Data Science professionals are taking home the largest paychecks, but these professionals also heavily invested in their education to retrofit their careers as per the industry’s demands. But do we know how they got into this domain and more importantly, what was their learning path to become a data scientist?
Analytics India Magazine has kick-started a new column – ‘My Journey In Data Science’, where we feature experienced data science professionals and showcase their side of the story.
This week, we got in touch with Pravat Ranjan Jena, Senior Data Analyst & BI at Dell. Jena has over 6 years of experience in the field of data science. Over the years, he has worked on several successful projects and no doubt his LinkedIn recommendations do showcase his success.
A graduate from ITER – Siksha ‘O’ Anusandhan, Bhubaneswar, Jena has always been enthusiastic about data. While his fellow mates were not aware of the popularity of data, Jena was already exploring the space early on.
“There was a time when many of my college mates didn’t know about the significance of data. They even mocked me thinking I am wasting my time doing data all the time. I am not bragging about it, but today it’s been more than 6 years and I am in a far better position than most of them,” said Jena.
When asked how he managed to land his first job, Jena said that he made the most out of an opportunity. There was an interview at Intel Security (McAfee) and Jena managed to crack the interview and landed the job. He has worked with companies like Oracle and IHS Markit as a data analyst — he has worked on data management, data mapping and cleansing of mass records & data enrichment.
“Data science is amazing. You never get bored — every day seems to be exciting. I personally feel that this domain teaches you on a daily basis. The learning never stops,” said Jena.
The Learning Phase
Talking about the learning phase, Jena has never taken up any course on data science. The Dell Data Analyst believes if you have the urge to learn something, you can always do it without spending a penny on courses.
“There is a tremendous amount of content that is available on the internet for free. You just need to know how to make the most out of them,” said Jena.
Even though Jena believes the internet is replete with learning resources on a vast number of topics, he also believes there are many channels, influencers and YouTubers who are misleading data science aspirants and beginners. Jena’s advice is to stick to authentic learning resources and not follow fraudsters or fake influencers who position themselves as experts in the domain.
Jena’s entry to data science was purely on-the-job. “When I got my first job, I have put in tremendous efforts from my end, so it became easy for me to learn and work at the same time,” said Jena. “When you work for a company it’s not just the salary you get, but whether you accept it or not, you get to learn. You just need to have the enthusiasm of taking everything as learning.
This How You Handle Rejection And Upskill
When asked how he handles rejection, Jena shares he has always been positive about everything in life. According to him, a rejection doesn’t imply that you don’t have the required skill set, it means it is not aligned for that job role. So instead of complaining about rejection, move on to the next step and try again.
On upskilling Jena shared his experience:
“I once went for an interview where the interviewer asked me whether I have experience working with databases. I said I have worked with SQL Database for a significant amount of time. However, the interviewer said that they are looking for someone who has experience working with MongoDB.”
With time, things are changing — whether its skills, tools or any other things. So, what one should do is, keep upskilling and learning new tools. Do not limit yourself to the tools that you are using on your current job. Explore the domain; see what is happening around the industry.
Jena’s Advice To All Aspiring Data Science Professional
If you are a fresher, do not jump right on to learning the critical aspects of machine learning or artificial intelligence. Firstly, try to know the industry — domain knowledge is really important. You have to understand the trends in the industry. Later you can move on to learning programming and databases.
Jena reveals there is no need to spend a lot of money on courses. He believes one can definitely make a great career in data science by making use of open and free sources available on the internet. Also, do not confuse tools with concepts. Learn the right way.
“Don’t get tempted by looking at other data scientists using some high-end tools and solving complex problems. Everything takes time, even the top data scientists started from a basic level — get the foundation strong Start from the basics such as SQL, Tableau, MS Excel etc. and then jump on to the high-level concepts,” Jena said in closing.
