Tips to Successfully Start a Career in Data Science

It’s not easy to get started with data science; you’ll be facing a lot of questions, such as whether I should learn R or Python? What techniques should I focus on? Do I need to learn to code? And so on. This can be intimidating for almost anyone, but don’t you worry; in this article, I’ll provide you with some straightforward & simple tips that will set your path to learning data science. So, what are we waiting for? Let’s begin!

Choose the right role

Data science is a very vast field; there are plenty of different roles, such as data scientist, data engineer, data visualization expert, and so forth. Depending on your background and previous work experience, getting into one role would be easier for you than another. For instance, if you are a software engineer, it would be much easier to shift into data engineering. 

Until and unless you have a clear picture of what you want to become, you will stay confused about the path to take and skills to hone. So, what should you do in that case? Let me suggest a few things:

  • Find people who have worked in the industry or are still working, talk to them, and figure out what each of the roles entails. 
  • Take mentorship from people, request them to give you some of their time (even if very little), and ask relevant questions. I’m sure you’ll find plenty of people who would want to help a person in need!
  • Try to understand what you want and your strength clearly, then opt for the role that suits your field of study.

One more thing, avoid hastily jumping on to a role; first, figure out what the field requires and then prepare accordingly for it.

Take up a Course and Complete it.

Once you’ve chosen a role, the next logical step is to dedicate effort to understand it. According to a survey conducted by IBM, the number of jobs for all US data professionals will increase up to two million in 2020, the demand for data professionals is big, so hundreds of courses and studies (both free & paid) are out there to teach you whatever you want to learn. Finding material to learn isn’t an issue, but learning it may become if you don’t put in the required efforts.

As mentioned above, both free & paid courses and studies exist out there; it doesn’t matter which sort, of course, you go for, but what matters is that it should clear your basics and bring you to a suitable level, from which you can push on further.

When you opt for a course, actively follow the course material, assignments, and discussions around it. Only doing a course end to end will give you a clearer picture of the field.

Choose a Tool / Language and stick to it.

As I said previously, the best way forward in the data science field is to get an end-to-end experience of whichever topic you are pursuing. A pretty hard question you’ll face when getting hands-on is which language/tool you should opt for?

I think the most straightforward answer to this question would be to opt for any of the mainstream tools/languages there are and kick off your data science journey. After all, it’s the understanding of the concept which is more important; the tools are just means for implementation.

Some of you’ll be thinking that I’m trying to avoid answering the question; well, I’m not. If you go on the internet, you’ll find plenty of guides/discussions which address this particular question. The gist is to start with the most simple language or the one you are most familiar with. If your coding skills are not that good, you should prefer GUI (graphical user interface) based tools for now. As time passes and you start to grasp the concepts, you can get your hands-on with the coding part.

Join a peer group

Now that you’ve chosen a role and are getting prepared for it, what you should do next is to join a peer group. The reason why you should do this is that a peer group keeps you motivated. Taking up a new field alone is not easy, but the task gets a bit easier when you have other people alongside you, especially friends.

The best way to be in a peer group is to have a group of people you can physically interact with, but if that’s not possible, you can still join online forums such as StackExchange & Reddit, which can provide you with this kind of environment. 

Focus on practical applications and not just theory

As you go through the learning phase, you should start focusing on the practical applications of the knowledge you are gaining. Not only will this allow you to understand the basic concepts better, but it will also give you a deeper sense of how they would be applied in reality.

A few tips you should follow when undergoing a course:

  • Make sure you do not miss any of the exercises and assignments, as these will help you to understand the applications.
  • Take the knowledge you have and start working on a few open data sets. Even if you do not understand the math behind a technique initially, understand the assumptions, what it does, and how to interpret the results. You will always have time later to develop a deeper understanding.
  • Take a look at the solutions by the veterans of the field. They would be able to pinpoint you with the right approach faster.

Follow the right resources.

The courses and studies are not the only sources of knowledge; there are others too; one such is the blogs run by the most influential Data Scientists. These professionals are really active and update the followers on their findings and frequently post about the recent happenings in the field.

So, make it a habit to read these blogs every day. Although, there is one thing that you should make sure of, and that is to follow the right resources and not the wrong ones.

Work on your communication skills

You should always remember that no matter how technically profound you are, it does not automatically translate into you acing the interview. So, try this activity; make your friend or family with good communication skills hear your intro and ask for honest feedback. He will definitely show you the mirror!

Communication skills are important for passing the interview and are even more important when you are working in the data science field. To share your ideas with your peers or prove your point in a meeting, you should know how to communicate efficiently.

Thank you for reading!

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