Year by year, more people are taking data science as a career option. While experienced folks from the domain are making transitions from one firm to another, there are people who either belong to a different domain or are just getting started with data science. And it is not an easy task to start data science right from scratch — not only in terms of knowledge and skills but also in terms of the time gap that comes between data science course completion and finding a job.
The Typical Scenario
Whether you belong to a different department or you are a graduate, when you take up a data science course, it roughly takes 6 months to 1 year, and it might even go up to 2 years. You start from the basic and eventually move to the core aspects. But the major challenge here is the time frame to complete the course — especially for someone who is making a complete career switch. That black hole of time between completing a data science course and finding a job is really crucial. A significant amount of gap might make a negative impact on your resume.
So, how to cope with this challenge? How to make the most out that time frame? In this article, we are going to figure out just that.
The Portfolio Game
The first thing to do after completing your data science course is to focus on making a portfolio which stands out. You might have done projects or worked as a freelancer, or might have helped small companies deal with their data, add each and every relevant thing on your portfolio. It is considered to be the candidate's first impression, therefore, focus on putting up more things about your work rather than your school achievements.
Word to the wise: While every other person fakes a lot of things on their portfolio, you, on the other hand, stay genuine. Because at the end of the day, it is the candidate who is going to prove his/her worth, not the portfolio.
The Job Hunting Phase
Do not apply for jobs at the get-go or in the middle of the course. Rather, when you reach the final sessions of your data science course, start applying for the jobs that suit you well. Also, by the end of the course you would have a resume with significant learnings, projects (both from the institute’s side and the volunteer ones), so make the most out of them to create a profile on portals on the internet.
Doing this, you give yourself the time you need to learn everything completely and when you apply for a job during the last session of your course, your chances of struggling after the course completion decreases.
Another point to keep in mind while surfing the internet for jobs is to know where job posts are relevant and genuine. There are several websites on the internet that pretends to be a top player in the job posting sector; however, that is not completely true.
Where you look for a job is again a crucial thing. It is no surprise that a huge number of job seekers waste a lot of time attending irrelevant interview referred by unreliable job portals. So, when it comes to seeking a data science job, you can always rely on portals like LinkedIn Jobs and Analytics India Jobs.
Focus On Experience, Not On Money
When you take a break from your previous work domain, opt a data science course, and then start looking for a job again, you might feel like working on a side hustle to keep the cash flowing. However, rather than focusing on working and making money, you can participate in hackathons and other online competitions. It would not only help you polish your skills by letting you work on real-life problem statements but also help you add a brownie point on your resume.
If you feel you can utilise your free times to help early-stage companies with their data, you can opt for that too. But make sure you do not indulge in projects from big companies, as they tend to keep things fast-paced and it might end up taking a lot of your time. You either focus on polishing your skills by helping the ones in need or completely focus looking for data science.
Without a doubt, LinkedIn is one of the best ways to advance your career by making connections. If you are not making the best of the platform now, then you might be left behind by a huge margin. Connect to senior folks and HRs from the data science industry and try to figure out how they hire, what are the skills they look for etc.
Once you get a clear picture of the data science hiring scenario, then maybe you can look back and rectify the mistakes you were doing at the initial stage. It would not only give you an idea of the industry but also let you know about the concepts and skills that need more focus to land that dream data science job.
Attend Conferences And Hackathons
Many might not be aware of the fact that even events, conferences, and hackathons are some of the great ways to increase the chances of getting hired. When you try some ways to land a job but fail, then take a break and attend some data science related conference. These conferences also conduct hackathons and competitions, and if you manage to catch some eyes of some of the senior folks from the industry, you definitely going to increase your chances of getting hired.
Making a transition from one firm to another is one thing; but when you switch domains, things become critical. You have to have a plan about how you are going to take things ahead and how you are going to a different approach when it comes to learning and looking for a job.
Register for our upcoming events:
- WEBINAR: HOW TO BEGIN A CAREER IN DATA SCIENCE | 24th Oct
- Machine Learning Developers Summit 2020: 22-23rd Jan, Bangalore | 30-31st Jan, Hyderabad
Enjoyed this story? Join our Telegram group. And be part of an engaging community.
Our annual ranking of Artificial Intelligence Programs in India for 2019 is out. Check here.
Provide your comments below
What's Your Reaction?
Harshajit is a writer / blogger / vlogger. A passionate music lover whose talents range from dance to video making to cooking. Football runs in his blood. Like literally! He is also a self-proclaimed technician and likes repairing and fixing stuff. When he is not writing or making videos, you can find him reading books/blogs or watching videos that motivate him or teaches him new things.