How Crucial Is Domain Knowledge For Data Scientists?

 Myth Bust: Data science is not just about coding!

Watching people create data science projects using Jupyter notebooks might give a superficial impression of data science as a hardcore programming-intensive field. Numerous people are led to believe that data science is all about Python and R. However, Data science has more to offer than just writing plain code. Rather, coding is merely an “enabler” or a handy tool to ease the overall process.

Then what exactly is data science about if not hardcore coding?

Well, simply put, it has more to do with your statistical problem-solving skills. A good data scientist is expected to understand the problem statement and arrive at data-driven outcomes/insights.  In order to be good at solving business problems, one needs to understand the kind of business itself in the first place, and that is where domain knowledge comes into the picture.

Wikipedia defines domain knowledge as “Domain knowledge is knowledge of a specific, specialized discipline or field, in contrast to general (or domain-independent) knowledge. The term is often used in reference to a more general discipline—for example, in describing a software engineer who has general knowledge of computer programming as well as domain knowledge about developing programs for a particular industry.”

Why is domain knowledge important?

1.       1. To get specialized insights by narrowing down on certain aspects. (derived features)

2.       2. To understand the project requirements in a better way.

3.       3. To decide what is the best data for the problem.

4.       4. Understand the data better (Better optimization).

Here are some tried and tested ways to improve your domain knowledge:

1.       1. Take up an actual job/internship. (What better than getting your hands dirty? )

2.       2. Participate in NGOs/unpaid Jobs (To learn how an organization works)

3.       3. Domain-specific courses. (For example, if you are interested in working on an aviation-based use case, you might want to learn more about areas such as aerospace, aero robotics, aeronautics, and UAV (Unarmed Ariel vehicle) related fields by doing some extra courses.)




Image Courtesy: http://www.nexiats.com.sg/wp-content/uploads/2017/11/Nexia-Pulse-Q3-2017.pdf

 

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