Why you should be a Generalist first, Specialist later as a Data Scientist?Admond LeeBlockedUnblockFollowFollowingMar 16When I first started out in data science, what I wanted to become was dead simple — be a data scientist.
I had no idea if I wanted to become a data science generalist or a specialist.
And to be honest, I hadn’t heard of these terms — “generalist” and “specialist” — not until after being in this field for quite some time.
This makes me wonder the pros and cons of each of this and ponder over my career path in data science.
After doing some research online and talking with some people in this field, I’ve made up my thought to become a data science generalist first — aka full stack data scientist if you’d like to call that — and a data science specialist after gaining more experience and skills in different areas.
And you’ll know why in the later section.
In the following writing, we’ll discuss more about generalist and specialist in the context of data science.
So what’s a Generalist and a Specialist?Before going any further, let’s first understand what we mean when we talk about being a generalist and a specialist in data science.
A generalist is someone that has knowledge in many areas whereas a specialist knows a lot in one area.
Simple as that.
Particularly in data science, it’s notoriously hard to become a generalist in all phases of data science project lifecycle.
It takes years to acquire all the skills in different areas, yet it’s not necessary to master all of them.
Similarly, it’s not easy to be a specialist in data science either.
Now you might define a generalist as the Jack of all trades, master of none.
I couldn’t agree more about that.
And this is precisely the reason why I’d choose to be a specialist in the later stage of my data science path.
Why be a Generalist first?After all, being a generalist is not meant to master anything, but rather to understand the full picture of the whole data science project lifecycle.
The question is: Why is understanding the full flow of the data science project lifecycle important in the first place?You see.
As a data scientist, we don’t build a machine learning model just for the sake of building it.
We first understand the business problem and frame that into a problem that can be solved through data science approach.
Then you need to identify data sources and define success metrics.
Depending on your company’s stage of maturity, you might also need to build a pipeline to collect data (YES, you may not even have data in place)…We can still go on and on but here is the point — all the job scopes above are part of being a generalist.
The good thing is that you’ll get to know the full picture of the data science problem as a generalist — as a data scientist in the beginning of your career.
In other words, you’ll learn and you’ll grow, tremendously.
What I’m advocating here is this: If you’re someone who is starting out in data science, my recommendation is to be a generalist first.
Go join a startup and take on many hats as you will probably be the only data scientist in your company.
Generalists add more value than specialists in a company’s early days, since you’re building most of your product from scratch and something is better than nothing.
Your machine learning models don’t have to be a game changer but should be able to provide actionable insights and results.
Learn how you can help the company generate more revenues.
Learn how you can leverage the existing data or build some pipeline to collect data to solve some problems.
Start with the low-hanging fruit first.
There isn’t always a need to go for AI if the company isn’t ready for that.
In fact, normal statistical approach is typically sufficient to tackle some simple problems.
The ideal data scientist is a strong generalist who also brings unique specialties that complement the rest of the teamBe a strong generalist.
Be the Jack of a trades.
Once you’ve enough experience and you’ve found your interest and passion in a specific area (say NLP), then you can deep dive into that, which leads us to the next stage.
Why be a Specialist later?Say if you’re a NLP specialist.
Your focus could be solely on building the best NLP classifier model given the data.
And that’s it.
All the things are already set for you.
The business problems are well defined (done by product managers).
The pipeline is ready and maintained 24/7 (done by data engineers) and the data is there for collection.
What you need to do is do what you’re best at.
This is crucial as you can focus on your expertise and strength to add the highest values to the project.
It’s perfectly fine to be specialist in data science.
Being a specialist in your niche plays an important role in a company, which is also something that makes you irreplaceable and valuable to others.
At this stage, since now you’re already experienced in different areas as a specialist in data science.
Your experience and expertise are not something that can be easily substituted by others.
Even better, you’ll be able to focus on your specialization and work with others as a team with your broad knowledge and understanding of other parts of the data science workflow.
You’re no longer someone who knows only one thing.
Instead, you’re someone who knows many things with the focus on a particular thing that makes you special.
Final Thoughts(Source)Thank you for reading.
I hope this explains why I decided to become a generalist first and a specialist later in my data science journey.
The idea of being a generalist first, specialist later as a data scientist may sound a bit controversial for some people.
At the end of the day, there’s no right or wrong answer to that.
The choice and order don’t really matter as long as that fits your vision as a data scientist.
As always, if you have any questions or comments feel free to leave your feedback below or you can always reach me on LinkedIn.
Till then, see you in the next post!.????About the AuthorAdmond Lee is a Big Data Engineer at work, Data Scientist in action.
He is known as one of the highly sought-after data scientists and consultants in helping start-up founders and various companies tackle their problems using data with deep data science and industry expertise.
You can connect with him on LinkedIn, Medium, Twitter, and Facebook.
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