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How to Build a Successful Career in Data Science

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Data science is an outgrowth of business and science – It essentially involves asking the right questions and providing valuable answers to these questions using real-time data.

From the above definition, data science may seem pretty easy, but you’ll find pursuing a career in this field a lot harder than you’d think – “asking the right questions and providing interesting answers” is a lot more difficult than it sounds.

If you’d love to pursue a career in data science, then you should be prepared to work with some really overwhelming big data – both structured and unstructured – from different sources.

To make everything easier for you, we’ve outlined a few tips from expert data scientists in the field to help you build a successful career.

Educational Requirements

Now that you have a fundamental area you want to focus on, it’s time to go to school and really pursue your goals.

There are many masters in data science courses available in various accredited institutions that can help you achieve your goals. However, you should find out if these courses cover the basics of your data role before applying. This way you get to study what you actually wanted.


Data Science

Choose the right data roles.

There are several data roles in data science – data analysis, analytical management, data architecture, data mining, machine learning and so on.

In data science, you cannot afford to be a jack of all trades, hence first and foremost, you have to figure out the area that most interest you and then take active steps to focusing on it.


Before you focusing on any area; first, check your strengths and weaknesses – what qualities do you have that can help you perform excellently in that role and what can inhibit your success? Knowing your strengths and weaknesses can help you maximize every opportunity to build a successful career in data science.

Get comfortable with tools and languages.

While undertaking your course, you should ensure you get comfortable with various tools like Pandas, sci-kit-learn, matplotlib, etc and in programming languages like Python and R. A.

Data Scientist is primarily a programmer who is better in statistics that any programmer and at the same time a statistician who is better at programming than any statistician.

Brush up your communication skills

Communication is key in data science. As a data scientist, you’ll have to work with lots of business organizations and other data scientists. So it’ll be prudent to brush up on your communication skills if it’s a little rusty.

Work with peers or in groups

Data science involves solving human problems using real-time data. There are many ways to arrive at this solution, and you can only learn more by working with peers or in groups.

Your peers essentially don’t have to be physical ones. You can find lots of peers on internet forums like Reddit, Quora, stack overflow, and the likes, who will happily provide you with tips when you encounter any difficulties.

Human problems and needs are continually changing, and as such you must be ready to continue improving your skills as a data scientist.

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