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4 Insights into the Industry of Data Science

What it takes to enter the current Data Science industry

Photo by Franki Chamaki on Unsplash
Photo by Franki Chamaki on Unsplash

"Run from it. Dread it. But data always catches up. You can either be a slave to data or use it to change the world."

We live in a society where data is the only thing that matters. You can very well make the company’s future if you know how to read the data right. If you can analyse them, use them, work with them, you can do wonders. Maybe that is why the job of a Data Analyst and that of a Data Scientist happen to be the most sought in the current time.

Having grasped insights from Mr. Nisarg Jain, a Data Scientist in one of the leading pharmaceuticals companies, you can rest assured that as you move forward with this read you will have a better knowledge of what it takes to make it in this field.

  1. What kind of Skills, you say?

The industry of Data Analytics has evolved over the years. Going into the placement scenarios, the companies expect certain kinds of skills that one should possess to get through the rounds. But hey, do not worry. We are not talking about some overwhelming skillset to create some advanced technology or some sort. They expect you to have the most basic ones. The ones that can be broadly categorized as:

Soft Skills: This is something that can make or break any kind of an interview. It does not matter how many hard skills you have, if you do not have the soft skills, the going does get tough. Having clear communication, showcasing critical thinking, and have an analytical approach to problems goes a long way in getting you through.

Hard Skills: Well, once you have soft skills, you cannot stay amiss of the general technical ones. Targeting a specific role in Data Analytics requires the basics of SQL and MS Excel. On top of that, the role of Data Science will require the use of Python or R or even SAS Language.

"The main thing that is tested usually in Analytics is your ability to break down and solve a problem and how your thought process is."

  1. Why should Engineers have all the fun?

There has been often been a misconception that only the students pursuing a B. Tech degree or someone from a Computer Science background can enter the field of Data Analytics. But times a-changing, you know. Once the skills are acquired, then it becomes a level field. It only depends on how hard the person works or what path they take. Once they have the basic skills to enter the field there is no differentiation between someone coming from a Computer Science background or say a Finance Background.

"I personally have had the opportunity to work with such people who did not have a technical background and still made it big in Analytics."

  1. Do not fret, the Industry has space for all.

As the market grows, so does the influx of prospective people entering this field. Now that we have established that this domain especially Data Analytics is not degree-specific, the question arises- "Does the industry have enough space for all?" The answer is simple. As the influx increases, so does the opportunities. There are specialized roles being created that did not exist some 5 years back. All the companies are becoming more and more data-aware. They are also realizing that having their own Analytics team is much more efficient than outsourcing the work. This leads to more opportunities. So, it goes both ways. As the number of people coming into this field increases, so do the opportunities provided by the industries.

"There are a lot of companies that are expanding their analytical abilities and creating more roles. The amount of inclination towards Data that is shown by the companies is now huge. Having an in-house analytical team makes more sense for the companies."

  1. Last but not least- Know your niche.

The fields of Data Analytics and Data Science are very very broad. There are no set skills that you can work or gather to get to that successful ending unless you decide what your niche is. If you want to pursue Visualization then you can try honing your skills in tools like Tableau or if Engineering is your call, then there are tons of opportunities in Machine Learning and Data Engineering. So, the only thing that needs to be done is to decide for yourself. It does not need to be the moment you enter the field, but it does need to be sooner than later. Because after all, who does not want an edge over the others, right?

"The sooner you realize yourself and get comfortable with the kind of line you would be pursuing within Analytics, it would go a long way in differentiating you from someone who realizes it 5 years down the line."

And hey, if you are ever stuck and do not know what to do moving forward. Go talk to someone. Go network. Message anyone, and everyone. Find yourself a mentor if you want to. Take the help you need, and then just do the work. Your success has always been in your hands.


References:

[1] Quotes of Mr. Nisarg Jain taken from the podcast conducted by me: https://open.spotify.com/episode/4syoDqlgnie4RdvbIoPNIb


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