Data Science is a booming field in India right now, and as per our report, there are an estimated 97,000 job openings. So big has this field become, that it has now become an integral part of almost all sectors across the globe. However, online forums are full of questions like, “How can I become a data scientist? What are the most valuable skills to learn for a data scientist now? Could I learn how to be a data scientist by going through online tutorials? What does a data scientist do?”
While there are many full-fledged courses (both online and offline) available now, there are many fresh graduates who are also exploring the opportunity of learning data science skills along with their education or day jobs.
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In this article, Analytics India Magazine will take stock of these steps via the routes adopted by noted professional data scientists.
The Basic Question: Why Do You Want To Be A Data Scientist?
It is true that the “hottest job of the 21st century” has all the buzz, glam and lucre, but many enthusiasts are still confused as to what this job entitles. Fewer still, understand what it takes to be a data scientist. One of the key questions that self-taught data scientists answered right at the beginning of their careers was, “Why data science?”.
Raj Bandyopadhyay, a Data Science Expert-in-residence at a noted data science learning platform, said, “In early 2012, I was a software engineer in Atlanta and getting frustrated with my role. Granted, I had a thorough background in Computer Science (with a PhD and all that), but after 3 years, working as a developer wasn’t satisfying anymore. I wanted to take the next step in my career, but the only option for a developer seemed to be to become a project manager.
I looked into management consulting for a while, but didn’t have the stomach to put in 80-100 hour weeks with tons of travel. Out of curiosity (or perhaps desperation), I signed up for the first product of a brand-new startup, and that put me on the path of being a data scientist.” That startup was called Coursera, and the product was its first-ever course Machine Learning by Andrew Ng.
Assess Your Skillset
Ratnakar Pandey, India Head Risk and Analytics at Kabbage Inc, says that assessing existing skills is the very key to achieving success in data science. He enumerates the following skill sets are important in a data scientist:
- Love for numbers and quantitative stuff
- Grit to keep on learning
- Some programming experience (preferred)
- Structured thinking approach
- Passion for solving problems
- Willingness to learn statistical concepts
“If you think you demonstrate above skills and aptitude and/or willing to learn, then one can start on this journey by checking out MOOCs like Coursera, edX, LinkedIn Learning and similar online learning platforms…” He adds that due to the democratisation of ML/AI, Google, IBM and other such companies have made it easier for all of us to have access to and grow our knowledge on Big data, ML/AI tools and techniques. He says that some of the free tools which a beginner should try to take out for a spin are:
- Google machine learning stack – Tensorflow
- Apache Spark
- IBM Watson
- Microsoft Azure
Upgrade Your Skills
Pravin Mhaske, one of the first ever winners of MachineHack, has been working with Infosys for about 15 years now. He manages projects professionally and plays with numbers as a passion. When asked about his journey with data science, he says that he got attracted to the field because of the “big data” buzz in 2016. Since then he has spent a lot of time polishing and learning basic skills in data science like:
- Inferential statistics
- Probability
- Linear/vector algebra
- Calculus
He did this by referring to various sources like Udacity, Khan Academy, edX and the 3Blue1Brown videos on YouTube. Mhaske also completed courses on R, exploratory data analysis and Python programming from Coursera, Udacity and IIT Madras (NPTEL) followed by data analytics course by IIT-M (NPTEL) which he found to be excellent.
Participate In Hackathons
Now, more than ever, we are seeing a pattern where young professionals want more challenging jobs and more interesting work, especially in the analytics sector. And hackathons have turned out to be a very valuable source of answers to both employees as well as employers. According to a survey conducted by AIM, over 64% of organisations have at one point or the other conducted hackathons for ideation and creating a healthy sense of competition among its employees. In fact, over 44% of the employees we interviewed said that their organisation held hackathons at least once a year.
Be Active At Meetups
With data science being a relatively new field, it is easy to get overwhelmed by the number of resources available online. We often hear about data science enthusiasts kick-starting their journey with online content but that’s only enough to teach you the basics in this field. This is where meetups come in handy. As online learning can also be a disjointed experience, data science meetups offer an excellent opportunity for networking and hands-on skills-building sessions for professionals.
Mingle At Conferences
Noted industry events like the upcoming conference Cypher are a great melting pot of interesting personalities from across the country. Given a vast range of topics covered over three days in five parallel sessions, Cypher sees a generation of content from the best minds in the industry like no other conference. Here, data scientists have a great chance of meeting peers, potential employers as well as competitors under the same roof.
…Or Just Jump In
In our talks with different self-taught data scientists, we also found out that many of these (now) veterans say that if they had been too prudent in their self-study, they would not have made it. Many of these successful data scientists have said that sometimes, jumping in with both your feet in the air works out for the best.
A data scientist said on a public discussion forum, “I quit my day trading job and studied for online courses on statistics and linear algebra… Subsequently joined a young startup as their first data hire. Stumbled around until I could create a scoring model based on assigning weights to parameters. Back then, I didn’t know what the technique was called. In retrospect, it was something similar to linear discriminant analysis. I gained some intuition there. I later joined a graduate program in data science, where I took individual projects in various domains. Played around in Kaggle a bit. Was I fully self-made? No one ever is. I had online guidance right here on Quora, R code on Stack Overflow, a startup which paid me good pocket money for a proof of concept, and a couple of professors with good material,” he says.
