A disclaimer before we start, Data Scientist is currently being touted as the lone savior of the world, “the sexiest job” of the 21st century.And because it is so “sexy” anyone remotely doing anything with data calls himself a Data Scientist. Educational institutions teaching stand-alone Python or R to students claim to produce data scientists.I am going to approach this post from a standpoint of rationality.Becoming a data scientist is putting in your blood, sweat, and tears.Nothing except sheer hard work is going to get you there. There may be bruises and cuts.So let me say this, at the end of the day, you are going to look anything but “Sexy”!!
OK, let’s get started, so you’ve made a decision to pivot your career into Data Science. Congratulations! You’ve just decided to make your life that much more intense. Now here are some ideas on how to become and stay a data scientist.
The speed at which technology is changing in the space is phenomenal. How does it feel to be standing here?
The field is vast and learning is immense.Know where to start and when to move on. I would suggest always go for a breadth-first approach to understand the core concepts by doing it yourself, hands-on approach to staying relevant. Pick up a couple of things at a time, spend a few hours each week going deeper into each one and you will have no problem keeping up with this dynamic landscape. Don’t burden your future self, be at peace with what you know today and also with what you don’t. Be comfortable with chaos.
Learning Data Science is like going to the gym, twice every day!
Data Science isn’t about picking up a couple of books, doing a few online courses, attempting a Kaggle competition and proclaiming that you are a Data scientist on all possible Professional networks. If you are taking a plunge into it as a first timer, it is like going to a gym and lifting heavy weights every single day.You get better with practice. Almost nobody gets it right the first time, or the 12th or the 19th time.
Work on real problems, currently, we are not solving a driverless car problem in India
Find interesting real open data sets, formulate a few questions you want to use the dataset to answer and some underlying problem to be solved.
Here are some interesting sources —
Pull in any supplementary datasets you want to use.Make sure at least one or two of these questions push the boundaries of your knowledge or force you to learn new tools or domains.
Share your work with others on GitHub
Learning is Organic, Don’t be in a rush
Everyone is in this big rush to learn the necessary programming for data science in 90 minutes..5 days… Researchers have shown it takes about several years to develop expertise in any of wide variety of areas, including chess playing, music composition, painting, piano playing, swimming, tennis, and research in neuropsychology. The key is deliberative practice: not just doing it again and again, but challenging yourself with a task that is just beyond your current ability, trying it, analyzing your performance while and after doing it, and correcting any mistakes. Then repeat. And repeat again. There appear to be no real shortcuts.It grows. Just like a child. You can’t accelerate the years. Accept it.
Grow your Network
Attend Data Science Meetups in your city and meet like-minded people, expert data scientists who have been there, done it. Take inspiration from their journey and the work accomplished. On Meetup.com, check out the Meetups that are well attended, have a vibrant community and have regular events.Here is an interesting hack to help you find out the best meetup to attend in your city, in a pythonic way!
The Bottom Line
The key is to stay patient, keep working diligently. Final parting thought –