Data Science: Viable Career? (2018 and Beyond)

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Data Science: Viable Career? (2018 and Beyond)

Does it look like a good option to be a data scientist from 2018 and beyond? What can you expect from this career over the next few years?

· Big Data Zone ·
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Data science is definitely a field that is rising in popularity and some software developers are starting to pay attention to it.

Essentially, data science is about using the massive amount of data that organizations are collecting to gain new insights, identify trends, and find ways to streamline business practices. When you consider that in 2020, the world will generate 50x more data than it did in 2011, it's no surprise that an entire discipline has grown up to help people make sense of it.

That being said, does it look like a good option to be a data scientist from 2018 and beyond? What can you expect from this career over the next few years? What practices can you implement to take the best out of it?

Watch this video and find out!

Transcript of the Video

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Hey, what's up? John Sonmez here from simpleprogrammer.com. I have a question for you about data science. Yes, I'm actually going to be talking about something programming-related today. I know that shocks some of you as this channel is moving more and more away from programming topics. Oh, my. What shall we ever do? To be addressed at another time.

Anyway, I got this question from Aaron and he says: "I'm wondering about your thoughts on the future of data science and your view of it as a career path. Do you think it's a good path for a freelancer or do you believe it's a path where one would generally be an employee? Lastly, do you think it's small enough of a slice to specialize and/or do you believe it's too broad of a field? Thank you in advance."

I have to admit, I am not a data scientist myself. But I will tell you that I have friends in data science and it is a growing field. It is very broad. When we see data science, what do we even mean by, honestly? It's too, too broad to say data science. It's sort of become a buzzword, and people like to say "data science," but what we're really after here is managing a large amount of data and doing analytics on it, and manipulating that data, which is something we've been doing in the software development industry for a very, very long time. I mean, do you remember databases and cubes? We've been doing this for a while. We've just been doing it more and we've had more data and we've been working with larger-volume things. There's been some specialization in that, technologies like Hadoop and other data, ways of visualized data technologies that have come out. I'm not going to name names of companies, but the point is that it's far too broad to say this when you're asking this question — I think maybe what you're saying is I'm interested in data and working with data. Valid.

Can you be an employee? Can you be a freelancer? Yes, because it's so broad, right? I mean there's plenty of roles for any of those things and those roles are going to diverge more. As we figure out more and more of the ways, and this is just my opinion, of how we're going to deal with the huge volumes of data that we have and how we're going to process those, then I think more specialization will evolve, but there's already specialization there. Right? What I would encourage you to do is, I would say this: data science is great. I think working with data is always going to be something like — we're always going to have it. It's only going to grow in demand, but you got to figure out what kind of data and what kind of manipulation or reporting or analytics. Right? In that realm of working with data, in that realm of data science, what are you picking out and what are you doing?

This is more important because when we say programming or software development, it's — I don't know. Yes, there's a lot of differences there, but typically, people say — well, at least they divide things by — "I'm a C# developer, I'm a Java developer, I develop in PHP or Ruby, I do web development." We have those things, but I think in data science, it's still early enough in the evolution of this larger concept that we don't have as many of those already predefined. It's up to you to go and figure out how we're going to use data, how we're going to use it in your work, what do you want to specialize in, and you're probably going to have to pick some technologies and some tools and some ways of working with it. That's the best thing to do, right?

I mean, if you want to be the highest-paid and have the highest number of options, both freelance and career-wise, you're going to pick a particular technology stack that you're going to specialize in. Yes, you need a broad base knowledge, but you need that — remember, we talked about this T-shape knowledge, where you're going to need somewhere where you're going to go deep, so pick some kind of tool. Pick some kind of data platform. Look in that space of working with data and see what kind of tools, what kind of things that you want to work with, what kind of technology, what kind of manipulation language for data, what kind of technology are you going to specialize in, and pick that and go deep there and get a really good understanding.

Build a blog. I've got my blogging course. You can check out here and talk about it. Maybe create a YouTube channel and do YouTube channel tutorials on it. Specialize very deep in that specific thing and that's going to give you the biggest benefit. This video might as well not be about data science because it could be about anything because this is what I tell you guys. I've got a whole specialization playlist which you can check out, but you have to figure out how to specialize, how to have a deep knowledge so you can be an expert. I've sort of upped the ante lately by saying that you should pick something that you can be number one in the world at and you can. Everyone has an opportunity to be number one in the world at something, some slice of a thing mostly because most people won't even try.

If you just pick a small enough slice, then you can build that. You can always branch out from there, but pick something and just be the best. There are so many fields of studies, so many points out there, so many technologies and branches of the technology that you can pick something that you can go deeper than anyone else does or that very few people in the world go that deep. If you have that expertise and people are using that technology, you'll be able to get a job. You'll be able to work as a freelancer. You'll be able to build your own business base on that. These are all good things. Being a generalist doesn't help you. Don't use data science anymore. I want you to focus and tell me exactly what kind of data sciences that you want to be, what kind of tools, what kind of technologies, what kind of data that you want to work with. Even pick the industry. Be that specific and you're going to have the best outcome.

big data, career, data analytics, data science

Published at DZone with permission of John Sonmez . See the original article here.

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