How to Begin Your Career in Data Science

Introduction

Learning data science can be intimidating. Especially so, when you are just starting your journey. That is why I thought that I would create this guide, which could help people starting in Analytics or Data Science. The idea was to create a simple and not very long guide which can set your way to learn data science. This guide would set a framework which can help you to determine data science through this challenging and intimidating period.

Just follow these useful tips, and you will get a good head start in your career.

Let’s get start with detail!!

1. Choose the right role

There are a lot of different roles in data science. A data visualization expert, a machine learning expert, a data scientist, data engineer, etc. These are a few of the many roles that you can go with it. Depending on your work experience and background, getting into one role would be more straightforward than another role. For example, if you are a software developer, it would not be awkward for you to change to a data engineering. So, unless you are sure and clear about what you want to become, you will stay unclear 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:

• Take mentorship from people request them for a small amount of time and ask questions that are relevant. I am sure no one would deny helping a person in need!
• Talk to people in the industry for figure out what each of the roles entails
• Figure out what you are good at and what you want and choose the role that suits your field of study.

But keep in mind that when taking a role: do not just hastily jump on to a position. You should first understand perfectly what is the field requires and prepare for it.

2. Take a Course and Complete it

Now that you have decided you're role, the next step is the logical thing for you is to put in dedicated energy to understand the role. It means not just going through the requirements of the role. The need for data scientists is significant, so thousands of Certified courses and studies are out there to keep 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.

When you take a course, go through it actively. Follow the coursework, assignments and all the discussions happening around the course. Now you need to have a clear understanding of all the material provided in the course. This also means the assignments in the course, which are as valuable as going through the videos. Only doing a course end to end will give you a more unobstructed view of the field.

3. Choose a Tool / Language and hold to it

As I mentioned before, it is essential for you to get the full experience of whichever course you pursue. A complicated question which one faces in getting hands-on is which language/tool should you choose?

This would plausibly be the most asked question by freshers. The most straight-forward answer would be to choose any of the important tool/languages there is and start your data science journey. After all, tools are just to implement; but understanding the concept is more important.

Still, the question remains, which will be a better way to start with? There are many guides and discussions on the internet which address this particular query. The gist is that start with the easiest of language or the one with which you are most familiar with. If you are not used to with coding, you should go for GUI based tools for now. Then as you get a grasp on the concepts, it will be easy for you to get the coding part.

4. Join a peer group

Now that you should know that which role you want to opt for and are getting processed for it, and the next great thing for you is to join a peer group. Why is that important? This is because a peer group will keep you updated. Diving into a new field may seem a bit daunting when you do it individually, but when you have friends with you and who is alongside you, the task seems a bit easier.

The better way to be in a peer group is to have a group of people you can interact with. Unless, you can either have a bunch of people over the internet who share similar goals, such as joining a Massive online course and communicating 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 which give you this kind of environment.

5. Use practical applications and not just theory

While supporting courses, 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 more profound 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 tasks and assignments to learn the applications.
• Work on a few open data sets and apply your learning. 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 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.

6. Follow the best resources

To never stop learning, you have to engulf every source of knowledge you can find. The most helpful source of this information is blogs run by most influential Data Scientists. These Data Scientists are update and active the followers on their findings and frequently post about the recent advancement in this field.

Make a habit to be updated by reading about data science every day with the recent happenings. But there may be various resources, influential data scientists to follow, and you have to be sure that you don’t follow the incorrect practices. So it is essential to follow the right resources.

7. Work on your Communication skills

People do not usually associate communication skills with a rejection of data science roles. They expect that if they are technically profound, they will ace the interview. This is a myth. Ever been rejected within an interview, where the interviewer said thank you for listening to your introduction?

Try this activity once; make your friend with good communication skills hear your intro and ask for honest feedback. He will show you the right way!

Communication skills are even more valuable when you are working in the field. To share your ideas with a colleague or to prove your point in a meeting, you should know how to communicate efficiently.

8. Network, but do not waste too much time on it!

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

Gradually, once you have got the 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!

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

•  Help you to have mentorship support
•  Help you search for a Job; this would either be tips on job hunting through leads or possible employment opportunities directly.
• Give you inside information of what’s happening in your field of interest

 End Notes

The need for data science is vast, and employers are investing significant time and money in Data Scientists. So taking the correct steps will lead to exponential growth. This guide provides tips that can get you started and help you to avoid some costly mistakes.