Breaking into data science requires hard work, passion, and perseverance.

Not to mention, companies like Google, Facebook, or Amazon seek more than just technical skills. For instance, at Google, they look to hire talented candidates, and of course, great data scientists for that matter. It is difficult to find people with passion and talent today. However, let us explore some tips on how you can start preparing your data science journey at your dream company.

Take baby steps

As the old proverb says, it is better to take many small steps in the right direction rather than making a giant leap in the wrong direction and stumbling backward.

The proverb fits well, doesn’t it?

Well, you need to first acquire the relevant data science qualifications, you surely don’t want to miss a thing or two before being called for your first interview. This rule even fits perfectly great for someone coming to the data science field from a different background.

It is seen that many people tend to switch careers just because the field is trending. Biting off more than you can chew can be fatal at times. Evidently, they tend to get frustrated and disheartened that they leave the field itself. For instance, people get so excited they start learning deep learning right away, only to understand nothing. A small step toward the right and relevant direction is some basic instinct an aspiring data scientist needs to get it ingrained inside their heads.

Also, if you intend to get into tech giants like Amazon, Google, or Facebook, you need to be practical. Having said that, you need more than skills and experience to get into companies such as these. However, it is not impossible.

How you can do it? Ultimately, you can start by joining a startup. Remember, startups are one of the best platforms to gain experience, provided you know your stuff. Starting your career at a small company provides added advantage to your growth as a data scientist.

At a startup, you get the opportunity to handle projects, solve complex problems, talk to business stakeholders. What’s even more surprising is the opportunity they provide in experimenting with different things.

• Learning is supreme

“Nothing will work unless you do,” a famous quote by Maya Angelou.

Truly, nothing will ever work unless you make efforts toward it. For someone looking to grab a data scientist job at companies like Google or Facebook, you will need to do a lot of grunt work.

You might be in a situation where you feel you have failed, but this is not where you stop. You need to try your hands on as many data science projects you can, study new technologies, get yourself enrolled in the best data science certification program. Since data science is constantly evolving, you will need to come up with newer solutions to solve real-world problems. A person looking to get a job at Google will always be willing to put in extra hours of effort into learning what it takes to get there. Not to mention, data science certification programs and training provide a robust learning experience and a lot of practical work to get your hands working with the tools and techniques.

• Build your portfolio

Having a solid understanding of theory is excellent, but it doesn’t add any value if you’re unable to put in into practical use. Therefore, you need to find ways how you can prove you’re good at the job. Start creating codes and work on projects, use Kaggle if required. Grow your network, start participating in competitions and discussion forums. Don’t just stop here, start creating your GitHub profile, and participate in Kaggle competitions as well. Perhaps, you may not be able to add the links on your portfolio, but you can surely add the projects you’ve worked upon, mention the competitions you’ve participated, and discussion forums you’ve been a part of, etc. As mentioned above, professional certifications in data science help validate your skills. A certified data scientist can guarantee your ticket to the candidate’s success.

This credibility serves as an invaluable advantage to your portfolio.

• Don’t be too choosy or too greedy

Most often people get confused while getting offers from analytics companies. They feel they might miss a chance of becoming a data scientist.
A word to the wise, if you get the opportunity to begin your career in analytics or data, go ahead. You can always switch tracks once the data science opportunity presents itself. Being involved in the vicinity of data will only give you more work experience. Always treat your first job as a stepping stone. If you do land your first job in the data or analytics field, remember to do two things on a priority basis. First, lay a groundwork so that the internal shift in the data science team gets easier. Second, maintain a good relationship by showing interest in your job, once this is established, you might have a chance of pivoting into the data science team.

If your talent skews toward the data science job, then you will crack the interview. The rest depends on how you perform during the hiring process.