OPINION
The supposed hottest job of the 21st century might not be so hot after all. Data Science has been with us for some time now, and it’s no longer just a buzzword. Both people and companies have utilized it to create value and money, but is it really a profession of the future?

Note from the author: This is an opinion piece, so it’s probably biased to a degree. Jobs in your country and with your skillset may vary. We don’t see the world through the same eyes. Please leave your thoughts and experiences in the comment section.
If you’re doing anything software related you’ve probably considered the option of switching to the field of data science. And why shouldn’t you, the jobs are supposedly everywhere, salaries are generally higher than in software development, and having the word "scientist" in your job title would make your mother proud.
Well, maybe not the last one, but you get the point.
After being in the field for a while now, exploring a good bit of libraries and other cool stuff, writing around 80 data science-related article, whilst in the same time exploring other options (like web and mobile development) on the side, I find my self to be qualified enough to break down the good and bad things about the field.
The focus today will primarily be on the bad things because the Internet is full of "why you should become a data scientist" and "learn data science in a month" type of articles.
With that being said, the ideal reader is someone who knows what will be the benefits from enrolling in the field but would also want to know what are the possible drawback. Also, someone who’s already in the field for a while might also find these points useful too.
Okay, without further ado, let’s begin with the first one!
1. Data Science is Boring
Yes, you’ve read that right. Most of the data science boils down to data extraction from the source table(s), performing some aggregations and calculations down the road and then storing results in the new table which is suitable for analysis. Well, that describes an ETL process perfectly, not data science.
Further, you’d spend some time cleaning and preparing data, which once again isn’t purely data science-related. Finally, we have the segment which deals with predictive modeling – this one also isn’t new, but gained in popularity a lot in the last couple of years.

Those three components combined, alongside with decent presentation and communication skills make your average data scientist.
But wait, won’t data science will revolutionize the world?
Yes and no. Yes in the term that professionals will be able to do their jobs better (like doctors), and no in the term that your ETL pipeline interest absolutely no one, and has nothing to do with your "data scientist" job title.
2. Data Science is Getting Automated
Or at least, the fun parts of it. You know, the buzzwords which made you enroll in the field in the first place. Stuff like predictive modeling, Machine Learning, etc. Don’t get me wrong, a lot of stuff can’t be automated here (yet), but a good portion of it already is.

And that’s sad, because whilst masses were worried about routine jobs getting automated we’ve actually automated everything interesting about our job. Nice.
I mean, have you seen professional cloud environments? If not, that’s okay, because they are pretty sad to look at anyways. Basically you have a limited number of algorithms that you can fit, and as long as the data is prepared in the right way, anyone who knows that it’s better to have a higher accuracy can test out all combinations and get a pretty decent solution!
I mean, it won’t be better than the one developed by a senior data scientist with 10+ years of experience, but ask yourself – how important really is that 2% increase of accuracy?
Try to think of this both as an employee and an employer. Is the marginally better model produced by a team of data scientists actually worth the time and money? It is, for some companies, but most of them will be perfectly fine with what "enterprise cloud environment" spit out.
3. Job Listings are Just Awful
Go over to your favorite job listing website and search for data science jobs. You’d expect skills like SQL, Python, R, Statistics to pop up – and you’d be absolutely right to make this assumption. The problem is, those will be only 30% of the requirements!
Some others may include Programming in general, APIs, version controlling, and maybe even some frontend skills. And that sucks for you.
Even if you are coming from the software developer background and have all of the mentioned skills under your belt, there still isn’t that jobs in data science.
Let’s take a look at listing from Indeed.com, taken on the 16th of May, 2020, limited only to the United States:


Now that’s a big difference.
You might be thinking now that software development is much broader than data science, hence more jobs are available. That’s absolutely true, and that’s why I’ve also searched for a narrower field in software development – Java developer. Let’s see how many job listings there are:

Yeah, around 3 times more.
But even if this doesn’t convince you enough, the following few words most certainly will – most data science positions are senior.
That’s right. Most small and medium-sized companies don’t have the same need for data scientists as they have for software developers. Maybe they are even searching for their first data scientist! Do you really think they would hire an intern or a junior to handle the data science-related work? Think twice.
The Verdict
Maybe I came a bit too harsh on data science. Maybe the situation is different in your country. But maybe it isn’t – and you should be aware of both pros and cons when making a decision this big.
In the world where machine learning is automated to a degree by which regular software developers feel confident enough to use it, I’d think twice if I was to choose my future profession today.
Don’t get me wrong here, data science is still awesome, but just be prepared to spend around 90–95% of the time on the ETL, data processing and preparation, and the other 5–10% doing what really interests you (predictive modeling).
What are your thoughts? What’s the situation like in your country? Let me know in the comments below.
Thanks for reading.
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