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AbdulMajedRaja RS

7 minute read

The madness called “Data science (Gold) Rush”

Java

When I was studying my Computer Science Engineering — almost a decade back, I found many of fellow students in doing java courses and getting SCJP (Sun-certified Java programmer) certifications. That was the time when java ruled the world. At least based on the market trends. Streets with colleges were filled with Advertisements from Training Institutes for Java Certifications and Computer Networking courses. I felt like Madness during Gold rush and it seemed like that java was the only key to unlock secrets of life’s treasures. Engineering Students were behind it just because java as buzzing around and the herd mentality took them there.

Data Science

Today (2019), that java == data science . It’s very much like there was class madness_gold_rush and the whole world inherited that class properties to this new object data science. Pardon my poor object-oriented skills.

Today, History repeats. Streets in Bangalore (Supposedly, Silicon Valley of India) are filled with ads for Data Science courses. Python, R, Scala, Tensorflow, Pytorch, Hadoop, Spark — you bring it on — it’s all there in the banner ad stuck on the wall.

Well, very recently Data science doesn’t seem to be that charming so companies have started using Machine Learning and Artificial Intelligence in their marketing copies. YouTube shows me Data science ads from Datacamp, Upgrad, GreatLearning (Can someone tell their stupid algorithms that I’m a Data science trainer myself). Not just once but a heck lot of times that these days I’d prefer to see the Trivago guy ads whose repetition used to frustrate me once. Multiple Data science meetups in one city alone. Multiple Data Science conferences (with tickets even hitting more than 15K INR — which is around the monthly salary of a lot of Fresh graduates even today).

Linkedin is filled with Data Science gurus spending most of the time talking about Who’s a real data scientist — some says, The Math is the core and some else says, The Code is the core and a few others say — writing Algorithm from scratch is the way to go — multiple such definitions that it started its own fun thread of deciding a Real Data scientist where someone mentioned a real data scientist is who

If that’s not enough, there are tons of thought-pieces (I myself have written a bunch) of how to get started as a data scientist and so on.

With that, now in 2019 — data science has been *marketed* as the key to unlock the secret treasures of life. College Graduates are falling for it. Programmers in other technology are falling for it. Companies are creating crazy openings for it. Institutions and Training centers are making money off of it.

Bottom line, nothing has changed in the Education & Professional Landscape after a decade. The same herd mentality of crazily going behind where the crowd is going is still going on.

Economics

Before we move, let’s talk about a little bit of Economics. Do you know the price of Tomato increases during rainy seasons? Yes, It does. The reason is the supply-demand imbalance. Too little good Tomatoes (Supply) available in the market, while too many people wanting good Tomatoes (Demand).

Now, you’d probably heard every other graduate telling you that getting a Data science as a Fresher is difficult. Couple that statement with the above Economics. Indeed, It’d be difficult. Why? The answer is

“The Madness called Data Science Gold Rush”

The huge surplus supply of fresh graduates and experienced professionals wanting to jump into the so-called sexiest Data science job and the moon-shot expectations of Companies reading these thought-pieces finally end up in most supply not being used while the demand being high to let more supply inflow.

In Simple English, It creates Chaos — costs life and Time — of innocent Graduates.

Key

Let me state a simple fact:

Data Science is definitely not the only key to unlock the secret treasures of your life.

But you might ask me, “Hey, but that’s the job opening we see everywhere?”. That’s a good point. That’s probably because:

If Corporate, the company is either trying to advertise that they are Data-driven or really yet to figure out when and why to use a Data scientist.

If Startup, the company is trying to raise a bunch of money from VCs and Data scientists are nice showcase pieces for Marketing, PR and fundraising.

Does it mean, Data science roles are fake? No, I didn’t mean that. Consider yourself a startup building the next Flipkart — If you need 3 web developers to build and deploy different modules of the site, would you hire the same number of Data scientists to analyse the data generated?

Now, You would ask me, “What-if there’s BIGGGGG data (that’s really very big data) ?”

That’s for the next section.

Tipping Point

AutoML — Accountants got Accounting software that reduced the number of required Accountants for Book keeping. The same way, Automation is going to make a huge impact in the world of Data science and Machine Learning. You can deny and say “Are you stupid? Computers can never be as good as humans”. That’s what we’ve always been saying for every job. An account couple of decades would have said the same but today that’s no more real. I’m not saying that AutoML is going to replace Data Science. I’m saying AutoML is going to drastically reduce the number of required Data scientists because Domain experts are going to be powered up with AutoML tools. Google Bigquery has got ML features with SQL syntax, what more proof would you need?

In simple English, You don’t get to see MS Office openings very often. Because MS Office has become so ubiquitous that we all use them. I hope someday AutoML is going to do that.

Now, You would ask me, “What exactly your suggestion is? “

That’s for the next section.

Data Science + X

Data Science isn’t definitely going to be a standalone domain as its set up in many organizations. Data science will tightly get integrated with Business. Like, Marketing will have its own Data scientists, Supply Chain will have its own Data Scientists, Testing team will have its own Data Scientists. After all, that makes a lot of sense because you will be more agile when you analyse data at the point where its generated than channeling it to someone who has no or less clue about its data points.

So, the suggestion is — Learn Data Science as a nice auxiliary skill. But gain a strong hold of something else. Don’t jump into the madness like everyone else is trying to.

You might ask me, “But Andrew Ng has said “AI is the new Electricity”, Do you know more than him to deny him?”

No, I definitely don’t even stand anywhere closer to him and No, I’m not denying him either. I also believe AI will transform our world. But if AI is the new Electricity, You don’t want to become a wireman, rather you should become someone who invents Electric Bulb. To take a page from the Internet History, You may not want to become the guy who puts cable across your campus, rather you should become a Hacker like Mark Zuckerberg who can play with Technology and build something valuable that can impact Humanity.

In Data science context, You could be a hard-core Web Developer and with a mediocre data science knowledge — now, you could simple use a pre-trained model for mobile phone and create a new disease identification app or something for people in need.

Be Application-driven, Not Technical-driven

Other Professions:

This section is dedicated to list out throw light on some other jobs.

  • Web — Front-end, Back-end, Full-Stack Developer
  • App Developer
  • UI/UX Designer
  • Developer Advocates
  • Program Managers
  • Product Managers
  • Customer Success Representatives
  • Service Reliability Engineers
  • DevOps Engineers
  • Data Engineers
  • Cloud Architects

 — these are some job roles from the top of my mind that Computer Engineers (or those want to be in CS) can purse, but there are much more like this. Explore!

If you agree on what I said, Like and Share — If you disagree on what I said, Comment your thoughts of disagreement, Let’s discuss!

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