Data Science Training Best Practices
Everybody looks up to successful data scientists who have made it big in their life and career. People talk about, follow, learn from and mirror career paths of influential data scientists and experts from some of the biggest market players in the industry.
But do you know what could be better? Learning from those who couldn’t or those who had to struggle more than what was required to get to point B. If you notice, some of the wisest lessons on life and career come from them.
As aspiring data scientists, it’s understandable that you don’t have your career path sorted. You could be confused on your stance right now, your job role or even the company you are working with. But what you need to do is learn from people who stand at the other end of the successful lot. Real eyeopeners come from them.
In this article, we have compiled some of the key takeaways from conversation with such professionals. Follow these to cut to the point you are successful.
Do Not Learn Data Science Because It’s Trending
One of the rookie mistakes in building your career path is jumping into a field or an industry because it’s trending. This happens when you are at a point deciding the way forward in your career and you see people in your circle jumping into an industry that is creating a buzz.
Data science is undoubtedly a field that is taking the world by storm but venturing into it because your friends are doing it doesn’t make sense. You should analyse your strengths and weaknesses, interests, sustainability and passion before you make the move. If your weaknesses outweigh your strengths but still you have an interest in it, put in the effort and make the transition. Apart from this, joining the trending bandwagon could only increase the intensity of your consequences.
Choose The Right Course
When you know data science is the right path for you, you need to choose the right program that would take you to your destination. Today, there are tons of data science programs out there (free and paid) across multiple channels. There are online training sessions, offline classes and blend of both as well.
If you are self-exploratory and find learning at your pace suitable, online training sessions work best. On the contrary, if you need motivation and somebody behind you to push you, offline or in-person classes are ideal.
Besides, choosing the right course also involves the program that fill your career goals. If you are looking at specializing on tools like R, SAS, Python and others, there are specific courses for that. Or, if you would like to cover data science comprehensively, there are full-fledged 10-month programs offered by various players. Depending on your career needs and aspirations, choose the one that works best for you.
Take Part in Hackathons
Courses that you take up instill in you theoretical knowledge. If you look closely, what you learn on textbooks and in classrooms are very different from what you would do at your desk once you join the industry. There is a huge difference between completing a course and becoming industry ready. Only a few courses address these loopholes and tailor programs accordingly. That is why apart from what you learn on your courses, you should take part in multiple hackathons and contests to constantly stay abreast of latest happenings, trends, techniques and tools.
Besides, participating in such events also gives you access to real-world data sets and concerns companies are facing right now. This puts you in the shoes of an actual data scientist and lets you steer ahead from there. One of the other major advantages of doing this is you get to know where you stand in the crowd and what you need to develop (more) to stand out.
Work On Building Your Portfolio
Though certifications and institutes matter, what companies really look for are your exposure to and experience in the industry. That is why you need to work on several projects by volunteering in companies, interning or by doing part-time roles. This lets you gain industry experience and helps you build a substantial portfolio recruiters love to see as well.
Also, data scientists do not have a mundane job. They are bombarded with challenges everyday and a robotic approach to solving problems does not work to your advantage. The more projects you work on, the more credible you are.
So, these were some of the best practices to train on data science. If you talk to data scientists out there, they would tell you that they had fumbled at doing any of the factor we have included here. Do not make that mistake and you would rise in your career. Let us know your thoughts.