It’s all over the place.
It seems that we are in a Data Science era. There hasn’t been anything this popular since perhaps the iPhone. It makes you instantly wonder, “should I get into Data Science?”n
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If you don’t believe me, take a look for yourself. n
After a couple of quick Google searches, or scanning top news articles from the past couple of years, you’ll find the following headlines all over:
“Data Science – the Sexiest Job of the Century” ,“Data Science is the Job of the Future”, “Why is Data Science so Important?”, “Why do you need Data Science?”, “Is Data Science the next big thing?”
All have the power to point you in a similar direction – that Data Science is the way to go. n
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However, there is an old adage that is very valuable in these moments:n
“All that shines isn’t gold.”n
For a field that seems to have everything positive, is it really that good? Can you trust something that everyone is claiming to be “the way to go”? n
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This is what we’re aiming to do here; solve the mystery once and for all. Should you get into Data Science?
So stick around, and we’ll give you an objective look from the inside as to:
- Whether Data Science is worth it,
- Should you Get into Data Science,
- and some Food for Thought.
Data Science – is it worth it?
Let’s get some facts out the way which you’re probably familiar with:
- Data Science is here to stay
- It can be applied in a wide range of industries
- It pays well (very well, actually)
Data Science is here to stay:
Human beings are producing approximately 28,000 Gigabytes of data. n
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Every minute. n
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Here is a quick chart depicting how much information that really is:
For those of you who remember, only a couple of years back, when a 3 Megabyte song was enough to take over the whole Internet for a day, you’ll see just how crazy 28,000 Gigabytes of data is.
With this in mind, it is clear to see that data and Data Science are not going anywhere.
Think about it:
We are already collecting this vast amount of data; what can we expect from the future?
The answer: more data.
These trends will only continue to grow, possibly never stop and simply evolve into a new form of itself.
It can be applied to a wide range of industries:
Some technologies are tied to specific industries, such as Netflix and the technology behind it. It may seem like it was an overnight success, but the truth is many factors pitched in to allow Netflix to become the giant that it is today. n
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Netflix was only possible once streaming technology become available, a good enough cloud to hold up high quality movies became cheap enough, trust in recurrent and online payment by consumers, to name a few. You get what we mean. n
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But this means that probably streaming will be somehow tied to video related industries. Fortunately, Data Science is, once again, the exception to this rule. It is fortunately not tied to a specific tool, (there are several alternatives to even accomplishing every single thing), but also this means that it is not tied to a specific industry. n
As mentioned previously, the coming of the age of “all things data”, means that more and more pieces of technology, industries and fields that didn’t previously involve data, will.
What does this mean?
This means that even though might have initially thought Data Science was solely a “tech” field involving highly advanced programming, dominated by analysts, then think again!n
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(This is really the beauty behind Data Science.)n
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Data Science can (and should) be applied to as many industries as possible. n
How?
We’ve been talking about Data Science as a whole, but in reality it has several subsections that you might have heard of before. These include, Machine Learning, Artificial Intelligence, Computer Vision, Chatbot, and Tableu. Take a look at this graphic to see what we mean:
The wonder of it all is that each of these subsets of Data Science is a world of its own. You could, hypothetically, dive into each, submerging yourself in mountains of information and application, for example:
- Machine Learning is the backbone behind speech and image recognition.n
- Artificial Intelligence is behind virtual assistant technologies such as Siri.n
- Computer Vision is responsible for Tesla’s self-driving cars and drones. n
As you can see, the possibilities seem endless, so if you want to get into Data Science, it mostly comes down to how creative you and thorough you really want to be.
A piece of better news is: we are just getting started.
It pays well:
What is the average pay of a Senior Data Scientist?
According to Glassdoor, $141,000 dollars.
A Junior Data Scientist?
According to Glassdoor, $104,000 dollars.
True, there are few jobs which can claim salaries of this calibre, but we want to make something clear:n
Data Science is not a magical field that pays a huge amount of money to whoever jumps in. n
There are a couple of reasons behind these paycheques: As mentioned by Forbes, one of these is: supply and demand.
Data Science is no exception to the most fundamental laws of economics: high demand and small supply will be reflected as an increase in price, with the opposite standing true. n
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Therefore, at this precise moment, it’s safe to say there is quite a substantial demand for Data Scientists with few currently available. Naturally, the “price” is driven up for the role. n
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However, this is changing as we speak. n
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More and more people are getting into Data Science. They are seeing the potential and impact that the field can have on their career, future, and even current businesses.n
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This doesn’t mean that you are not in time to join the party. In fact, it might be the right moment. n
The right moment?
Correct.
Generally, there are several moments when you could “enter” into a new field – these have been categorised in different instances by the “Technology Adoption Cycle”.
This model works for most industries and products across the board. n
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Innovators are those who see potential before any tangible manisfestation. The Early Adopters use new products or technologies as soon as they come into play. Early Majorities are a little more cautious, waiting for trends to transform to movements before hopping on board. Late Majorities essentially are the same, just they join in, as you expected, a little later. Finally, the Laggards will come if they feel the pressure to do so. n
The good news here is that Data Science is still in its early stages. n
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We are no longer in the risky part of the trend; Data Science has proven to be a field that is growing and living up to its potential. n
If you’ve been feeling a bit hesitant about joining Data Science as a field, right now there’s a beautiful mix of relatively low risk and huge potential.
Food for Thought
We want you to take something with you after reading this post:n
Data Science is, in fact, important.
Fortunately or unfortunately, data is all around us, and it is here to stay. There is really no going back on this, so you can either take an active or a passive stance towards this fact.n
The cold hard facts are that there are tons of benefits of getting into Data Science.
“Will it be hard?”n
-Probably.n
“Will I have wished I’d started a long time ago?”n
–Probably-ier.
But there is no better time than now to start.
The good news is that there are many ways in which you can get started in Data Science, there is no right or wrong path to it. Whichever path you choose might be the right path – so don’t worry: just get started.
Think about it:n
There are tons of things that you can do to ease into this fantastic field. The only thing that you have to do is: try. n
We recommend starting by listening to people already doing it; while having your morning coffee or whilst on your commute check out this fantastic episode by Nicholas Cepeda (only 49 min long): n
Here are a couple more of the best episodes we’ve picked out, if you want to dig in a little deeper:
As you’ll see, anybody has the potential of becoming a Data Scientist, being happy while doing it, and being handsomely paid at the same time. n
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So what are you waiting for? What’s the harm in trying?
The worst case scenario is you’ll have learnt about something amazing that is happening all around you. The best case? You’ll fall in love with something thousands of us are already passionate about.