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How Do I Start a Career in Data Science Field?

No matter where we go, what we do, we are surrounded by huge data, and utilizing that data to grab our attention and interest has become the latest technique for businesses. And that is where they use Data Science. So as I said, it is everywhere so the chances of getting a job in this area are unlimited only if you are well versed in the tools, methods, and technologies in Data Science. This blog is a complete guide on how anyone can start a career in Data Science. 

Many of us must definitely have heard this Data Science as the hottest job and high-paid job, right?! Data Science has become tremendously popular in each and every industry. As a result, it’s no wonder that nearly everyone aspires to work in this profession. One more fact about Data Science is that despite your background, anyone can learn the skills by doing a Data Science Certification course and enter into this career. 

How Do I Start a Career in Data Science Field?
How Do I Start a Career in Data Science Field?

Becoming a data scientist is no longer a myth. Below is a comprehensive guide to help you reach that goal. You can also check out this Data Science course tutorial so get a head start in your career.

#What is the background and skill set?

This might be the first question that comes to mind whenever you think of a job. What background and skills do you need to get this job? Luckily, there is no such thing required to enter this profession. It does not matter whether you are a student or a working professional who may or may not be from a technical background. There are many people out there who are achieving greater heights each day as data scientists without any technical background. So do not hesitate to enter this career field if you don’t have previous experience, it is always worth trying. 

#What to learn?

And then comes the question of what to learn and where to start. Let us divide the skills you need to have to become a professional data scientist. 


Well, it is always a great start, to begin with, basics. As you may or may not know, Data Science involves knowledge in mathematics and concepts like statistics, probability, calculus, and linear algebra. All these are the basic concepts you must grasp. Data Science for Beginners blog will help you learn Data Science concepts from scratch. 

Programming Language

Then you need to start concentrating on the coding part. Here you may ask how I can do programming without any technical background. But my answer would be you can totally do it. Because in Data Science we use such programming languages that can be easily understood and learned. Mostly, all the data scientists or engineers prefer to choose Python or R programming language as they are easily accessible. Between these two, Python is quite easy to learn because of its readability feature. So, within a few weeks, you can learn Python. 

One thing you need to remember is that learning a programming language definitely requires practice. So the more you get your hands dirty the more you learn. 

Once you have learned how to do the basic coding using python, you must also learn its libraries. Because python libraries are crucial in each Data Science task such as data gathering, data mining, data visualization, etc. 

Machine Learning & Artificial Intelligence 

Understanding how ML works and how to write algorithms in it is essential in Data Science. Decision trees, logistic regression, deep learning are all the crucial concepts that are required in Data Science. These can help you build predictive models. And AI methods can help to gather the data and analyze it thoroughly. 

Data Science Concepts

Then you need to start learning concepts of Data Science such as data gathering, data preparation, data analysis, data cleansing, and data visualization. You have to know how all these works and how to do them. We should also understand which python library is best for which task. In this way, you will be able to use everything you have learned till now. 

#How to get a Job?

Once you have learned all the theory concepts and understood how to use them, here comes the next steps to get a job in this career. 


Projects are the best way to show the recruiters that you have experience in working on real-time projects. So after learning all the required theories, you should definitely work on projects. There are numerous project ideas available on the internet. Pick the right one in which you are interested. you must pick such projects which solve any real problems. 


No doubt certifications are validations of your skills and abilities. So do not compromise on getting certifications. Although, there is no use if you get certifications from any platform. You have to choose the right certifications that have industrial value. Here are the top certifications which might help you stand out from the crowd.

  • Microsoft Certified: Azure Data Scientist Associate.
  • Google Professional Data Engineer Certification. 
  • IBM Data Science Professional Certificate.
  • SAS Certified AI & Machine Learning Professional.
  • Cloudera Certified Professional (CCP) Data Engineer.


To develop a solid portfolio, you need to create a GitHub, Linkedin, or any website to provide examples of prior work samples or projects you have worked on. This is tangible proof of your Data Science abilities. Complementing your knowledge with a portfolio that demonstrates your skills can help you stand out from the crowd.

#What job should I apply for?

Data Science offers various career opportunities to its experts. If you have the skills to become a Data Science expert, then you will be eligible to apply for jobs such as Data Analyst, Data Engineers, Data Scientist, Data Architect, Database Administrator, Machine Learning Engineer. 

Although, if you are in the initial stage then apply for data analyst. As starting employment, data analyst roles might be simpler to come by and can serve as a wonderful springboard for a Data Science career. 


I hope this blog was insightful and clarified all your doubts regarding entering a career in the Data Science field. 

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  1. Thank you for the valuable information on the blog.I am not an expert in blog writing, but I am reading your content slightly, increasing my confidence in how to give the information properly. Your presentation was also good, and I understood the information easily.

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