Choose the Right Role

There are a lot of varied roles in the data science industry. Data visualization expert, machine learning expert, data scientist, and data engineer are a few of the many roles that you could go into. Depending on your background and work experience, there are related important job roles in the data science industry.

But what if you are not clear about the differences or you are not sure what should you become? A few things I would suggest are:

Talk to people in the industry to figure out what each of the roles entails. Take mentorship from people; request them for a small amount of time and ask relevant questions. I’m sure no one would refuse to help a person in need!

Figure out what you want and what you are good at and choose the role that suits your field of study.

Take up a Course and Complete it

Now that you have decided on a role, the next logical thing for you is to put in dedicated effort to understand the role. This means not just going through the requirements of the role. The demand for data scientists is big so thousands of courses and studies are out there to hold your hand, you can learn whatever you want to. Finding material to learn from isn’t a hard call but learning it may become if you don’t put efforts.

What you can do is take up a MOOC which is freely available, or join an accreditation program which should take you through all the twists and turns the role entails. The choice of free vs paid is not the issue, the main objective should be whether the course clears your basics and brings you to a suitable level, from which you can push on further.

When you take up a course, go through it actively. Follow the coursework, assignments and all the discussions happening around the course. For example, if you want to be a machine learning engineer, you can take up Machine learning by Andrew Ng. Now you have to diligently follow all the course material provided in the course. This also means the assignments in the course, which are as important as going through the videos. 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 mentioned before, it is important for you to fully understand whichever topic you pursue. A difficult question you’re going to face is, Which language/tool should I choose?

Which would is the best option to start with? There are various guides and discussions on the internet that address this question. The basic idea is that you should start with the simplest language or the one with that you’re most familiar with. If you are not as well-versed with coding, you should stick to GUI-based tools for now. Then, as you get a grasp on the concepts, you can get your hands dirty with the coding part.

Focus on Practical Applications, Not Just Theory

While undergoing courses and training, you should focus on the practical applications of things you are learning. This will help you not only understand the concept but also give you a deeper sense on how it can be applied in reality.

A few tips you should do when following a course:

Make sure you do all the exercises and assignments to understand the applications. Join discussion forums and start asking and answering questions.

Work on a few open data sets and apply your learning.

Take a look at the solutions by people who have worked in the field and trends going on in the industry.

Network, but don’t waste too much time on it!

Initially, your entire focus should be on learning. Doing too many things at initial stage will eventually bring you up to a point where you’ll give up.

Gradually, once you have got a hang of the field, you can go on to attend industry events and conferences, popular meetups in your area, participate in hackathons in your area – even if you know only a little. You never know who, when and where will help you out!

Actually, a meetup is very advantageous when it comes down to making your mark in the data science community. You get to meet people in your area who work actively in the field, which provides you networking opportunities along with establishing a relationship with them will in turn help you advance your career heavily. A networking contact might: