Data science is booming, but there are some things you need to know before making the shift.

Are you thinking of switching to data science as a career option? Did the flood of articles about data science bring the next big thing change your mind? Then this article is for you. Data science is an interesting and challenging field. It involves working with advanced tools and requires life-long learning to be updated with new trends. Are you ready for that?

Due to a shortage of skilled data scientists, companies are actively hiring more professionals. And rightfully so, because the amount of data produced in our daily lives is so vast that it can't be dealt with by a single person. So if you are an IT professional, an admin, or similar, here's everything you need to know about switching your career to data science.

Data Science, But At What Cost?

The shortage is definitely an advantage and your experience in the IT field will yield some results but let's not forget the fact that you will have to juggle your existing job with data science classes in order to get trained. And after you are trained with the necessary skills that employers are looking for, the starting salary might be less than what you were earning in your previous job. But that's just a phase. The future prospects in data science are brighter now than ever.

Is On-The-Job Training Practical?

The odds of this working out are very slim. Unless you're hired in a way with a decently sized data science group, the company might give you a few months of training but that's not a norm with every company. Currently, employers hiring data scientists are looking for people from an accredited university. So is the case with MOOCs. Reading free material off the internet can be a motivating way to train yourself, but in an interview, the candidate with a formal degree will have the upper hand. There are no crash courses for beginner-level data scientists, you need to think long-term, about 18-24 months. So, what's the takeaway? Get the necessary certification from a renowned institution.

Is Bachelors Enough? Or Should You Get A Masters Degree?

MS in data science is the most preferred choice. That will take about two years of education and some work experience. Can this be done through distance learning? Yes, if you can commit to it. But will a bachelor's degree do? If an institution is offering the same set of knowledge through a bachelor's degree, there's no reason to not consider it. All you have to do is make sure the curriculum is specific to data science and doesn't focus broadly on computer science or data analytics.

How To Choose The Right School?

Because you're thinking about a mid-career switch, consider where you want to live first. Chances are, there are data science jobs around you. That's how you should decide what school to go to. Unless you have the chance to go to a prestigious university, choose the institution that's closer to where you want to live and work your way through the alumni network to secure an opportunity.

Did You Pick Your Specialization?

The demands from various industries created specializations within data science that you must consider. Pick an industry you'd want to work in and select the specialization accordingly. While the techniques remain the same, the way they are applied differs. 90% of the work in data science is about predicting consumer behavior, so there might be a possibility that you can go ahead with data science in your current field, whether it's security or hospitality.

Also, if you're thinking about how the interview for a data science job will be like, here are the popular interview questions.

These are the things you should know while making a mid-career switch to data science. While the field is booming and companies are aggressively hiring, refer to their job requirements and make decisions about the courses. If you are going to scale up in your existing field, you will have an advantage over fresh graduates, if you want to change your field altogether, it's a process that will yield results in the future. Good luck!