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Data Science: Viable Career? (2018 & Beyond)

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the link the description below. Hey, what’s up? John Sonmez here from 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 sciences and your view
of it as a career path. Do you think it’s a good path for a freelancer
or 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 the data scientist
myself. Oh, I was just looking for my coffee. I need some coffee, 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 data science honestly like it’s too, too broad to say data science? It’s sort of become a buzzword and people
like to say data sciences, but what we’re really after here is doing—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 this, is that it’s far too broad to say this like 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 the—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 the 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, what you’re going
to do is 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 what you’re going
to need is 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 real 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. All right. That’s all I got for you today. If you have a question for me, you can email
me at [email protected] or you can just subscribe to the channel and, probably,
I’ll eventually answer your questions because I do like two to three videos a day. All right. Click the bell so you don’t miss any videos. I’ll talk to you next time. Take care.

38 Replies to “Data Science: Viable Career? (2018 & Beyond)”

  • Data Science is already broken 🙁
    Sadly knowing how to code it isn't enough. And this is a sacred war (just like the eternal war on formal education vs online/bootcamps in CS?).
    Data science, it is not just about cleaning data, ploting trends, calculate some features and putting all in a prediction model. There is no any science on that. A Data Scientist that doesn't know how to DO SCIENCE, it's going to fail. It's just like any other developer (frontend, backend, etc).

  • Coding dojo will make a data scientist out of you in only 3 months!
    1. IQ over 130.
    2. Low sex drive, low appetite and no social circle so you can look at numbers for 14 hours a day.
    Or just be asian.

  • I'm actually working up to become a Data Scientist . Here's my experience about what technologies that I see are in demand:

    R programming, SQL, python and maybe some C++ (plays nice with R Studio).

    I Recommend learning Tidyverse (R) and Pandas (Python).

    With these tools you have the data cleaning and visualization covered.

    Best wishes to anyone making the jump to this field.

  • I have studied machine learning as taught by the school of computer science and data analytics as taught by the school of statistics and it is very different.

    Being able to write a machine learning algorithm from scratch has zero to do with data science. Data scientists use libraries and tools. Their primary skill is not coding, it is statistics and probability.

  • I have a choice between either applying for Computer Science or Data Science for university (I’m in the uk so we have to pick our major before we apply and we can’t change it). I’m pretty good at maths and I take all the further maths classes in my school, but I don’t know what it would be like to be a data scientist in the industry, whereas I have some idea with software engineering but I’m still not sure if I’d like it.

  • John, can you make a video about specialization in ML and AI engineering? I think the majority assume this fields when talking about Data Science.

  • Your advise on "choose something before others do", is the best thing I've ever learnt from YouTube. Cheers mate! (Y)

  • I think I fuckin love this career, the moment I just discovered it. It seems so fuckin interesting. But I fuckin hate math

  • Only issue with this field is that Investment Banks/ Private Equities / Hedge Funds are having issues with MIFID II which can lead to more consultants for DS rather than employment.

  • I currently work as a jr.Data Scientist at IBM, and have a masters in Computer science. This job always asks of you be learning and challenging to work on every project independently.

    The work involves working with Big data of companies in Medicine, manufacturing, and observing patterns for development that leads to increase in companies business model and also become better serving customers.

    This involves heavy applied mathematics , programming skills with python or spark so it scales better for deployment with end making profits maximize.

  • Personally I'd say that the best data scientists (i.e. 'unicorns') are those who are proficient in multiple areas of the subject. Specialisation is important to a degree (as with most things), but to be able to appropriately address a broad range of data problems accurately, I don't think you can specialise too much

  • Ok . First thing you should realize is , data science is not software engineering . Yes , you need to code – but that’s about it in terms of similarities . Yes , it’s always handy to know python / R , but that’s almost imperative. You’re not learning any technology here the way you’re learning in software engineering . You need three things:

    1) Statistics and ML knowledge
    2)Decent business acumen.
    3) Coding skills .

    There is no parallel of data science and software development. Machine learning however , is very similar but the objective is very focused and probably doesn’t require as much business acumen.

  • I’ve been a quant, software engineer and architect for years. I’ve worked money Carlo , Black n scholes, Cox Rubinstein, rsi, Bollinger bands, stochastics, svm , random forest, etc and I’m not still not considered a data scientist because Is not my job title. Then a newby comes and call himself a data scientist and he’ll be considered a data scientist because he decided to call himself that way

  • What I'm specifically interested in is deep learning and the future of AI – Autonomous cars/Search & rescue bots/ world hunger/poverty solutions. I feel like AI is a great tool to help us there, what do you think?

  • working with data covers tons of areas such as sql developers, reporting analysts, data goverance, data modeling, data warehousing, report developers ( tools such as cognos, tableau, SSRS etc…) and deeper analytics using math and stats and those tools include python, R and SAS. Tons of different areas to go into

  • Very soon i Will be a complete computer Science Engener, BUR am interest in Data Science das why i attempt to tesis is My statistic professor, but i know ain't enough cuz i take my tesis as an intro to the DS industry
    It wiil be better to do a PhD in Statistic After i graduate o a 1yr Master in Data Science &Big Data?

  • Nice video. Anyone looking to get into data science should really get into data analytics using something like Python or R first. So much of the work is basically data analysis so it makes sense to test the water and develop the fundamental skills in something that it is 100x easier and has many more jobs for all levels. Use data analysis as a stepping stone into data science.

  • I'm taking computer science specializing in artificial intelligence. It seems like data science is a superset of statistics. It's basically statistics with more coding. CS artificial intelligence covers a broader topic other than machine learning such as boolean satisfiability, constraint satisfaction, pathfinding, tree traversals, etc

  • Hi, i have a bachelor in engineering, and i don´t know nothing about computers or programming! You just talk and tlk and talk and din´t say nothing in the end! It would be best if you don´t talk at all, next time!

  • Great video! Completely agree that specialization is really important within the field. From my experience as a data scientist and manager, people that specialize in high demand areas like computer vision and NLP can earn significantly more than generalists on the job market. Even getting really good at a specific tool like Tableau can result in tremendous dividends.

    Definitely still learn the basics! Happy to answer any questions, as I have a decent amount of experience in this field.

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