Before you go, check out these stories!

0
Hackernoon logoA Roadmap For Becoming a Data Scientist by@the_jane

A Roadmap For Becoming a Data Scientist

Author profile picture

@the_janeJane

Tech freak who loves to write about the latest cutting edge technologies.

So you want to become a data scientist? You have heard so much about data science and want to know what all the hype is about? Well, you have come to the perfect place. The field of data science has evolved significantly in the past decade. Today there are multiple ways to jump into the field and become a data scientist. Not all of them need you to have a fancy degree either. So let’s get started!

What is a data scientist? What do you want to do?

The Definition of data science and data scientist varies from company to company these days. In my experience, there is a discrepancy in who the companies hire and what they want from them. Data science is a very broad field and being a data scientist does not mean you need to know and be capable of doing everything that comes under its banner.

My first advice to you would be to look into the different aspects of data science and see what catches your interest the most. You should also look into the job postings and industrial demands for them. Make your choice based on these factors. Once you have decided you should look into gaining more knowledge in that particular aspect of data science.

Is a degree necessary to be a data scientist?

This is a very commonly asked and tricky to answer, question. Going for higher education is a recommended choice, but not one that is feasible for everybody. In fact, it is not even a hard-coded requirement. Sure a degree might give you an advantage in getting your first job (key-word being might). But there are many individuals out there who have made successful careers as data scientists and analysts without any dedicated college degree. You might want to look into online courses that can help you learn. You should definitely check their reviews and success stories of people who took those courses, maybe even try to get in touch with them.

Networking is important

Once you have decided your interests in data science and maybe even have a dream job in your mind. You need to start chatting people up. Send mails to people working at your dream company, to people who have made careers in the field you are looking to join, ask them for advice. Most of them might not respond, but some of them will, and that is valuable advice. Whether you chose to go for a college degree or an online course. You need to keep updating your resume and social media profiles. LinkedIn is the professional social network, use it!

Choose a programming language!!

Python and R Programming are the most popular programming languages for data science. On one hand, R has been the leading language for statistics and data analysis for the past two decades. On the other hand, Python has rapidly become one of the most popular and fastest-growing programming languages in the last five years.

Math is your friend ;)

Statistics, regression models, graphical models, basic 2d and 3d geometry, matrices, distribution models and much more are used every day in data science. Brush up your basics and try to learn as much as you can. Without a good handle on the maths, you simply won’t make it far as a data scientist. I would recommend reading articles related to data science and your chosen field to know what exactly you need to learn. Listening to relevant podcasts can also be helpful. There are also many free online courses available that can help you with this.

Make a few projects (The most Important part)

Nothing showcases your skill and knowledge, like a project made by you. Don’t wait till you know enough for making a project. Just get started with whatever you know till now. This will show your knowledge till now, improve your understanding of the concepts and also give you the confidence and motivation to keep going.

Keep making projects that suit your current level. This way you can show your skill with basic concepts as well as the advanced ones. It will also show your progress. Try to avoid using other people’s code. They are your projects for a reason. You may have gotten the idea for the project from someone else, but the project itself should be a reflection of your approach and skill.

Contribute to open-source projects

There are many open-source projects out there that are constantly looking for good contributors. You can even find projects that are suitable for beginners and move up as you gain confidence.

This not only improves and showcases your skills. It also helps in making contacts and connections. Other contributors and maybe even the project leaders and owners might help you in getting your first job.

A leap of faith

By now you should have a few projects under your belt. You have a qualification (either a college degree or and online course certification). And you also have a few contacts that might be able to get you in the industry.

Go back to the people you contacted in the beginning. Show them how you have improved in this time and tell them how grateful you are for the advice they gave you. Sending your resume to as many places as you can, may land you a job but a face-to-face meeting is always the best.

Conclusion

Hard work and dedication always pays. Smart work can pay even more. This guide should definitely lead you to success as a data scientist and help you in landing your dream job. Even after you get a job and join the industry, don’t stop learning and don’t stop contributing to the community. Here is the gift for all the data science aspirants out there. You never know, a better opportunity might just be waiting for you around the corner.

Tags

The Noonification banner

Subscribe to get your daily round-up of top tech stories!