“Is there anyone here who, planning to build a new house, doesn’t first sit down and figure the cost so you’ll know if you can complete it?”.
Believe it or not, Jesus says in the Bible. Whether you’re inclined to listen to Jesus or not, you must admit that his saying applies to life.
Like selecting your career path.
Would you want to become a doctor if you’re not ready to commit yourself to long hours of stressful work, no fixed days off, and not to mention a lifelong commitment to learning? No, right?
Similarly, choosing to become a data scientist is not some frivolous decision you make. It may be one of the most sought after jobs in tech, but it has its own demands.
We spoke to someone who’s been in the trenches on the battlefield of a data scientist and asked him to share his views on this multifaceted role.
So, we present Parikshit Nag, the Head of Data & Analytics at Indus OS, and his “5 Reasons Not Become a Data Scientist”.
[Prefer listening to this rather than reading? Check out our podcast on Spotify: https://spoti.fi/3uGA4Pc]
Reason #1: You don’t want to be a lifelong learner
Back in 2012, the Harvard Business Review deemed data science as the “sexiest job of the 21st century”. It was in high demand and the data scientists were paid well.
A decade later, data scientists are still in demand and are still paid well.
But that’s one aspect of the industry.
Although it is a lucrative career, there’s a high chance of falling behind if you don’t keep yourself up to date with all that’s happening in the industry.
There’s always new research, new algorithms, and new architectures that pop up regularly. Data science is constantly evolving and if you don’t commit yourself to continuous learning, you run the risk of becoming irrelevant.
Reason #2: You’re not ready to do the dirty work
Remember those times when you were expected to warm up before every training session. Whether it was at the gym or the field, you had to warm up. Warm-up is tedious, onerous, and sometimes looks pointless.
But is it though?
Warm-ups are an integral part of any training routine without which you would increase your chances of injury.
If you don’t want to do warm-ups you might as well forget about training.
It’s no different with a data scientist’s job. Many associate data science with cool and complex algorithms. Although algorithms are part of the job, many forget about the more tedious, onerous, and sometimes pointless looking tasks like data cleaning and wrangling.
The primary purpose of a data scientist is to get the right data in place in the right format. And this necessitates doing a lot of dirty work.
Reason #3: You don’t like the business side of being a data scientist
Data scientists are not shooting arrows in the dark. They shouldn’t be. Their work should be geared towards solving a business problem.
An aspiring data scientist should understand it’s the business side of things that drives the work. Being able to adapt your expertise in AI, machine learning, and algorithms to an organization’s business strategy is vital to surviving in the field.
If you’re only interested in the technical aspects of the job, you probably won’t enjoy a career in data science.
Reason #4: You hate maths, stats, and coding
Data science is built upon four pillars. They are business acumen — which we mentioned in the previous section — maths, statistics, and coding.
Maths and stats provide the foundation for data scientists and coding helps in the execution.
You may be able to scrape your way through by depending on previously existing algorithms. But when it comes to customized solutions, you’ll be at a loss to take action without a keen knowledge of maths and stats. On the other hand, without coding, you won’t be able to get any of your models into production.
Reason #5: You like being told what to do
Data science is still in its infancy. 10 years ago, there weren’t even college courses on data science!
As a data scientist, you’ll work with a lot of unstructured data. Also, not a lot of people know or understand data science, therefore it involves a lot of exploratory efforts. As a result, you need to be creative enough to solve problems that present themselves.
Unlike software development, data science runs in a very iterative fashion.
It needs extensive research and you depend on your intuition to come up with creative solutions.
If you can’t work independently and think on the fly, you may want to consider another field other than data science.
Conclusion: How You Can Become a Data Scientist
The road to becoming a data scientist is clearer nowadays. With many having traveled the path, we can tell what it takes to become one.
To become a data scientist, simply do the opposite of what was told in the earlier section! In other words, be a lifelong learner, think independently, and understand the importance of the business side of things.
Being able to formulate solutions from half-baked business problems is a skill that will take you a long way in this career.
P.S. We’re hiring now and we’ve got multiple positions open. Click the link to find a good fit for you: https://bit.ly/3Oz2ylj.
Enjoyed this post? You can read more of our blog posts here.
You can also keep up with all that’s happening within the app world by following us on Twitter, Instagram, Linkedin, and Facebook.