There are many reasons why a career in data science is attractive. For one, it’s an industry with high salaries, even when compared to other salaries in tech. It also provides plenty of opportunities to advance and to get into executive roles, if that’s your cup of tea. Most importantly, it is an exciting field that almost never devolves into “working in the mines.”

The shortage of data science talent is dramatic, but there are still a few mistakes you can make getting your foot in the door. These are the types of mistakes that can slow down your initial career progress, so we’re going to cover them in this article to help you make sure you’ll avoid them.

Underestimating Formal Education

One of the most noticeable effects of the chronic shortage of talent in tech is the practice of hiring people with little or no formal education, especially for junior roles. With some serious commitment, someone with no computer science education can learn the basics of one or two web development languages in six months and, more likely than not, they’ll be able to land an entry-level job.

Things are different with data science. According to a Burning Glass report, more than 40% of data science job ads require a master’s degree or higher. The good news is that more and more colleges with data science programs offer online courses, making them more available.

In addition to these, Flatiron School, General Assembly, and BrainStation all offer online bootcamps focused on data science. (Keep in mind that some of these also require certain knowledge of statistics and applied mathematics.)

Of course, you can still choose to go self-taught. You might make it, but it will be more difficult.

Neglecting Practical Work

Formal education is more important in data science than in most other tech fields, but that still doesn’t mean you can neglect practical work. There are different things you might do to build your data science portfolio, even before your first actual job or gig.

Personal projects can be a great way not only to put your skills to use but also to show off your thinking and approach to data problems. The good news is that there is no shortage of free datasets out there. It’s really up to your imagination and interests as to how you can put them to good use.

GitHub, StackOverflow, and Kaggle are just some of the platforms where you can showcase your projects. Getting involved in discussions can also be a great way to show what you know.

This article from freeCodeCamp is a great guide that you can check to find more ideas to build your data science portfolio.

If you can afford it, you can also look for internships – paid would be perfect, but sometimes unpaid will have to do. Either way, internships will provide you with an opportunity to learn from people who have been in the industry longer than you and to really test yourself.

Lack of Focus

I don’t mean the go-getter, let’s-do-it kind of focus. I’m talking about the need to find your niche in the (let’s face it) complex field that is data science.

For one, not all data science jobs are the same. The skills you will need for the different jobs will vary, as will your career path. For example, are you gunning for a data analyst career? Perhaps you want to be a data engineer? Perhaps you’re looking for a career in machine learning?

The best thing to do is to research these (and many other) jobs in the field. Find your focus. The most in-demand skills will mostly stay the same (at least the core ones) but the emphasis may shift depending on the job you have in mind.

It’s almost as important to think long and hard about the different industries that hire data scientists.

For example, a data scientist in retail and a data scientist in healthcare will find themselves in quite different environments with different requirements. According to Sunichal Dev, a data scientist from Noodle, domain and industry knowledge can be just as important as technical skills. Once again, you might want to focus your efforts.

this article has been retrieved from https://insidebigdata.com/2021/03/13/mistakes-to-avoid-when-starting-a-career-in-data-science/