The opportunities Data Analytics offers in this age of big data is endless. Data scientists are using data analytics techniques and tools to provide results to huge sets of data that many companies depend on to function. This has led to a huge demand for data scientists in the labour market. Many Millenials are looking to have careers in data analytics. But just like in other fields, a successful career in data analytics is marked by some specific rules. Analytics Vidhya, a data-focused website, provides a roadmap for starting a career in artificial intelligence.

See below some of what they touched on.

Choose the Right Role

There are a lot of varied roles in the data science industry. A data visualization expert, a machine learning expert, a data scientist, data engineer, etc are a few of the many roles that you could go into. Depending on your background and your work experience, getting into one role would be easier than another role. For example, if you a software developer, it would not be difficult for you to shift into data engineering. So, until and unless you are clear about what you want to become, you will stay confused about the path to take and skills to hone.

What to do, if you are not clear about the differences or you are not sure what should you become? I few things which 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 a 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 get an end-to-end experience of whichever topic you pursue. A difficult question which one faces in getting hands-on is which language/tool should you choose?

This would probably be the most asked question by beginners. The most straightforward answer would be to choose any of the mainstream tools/languages there is and start your data science journey. After all, tools are just a means for implementation; but understanding the concept is more important.

Still, the question remains, which would be a better option to start with? There are various guides/discussions on the internet which address this particular query. The gist is that start with the simplest of language or the one with which you are most familiar. If you are not as well versed with coding, you should prefer GUI based tools for now. Then as you get a grasp on the concepts, you can get your hands-on with the coding part.

Join a peer group

Now that you know which role you want to opt for and are getting prepared for it, the next important thing for you to do would be to join a peer group. Why is this important? This is because a peer group keeps you motivated. Taking up a new field may seem a bit daunting when you do it alone, but when you have friends who are alongside you, the task seems a bit easier.

The most preferable way to be in a peer group is to have a group of people you can physically interact with.  Otherwise, you can either have a bunch of people over the internet who share similar goals, such as joining a Massive online course and interacting with the batch mates.

Even if you don’t have this kind of peer group, you can still have a meaningful technical discussion over the internet. There are online forums that give you this kind of environment.

Focus on Practical Applications and not just Theory

While undergoing courses and training, you should focus on the practical applications of things you are learning. This would help you not only understand the concept but also give you a deeper sense of how it would 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.
  • Work on a few open data sets and apply your learning. Even if you don’t understand the math behind a technique initially, understand the assumptions, what it does, and how to interpret the results. You can always develop a deeper understanding at a later stage.
  • Take a look at the solutions by people who have worked in the field. They would be able to pinpoint you with the right approach faster.