understand all the principles online and it is overwhelming to someone. At first, when you start, don’t be scared and pause to learn if you are confused by too many concepts. Have patience, discover, and keep active.

Some useful learning resource links available at GeeksforGeeks:

A Roadmap to Learn

Starting with the Data Science Summary. Read several blogs related to data science and even research stuff related to data science. For starters, read blogs on Data Science Introductions, Why chose data science as the profession, industries which are benefiting most from data science, top-10 data science skills for studying in 2020, etc.

Get excited to study data science and develop some fantastic data science ventures. Do this daily and think about data science one by one. Before you start your trip, it would be really best to attend any workshops or lectures on data science. Get your target clear and move on to your objective.

How to Become Data Scientist – A Full Guide

Rajat Singh
Rajat Singh
Rajat Singh is the Editor-in-chief at Bioinformatics India, he is a Master's in Bioinformatics and validates all the data present on this website. Independent of his academic qualifications he is a marketing geek and loves to explore trends in SEO, Keyword research, Web design & UI/UX improvement.

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