Data Science is one of the most coveted job roles in the industry right now. There’s no doubt it. But if you are looking to start a career in this field, more often than not, the conversation goes something like this –
“I want to have a high growth role and a role with a high pay-out which is exciting and helps me solve big organizational or global problems. ”
Your friend replies – “I have heard data science is a buzzword, it is a high growth job, with a high salary band, and gives you the power to solve crucial problems. You should definitely check it out!”
A majority of people know that data science is exciting, an upcoming field that has a high salary band but only a few know about the depths of the field. So in this article, I am going to talk about some of the fundamental discussion points that you must know if you are starting out in this field.
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Data science is a combination of data analysis, algorithmic development and technology in order to solve analytical problems.
A data scientist works on complex and specific problems to bring non-linear growth to the company. For example, making a credit risk solution for the banking industry or use images of vehicles & assess the damage for an insurance company automatically.
In simple words, a data scientist is a problem solver who uses data to solve problems that create business value.
A typical data science project lifecycle looks like this:
But a data scientist may not be involved in all of these steps. Let’s look at some of the data science-based roles.
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Data science is a multi-faceted role. There is no one-size-fits-all approach to learning data science. Having said that, there are a few core skills you will need to pick up to make a successful career transition to data science.
Here are the key skills you would need:
Apart from these core skills, there are other skills you should be aware of, such as:
The role of a data scientist requires good quantitative skills as well. So if you are coming from a non-quant background make sure to strengthen your quant skills!
Here are some great resources to learn machine learning skills –
A lot of data science professionals and enthusiasts get confused by the data science career lifecycle. It is probably due to data science being a relatively new field and organizations globally are trying to figure out the “Sexist Job of the 21st century” as they incorporate data science projects in their organizational priorities.
Let’s check out a typical career path as a data scientist –
Caution: The data science career path are losely defined in the industry. The exact career path can depend on the maturity of your organization in data initiatives.
As we can observe in the above career path, being in a data science role throughout the journey can lead up to a position of a data science leader which is about identifying and formulating business problems, stakeholder management, and forming the overall data strategy of the division or the organization.
As mentioned above, since the role is relatively in its nascent stage, a lot of professionals apply their learning to move on to entrepreneurial path or product roles. Data Scientists gain plenty of domain knowledge over their experience and hence gain a lot of insightful knowledge about the product and industry-leading them to do well as entrepreneurs or product managers.
Are you trying to make a career switch to data science for getting a salary bump? Well, the move is entirely justified! However, it isn’t as straightforward as you might think. There are certain things, such as work experience and your current domain, that will play a MASSIVE role in deciding your salary post-transition.
Taking figures from the popular and relatively accurate website called Glassdoor, this is what the salary situation looks like for a data scientist:
As you can observe, in India, the average salary of a data scientist is approximately INR 10,00,000 per year. Whereas, the average salary for the same in the USA is a whopping $134,000 per year. (there is definitely a big gap there!)
If you bring a bit more experience to the table and you have relevant domain experience, you might look at a more senior role (though this is a bit rare if you have no prior data science experience):
As a senior data scientist, you will be looking around an average salary of approximately INR 19,00,000 per year in India. In the USA, you can expect an average salary of a whopping $134,000 per year.
As we said, it comes down to how relevant your previous experience is. More often than not, as a person transitioning from a non-data role to data science, you’ll be looking at the first graph whereas, if you have been previously working in the data industry, you might be eligible for a senior data science role, which is the second graph.
Data Science is one of the most exciting and upcoming roles in the industry with a lot of growth and financial potential. As we have noticed in so many reports, there are plenty of data science jobs out there but there’s a big caveat! There are not as many skilled data science professionals for these job roles.
A recent Gartner report projects that there will be approximately 2.3 million new jobs created by the year 2020 in the field of Artificial Intelligence and Machine Learning.
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So are you ready to take on the most coveted job role of the century?
In this article, we discussed some of the key attributes of being a data scientist, the role, skills, salary, and finally how can you become an industry-ready data science professional with the help of the AI and ML Blackbelt program.
I hope you got to learn about this exciting industry and found this article insightful. I will be happy to hear your thoughts in the comments.
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