As of 2021, there were 93000 vacant positions for data science experts in India alone. No wonder you want to be a part of the data science industry. It is actually a pretty good career choice given the global demand for skilled data science professionals and the acute lack of employability among a large portion of the certified data science graduates.

Data science on the ground can be very different from what it seems like in the classroom. Data scientist had been the #1 job in America for 4 years straight starting from 2016. The buzz and the craze built around the profession is quite genuine and legitimate. The only problem is that a lot of people approach this discipline from an angle which is somewhat misled.

There are things that you must understand, important things that might make or break your career, before opting for a data scientist course. We are going to talk about some such issues. In the course of this short article we will understand some truths, bust some myths, and get very real with the world of data science.

You do not become a data scientist by completing a data science course

The people running after their mentors and guides, pulling all-nighters, making sacrifices every day to be called a scientist at some point, would be incredibly frustrated if you went for a six month data science course and were called a data scientist at the end of it.

When you undergo a data science course in Bangalore, or any other city for that matter, you learn some concepts, gather some skills, and gain some practical experience which would help you plant your first step in the world of data science. You would become a data science professional.

If you manage to join a large team, you would probably be stuck with data cleansing, and preparing for a while, before you work on your first predictive model or your first deep learning project.

Becoming a data scientist is a game of patience and perseverance. If you can stick in there and learn as much as possible as fast as possible, you will be able to call yourself a data scientist eventually.

You do not need a PhD to become a data scientist

The previous point might lead some readers to believe that they can only call themselves data scientists after getting a doctoral degree. So, it is better to make a clarification here.

There are two different aspects to the role of a data scientist.

One is an applied data scientist and the other is a data science researcher. In applied data science, the practitioners use existing algorithms with certain modifications to find answers specific to their domain or the business they are serving. You do not need a PhD to fit into such roles. In fact, most of the job opportunities that you would find as a data scientist are actually for the applied role.

A data science researcher creates algorithms from scratch, writes scientific papers, and builds up the knowledge base. They might need a PhD. Even then it is not an absolute necessity.

Your previous work experience may not translate when you make the switch to data science

The Harvard Business Review has made the discipline of data science sound awesome and tempting, and do not get me wrong, it is all of those things. Remember, we are here to talk about things that are not common knowledge. We want to answer questions that really matter.

The question here is whether or not your past work experience translates as a whole when you make a switch.

The answer is no in most of the cases. Actually this problem too has two different facets. If you make a switch into data science while staying in the same domain where you have racked up a 5 year experience, you will be valued as an asset because even though you would be new to the field of data science, you would be well aware of the domain, you would understand the data that is handed to you, it will be easier for you to make sense of it.

On the flip side, if you change your job role as well as your domain, some bad news might be waiting for you. For instance, the 5 years of experience that you have had in IT as a software tester won’t translate too well if you are going for a data science role in the finance sector.

You can become a data science professional without having quantitative background

While the majority of the data science professionals that you would come across have an engineering, mathematics, statistics, or computer science background, there are numerous cases where people from the “outside” came in and made it big in data science.

Does that mean the computer science graduate and the economics major start at the same point? Certainly not; the computer science guy would have a definitive edge initially owing to his grasp over programming languages and concepts around them.

It takes a lot of dedication and perseverance for a person from a non-STEM background to break through the discipline of data science, but it is far from impossible.

Soft skills play a bigger role in your success as a data science professional than you give it credit for

Imagine a person who is just marvelous with numbers. She knows what algorithm to use on what data. She knows exactly how much impact a certain change in the business processes could make. But even with all this information, she remains unheard, unfollowed, and ignored, because she fails to put her point across.

Soft skills like communication, story telling, problem solving, and decision making, are just as important as the technical skills for a data science professional.

Your work means very little unless it is acted upon. For a certain analysis to be acted upon, it needs to have a prominent impression on the executives. Strong soft skills will push you towards success.

Data science is not all about building predictive models

Data scientists predict the future by analyzing data. That wizardly ability is the center of all hype. Data science professionals do build predictive models that offer near accurate predictions based on data. But that is not what data science is all about.

The role of a data scientist involves a lot more than model building. There are numerous layers of tasks and responsibilities that a data science professional must partake in. For instance, a data scientist has to understand the problem statement, build a hypothesis, verify and clean data, conduct an exploratory analysis, and then prepare a model.

To conclude

The field of data science is full of opportunities for real enthusiasts. It is also a trap for those who just follow the hype and jump in without so much as a thought. You need to make sure that you know the field you are entering. You must not believe in myths and build your career around proven facts.

Data science as a discipline is evolving fast, however, there is something that would never change – the importance of persistence. Learn, practice, employ – keep this cycle going.