Numbers matter

Data science and machine learning offer several opportunities. Kunal Jain shares tips that can help to make it a lucrative option

Technology is changing at a pace that multiple industries are ripe for disruption. What was relevant and sought after a few years ago, is no longer in vogue. Ask a Digital Marketeer, a banker or for that matter a professional in any industry — would they be relevant if they did not learn new things for the last five years?

In a lot of scenarios — having experience is actually going against you. Try saying that you have seven years of experience in NET/Java/software testing and see what opportunities do you get. Now say that you are in your final year of graduation, have a good understanding of machine learning and have been winning data science competitions for a year — you will see floodgates of opportunities open.

The point is that the field for creating impact after graduation has never been this flat before.

Also there has been a slew of new technologies which have become mainstream in the last five years. blockchain, data science, Internet of Devices (IoT), augmented reality (AR) and virtual reality (VR), reusable space vehicles, new programming languages, new programming frameworks — all of these have come up in the last five years.

So one can’t ask for experience beyond a few years, if one want to hire a lot of people and make fast progress. Among all of these, data science and machine learning probably hold the most number of opportunities as things see today. Data science is now a horizontal impacting multiple industries — banking, telecom, ecommerce and oil & gas. So, it is no surprise that most of the graduates coming out today look up to building careers in data science.

Having said that, a lot of them do not make a meaningful effort in learning and applying the subject. Hence, it is still a very small percentage of people coming out today — who get opportunities in data science. But, with the right preparation and application — there is no limit to the kind of opportunity you can get and the kind of impact you can create.

So, how does one prepare for a data science career as a person graduating soon? Here are some handy tips.

• Start learning about data science and programming as soon as you can. It does not matter which stream you are in. As long as you have good quant skills, love problem solving and have a lot of curiosity — you are a good fit with data science roles. Start by learning python, basics of statistics and linear algebra. Once you have done that — you can move ahead with learning data science algorithms and processes.
• Apply what you learn. Data science is actually an Applied Science and what better way to stake your claim than showing some applications. You not only learn how to solve problems using these tools and techniques, but also differentiate your resume from all those wanting to do data science but not putting in efforts in the direction. Participate in practice problems on Analytics Vidhya or Kaggle to start with. Once you are comfortable, participate in a few hackathons and data science competitions. You can also pick up open data sets in the area of your interest to create projects and solve problems.
• Participate in data science communities and look for mentors. There are some thriving data science communities today in India. Join these communities, interact with data science professionals through meetups, webinars, slack channels and find a few mentors to guide you for the rest of your career.

As long as you have a few years to learn, you can start devoting a few hours daily and build up the momentum over time. If that is not the case and you have placements coming up in the next 3 - 6 months, you will need to put in more effort to cover up for the expectations. There is no dearth of resources available — only time! So make sure you devote high quality time and you can cover the ground.

Remember — you can't really ask for a better time to graduate!

The writer is CEO & Founder of Analytics Vidhya