6 Effective Tips to Begin a Career in Data Science 

Data science can be an overwhelming domain. Many people will tell you that you can not become a data scientist until you master the following: statistics, linear algebra, computation, programming, databases, distributed computing, machine learning, visualization, experimental design, clustering, deep learning, natural language processing, and more. It’s just not true.

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

So, let’s start!

1. Choose the right role

There are many varied roles in the data science industry. An expert in data visualization, an expert in machine learning, a data specialist, a data engineer, etc. are just a few of the many roles you could play. Depending on your background and work experience, it will be easier for you to take on a role than another. For example, if you are a software developer, it will not be difficult for you to go into data engineering. So, until you are clear on what you want to become, you will remain confused as to the path to follow and the skills to perfect.

What if you are not clear about the differences or are you unsure of what you should become? I few things I would suggest are:

Talk to people in the industry to understand what each role involves

Take people mentoring – ask them for a small amount of time and ask relevant questions. I am sure that no one would refuse to help someone in need!

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

2. Choose a tool/language and stick to it

As I mentioned earlier, it is important for you to have an end-to-end experience of the topic you are pursuing. A difficult question that you have to face is which language/tool should you choose?

This would probably be the most asked question by beginners. The simplest answer would be to choose one of the main existing tools/languages and start your data science journey. After all, tools are only means of implementation; but understanding the concept is more important.

Still, the question remains, what would be a better option to start? There are various guides/discussions on the Internet that deal with this particular issue. The key is this beginning with the simplest language or one with which you are most familiar. If you are not as versed in coding, you should prefer GUI-based tools for the moment. Then, as you master the concepts, you can become familiar with the coding part.

 3. Join a peer group

Now that you know what role you want to choose and for which you are prepared, the next important thing to do is to join a peer group. Why is it important? This is because a peer group keeps you motivated. Taking a new domain may seem a little intimidating when you do it alone, but when you have friends at your side, the task seems a little easier.

The most preferable way to be in a peer group is to have a group of people with whom you can interact physically. Alternatively, you can either have a group of people on the Internet who share similar goals, such as participating in a Massive online course and interacting with the batch partners.

Even if you do not have this kind of peer group, you can still have a meaningful technical discussion on the internet. There are online forums that offer you this type of environment. I will list a few of them

  • Analytics Vidhya
  • Reddit
  • StackExchange

4. Focus on practical applications, not just theory

While attending classes and training, you should focus on the practical applications of the things you are learning. This will not only help you understand the concept but also give you a better idea of how it would be applied in reality.

Some tips you should do while taking a course:

Make sure to do all the exercises and homework to understand the apps.

Work on some open data sets and apply your learning. Even if you do not understand the mathematics of a technique at first, 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 locate you with the right approach faster.

5. Follow the right resources

To never stop learning, you must engulf each source of knowledge you can find. The most useful source of this information is the blogs run by the most influential Data Scientists. These Data Scientists are really active and update followers on their discoveries and frequently publish articles on recent advances in this area.

Read about the science of data everyday and get in the habit of being updated with recent events. But there can be a lot of resources, influential data scientists to follow, and you must be sure that you are not following the incorrect practices. It is therefore very important to follow the right resources.

Here is a list of Data Scientists that you can follow. These are some newsletters to keep you on the road.

  • NYU
  • Wilde
  • KDnuggets News

6. Work on your communication skills

People do not generally associate communication skills with rejection in the roles of data science. They expect that if they are technically profound, they will have the interview. It’s actually a myth. Never been dismissed in an interview, where the interviewer said thank you for listening to your introduction?

Try this activity once; Make sure your friend with good communication skills hears your intro and asks for honest comments. He will definitely show you the mirror!

Communication skills are even more important when you work in the field. To share your ideas with a colleague or to prove your point of view at a meeting, you need to know how to communicate effectively.

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