What Does It Take to Become a Data Scientist?

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There is a lot of buzz online right now about how data science is amongst the best career options around. That isn’t entirely untrue; data scientist roles exist in almost every industry and company worldwide. The salary level is generally very high. The positions are creative, challenging, and ever-changing, which can be a lot of fun. It’s a flourishing community and one of which many are excited to be a part of.

But before you go out and dedicate yourself, you must be aware that there are downsides, too. You don’t want to score your dream job and then be in for a nasty surprise! Data science is a great career path, but before you commit to the role entirely, be aware of these four challenges.

 Day-to-day work isn’t always a walk in the park

Yes, there are a lot of complex algorithms, fascinating data, and other amazing insights involved in data science. You shouldn’t expect this every day of the week, though. Data science, as invigorating as it can be, is a job like any other!

Junior data scientists often have to perform more mundane tasks, which can weigh on them and make them feel cheated of such an exciting opportunity. A prime example is infrastructures and strategies. Several companies don’t have any such plan in place before you even get hired: which means it falls to the new hire to do all of that.

Some companies, too, only want data scientists to help make business decisions. That means no revolutions there. You must adapt to your environment: and your reality.

  1. You may have to work alone

Since many executives still don’t really understand the importance of data science, it can end up being a lot of work for one person or a small team to handle.

Data science doesn’t exist in a vacuum. However, you will still be expected to create value from almost nothing, perhaps by yourself. It’s essential to collaborate with and stay in communication with other teams throughout the company.

Isolation also increases pressure – even if you do have a small team, they’ll likely be separate from the rest of the business. With high expectations, low time, and limited understanding or help, it can all feel very frustrating.

This isn’t always the case – but it is a reality you have to keep in mind.

  1. If it’s even a little related to data, it’s likely your job now

You might know a lot, but you don’t know everything – and you can’t be expected to. Nonetheless, anything to do with the web or online information is likely to get routed in your direction by inexperienced employers. Analytics reports, sales reports, upgrades: a lot of things that relate to your knowledge base, but don’t have much to do with how you trained!

Some companies will expect you to have answers to every problem, too. Whether it’s increasing customer intake or judging how sales are processed, a lot of it will be put to you. If only it worked that way!

You can help create tools to reach out to the right people, but a reality of the job is one of your tasks is saying you don’t know everything.

  1. Data science competitions don’t count as experience

Data science competitions are great for networking, skill-building, and even job hunting. Still, in terms of real-time experience, they don’t really matter. They are beneficial, but competitive scenarios are very rarely like those you’ll experience in the real world. Real-world challenges are much less tidy and suffer from chaos, sometimes-conflicting sources, and other issues that involve a lot of tidying up!

As well, the real world requires a lot of contexts that will completely change your approach.


This might sound disheartening, but it isn’t about discouraging people from data science. It is a fascinating field, but the objective of this article is to let people approach it with both eyes open. Follow your passions, but make sure you know the good and the bad before you commit to it!