
Ask HN: Is it a waste of time to teach yourself data science without a degree? - thewarrior
I see a lot of people teaching themselves data science and machine learning but it seems that in the real world you won&#x27;t be allowed anywhere near such a position without having a degree in the subject.<p>This is opposed to regular programming gigs where you can get work based on a portfolio.<p>Also there are efforts to commoditize common methods and algorithms by wrapping them up in APIs and SDKs.<p>So is it a waste of time to learn it on the side with the hopes of getting a data science job ?
======
itamarst
(copied from answer to another similar question.)

Companies are looking for what you as a candidate can do for them.

Self-study or taking a class signals some level of "I tried to learn this
thing." So that's a start.

Even better is "I built X", where X is obviously based on skill you learned.
In which case you can omit the class because you have proof of learning, not
just trying to learn.

Even better is "I provided business value V to my employer by building X."
Because now you're showing how this skill is useful to someone else. So using
skill at work is another thing to try.

Ideal is you write the above, but emphasize V (or choose between multiple
things you can list) in a way that suggests you can help the needs of the
particular company you're applying to.

So there's having the skill (which is good), but there's also how you present
it to show it will provide value (also important).

More on the contrast between having engineering skills and marketing yourself
here: [https://codewithoutrules.com/2017/01/19/specialist-vs-
genera...](https://codewithoutrules.com/2017/01/19/specialist-vs-generalist/)

~~~
rdudek
This is great, however, the biggest roadblock might be the automated HR
application scanning. As a victim of this, I'm back in school to finish my
degree that I started 17 years ago.

~~~
klibertp
How is it going? How do you do it?

I'm in a similar position right now, but every time I think about being
subject to the bureaucracy again I shudder and choose to write interesting
blog posts instead... It's not about the costs or the time; I feel like my
dignity as a professional (and possibly even as a human being) would be
threatened. It could be because of the way the higher education is set up in
my country, though.

~~~
thatcat
>my dignity as would be threatened.. Bc thr way higher ed is set up in my
country.

Would you mind elaborating on this?

~~~
klibertp
In short, public universities and colleges have more than 50 years of history
operating in an almost communist, authoritarian state. Private institutions
are still widely considered to be worse than public, and the latter still
operate on a presumption that students should be happy for just being accepted
(and ripped off - sometimes unlawfully, as the public higher education is
supposed to be free). In reality, when we chose to go all democratic in '89,
the amount of money sent to the institutions diminished, while any change in
their mode of operation was resisted, for 25 years and counting, so things
only get worse since then.

It's hard to imagine without experiencing it firsthand. How could it be that
big of a deal? But it is: you're treated not as a customer, not as a partner,
but as a lowly supplicant in all your interactions with the institutions.
You're expected to just "put up with it for a while" (like 3 to 5 years...)
and drink some vodka if you get frustrated a bit too much.

------
imh
I have this same non-background and work on the proverbial team of mostly
PhD's. Short answer is yes, you can do it. Long answer is that you have to be
really, really good to compensate, and getting to that point is absolutely
exhausting. It's not about just going through a couple ML courses on coursera.
You need to understand statistics, CS, and ML at a really deep level, and that
means being good at applied math too. I was lucky to come out of physics and
have a solid applied math background anyways, giving me a few years head start
on that self study.

If you need structure to go through a few years of coursework on your own, you
should go for the degree. If you just want to learn how to put pieces together
and not learn how/why they work under the hood, you should opt for something
else.

As with most questions about going nontraditional routes, you have to be
really good to compensate, and getting really good is constant exhausting
work.

------
endymi0n
Don't have a degree myself and about a third of the people I hire also don't
have one. Why? Because I don't give anything about them.

I'd say if you don't want to work for a large, respected company _first_ ,
it's a waste of time. Your degree is your entry ticket to your first job, not
more. Later on, you can even work at Google if you want - just make a great
product and get acquihired.

Three tips on what you should do instead:

1) BUILD something and show off your skills. Like, continuously. Always have
your own challenges, do something about them, put your code online on Github.
Host it so it can be seen and played with. Work towards a goal and learn what
you need to learn on the side.

2) Focus on applying to companies not listing a degree in their job ad. You'll
see there are quite a lot of them.

3) Don't focus on your lack of a degree in any interviews. Don't deny it, but
just don't make it seem a deal. Often times, people won't even ask.

~~~
cr0sh
> I'd say if you don't want to work for a large, respected company first, it's
> a waste of time.

I wouldn't necessarily say that.

I don't have a degree (well, an Associates that ain't worth much). I've been
employed as a software developer for 25+ years now (since I was 18 years old).

I wish I had pursued a degree, though.

Back then, when it came to my education, I was pretty lazy - at least when it
came to more structured learning. I liked to pursue stuff on my own, though,
at my own pace. I've done well in that manner.

In 2011, I "discovered" the idea of a MOOC: I took Andrew Ng's "ML Class" \-
and successfully completed it. That led me Udacity's CS373 course in 2012 -
which I also completed successfully.

That isn't to say I didn't struggle with both of those: I had no experience
with probabilities and stats, and I hadn't touched linear algebra since high
school. But with the help of resources on the internet and elsewhere (along
with help from others on the internet, and fellow classmates), I managed to
complete both successfully, and I learned a lot in the process.

Last year, I started Udacity's Self-Driving Car Engineer Nanodegree. Today,
I'm working on term 2. We're dealing with localization - basically learning
SLAM, which was covered in the CS373 course, too. Prior to that, we learned
about how Kalman filters (standard, EKF, and UKF) all worked to integrate
sensor data. In the first term, as part of one of the projects I implemented
NVidia's End-to-End CNN to drive a virtual car around a track.

All of these experiences, and others outside of all this, have taught me that
perhaps I cut myself short by not pursuing a degree when I was younger. My
current plan is once I finish this Udacity course, I'm going to get my BS
online, then work toward an MS in comp sci. It isn't a matter of "I think I
can do it" \- I know I can do it. It's more a matter of absoluting proving it,
and likely learning a lot more along the way.

I don't think a degree is a waste of time, unless you intend never learning
more stuff as you "grow older". If your only goal is to "make money" and all
that, maybe it is. To me, though, had I gotten my degree back then, I believe
I would be much, much further along today. I can't change that, though - so
all I can do is move forward.

~~~
aws_ls
>Last year, I started Udacity's Self-Driving Car Engineer Nanodegree

Same. I am also doing the course, just finishing term 1 (in the Feb cohort).
Although I have a degree, taken around 23 years back. But I think, its the
_always learning_ attitude, thats more important.

As of now, I'm not exactly sure, as to how I can utilize the learnings in
CarND nanodegree, but I'm enjoying learning about stuff, all the same. Had
never coded in Python before I started taking Udacity courses, so learnt
Python along the way, and that's just one of the several things.

For me, I find, I am not satisfied with knowing some things at a high level.
Until I'd started taking these courses, I'd always get confused between the
terms AI/ML/DL for e.g. also other buzzwords would be learnt in some vague
way, and soon forgotten. Now the minimum value-add of this, is that that I can
distinguish between BS/hype or not, when read articles on ML/etc. Also have
advised some friends on what it can do and what it can't. That's a big
achievement by itself to me. And lastly although, I am pursuing my own
startup, so not actively looking for a job, but if some opportunity arises, I
will seriously consider it. Because, heck, why not?

------
quadrature
A good programmer with even just a high level overview of ML and Stats
concepts would be an incredibly valuable asset to a data science team. Most ML
people are academics who tend to not have good software engineering skills,
finding people who master both domains is really hard.

Also to add to that most of the work in ML is feature engineering, data
cleaning, testing and building pipelines which all require a good software
engineering background.

~~~
Helmet
Really? I don't know if I'm good, but I think I'm at least a "decent"
programmer and I have a solid grasp of ML and Stats concepts and I've had
absolutely no luck getting interviews, much less call backs for data science
jobs.

~~~
quadrature
If only you were willing to work in Canada :P.

~~~
colmvp
Have you seen the job listings for data scientists in Canada? It's paltry even
here in Toronto compared to parts of the U.S. like SF which have a fraction of
the population. Last time I checked, when I searched Data Scientist in SF
there were 3000+ listings while in Toronto there maybe a little over a
hundred.

And of the few job listings I've seen, most have high standards (PhD or min.
Masters, x years of experience) with old companies (banks, car companies).

What's funny is that a lot of people in my circle in Canada are actually doing
work for companies outside of the country (U.S., China...)

~~~
quadrature
I was speaking in context of the company i work for. You're right but we're
getting there.

------
jorgemf
You can get a job with a portfolio in data science. Just go to kaggle and beat
everybody in all competitions. That is worth more than a degree. Companies
will try to reach you if you can do it.

But, honestly, I think it is very difficult to learn data science by yourself.
Someone with experience teaching you will make a huge difference. Data science
is different than programming as in programming you can see step by step what
is happening, in data science most of times it either works or doesn't. And
you know it after your algorithm has run through all data for at least an
hour. It is really hard to learn this way, you need hints that only someone
with experience can provide to you. Moreover you can do a lot of mistakes
without knowing it, for example, when cleaning the dataset people use the
whole dataset to fill gaps and them split it for training and test. It feels
right but that it is a huge mistake that invalidates the whole experiment
(because you use information from the test set in the train set, to fill the
gaps).

------
NumberCruncher
It depends on how you define "data science".

If you are like AWS and say that using logistic regression is machine
learning, then yes, you can teach yourself data science. Learn SQL, read a
couple of books on logistic regression, use some open data for building a
couple of models. There are many companies where you can have a decent job and
an easy living with SQL and logistic regression on your tool belt.

If you say that data science starts with automating stock trading or building
the intelligence of self driving cars, than no, you can not teach yourself
data science. You will need at least one degree. Or more.

~~~
StavrosK
> no, you can not teach yourself data science. You will need at least one
> degree. Or more.

Why not? What is it that prevents anyone from learning anything without
getting a degree? I disagree with your statement, I think it might be harder,
but I don't think anyone "cannot teach themselves X".

~~~
haggy
I agree with parts of both statements. On one hand, if you're looking to
seriously get into data science then it's going to be hard to even get
interviews with companies that are looking for real data scientists without
those pieces of paper (diplomas). On the other hand, I agree that with enough
dedication and effort you can teach yourself anything (after all, university
is largely just you teaching yourself with the help of a schedule set by an
institution).

~~~
gorbachev
My company is hiring data scientists all the time. Nobody looks at the
education section of the resume of the candidates we get. We look at what the
person has actually done, and then we interview to make sure the resume wasn't
filled with lies about that experience.

~~~
StavrosK
That has been my experience as well. Not a single person has ever asked me
"what's your degree", but everyone looks at my projects and past work.

------
dagw
Non of the data scientists I know actually have a degree in data science. They
tend to come from either a physics, math or statistic background and have
picked up the data science bits of the side.

Also many jobs that aren't data science jobs per se offer many opportunities
to do data science type things. Get a job at a company that works with a type
of data you find interesting, and that perhaps doesn't have a dedicated in
house data scientist, and every time an interesting data related challenge
shows up just go "I have a good idea on how we can approach this" (assuming
you actually do). Next thing you know people will coming to you with their
data science problems and before you know it you have several years of data
science experience on your CV.

------
ChemicalWarfare
>> in the real world you won't be allowed anywhere near such a position
without having a degree ...

yes, most likely they won't hire you for a "Data Scientist" position, but
there are related jobs out there you can be qualified for if you have
programming skills and understand DS stuff to some degree.

I've seen setups where a PhD with a "scientist" in his title would act as an
architect/co-team lead with a senior engineer running a team of developers.

Someone has to implement DS' ideas after all and unless we're talking a really
small team (or a jack of all trades DS) where DS has to write all the code
himself - there is a need for developers with "some DS background" in those
situations.

------
jey
> _it seems that in the real world you won 't be allowed anywhere near such a
> position without having a degree in the subject_.

I don't have a degree but work as a data scientist at a research institution.
I'm self-taught and was originally hired as a software engineer on the basis
of my projects and work experience.

It's true that you have to convincingly make the case for your competence, but
a bachelor's degree is really at best a certificate of minimal competency in a
subject. Its signalling[1] value quickly gets swamped out by actual work
experience where you're continually learning and improving. So there's a great
hack: just do actual good work and put it on your resume. Your portfolio of
work should convey your competence so well that having a degree wouldn't
really add anything. (So you can skip the degree, but you'll still have to put
in the work.)

Remember that any healthy organization wants to hire for competence at job
duties. If some company rejects you for not having a degree because the hiring
manager has to cover their ass to upper management instead of optimizing for
getting work done, you should really just be glad that you dodged a bullet.

I think what's most important is to keep growing and learning. Pg had it
right: "If you're worried that your current job is rotting your brain, it
probably is"[2].

1\.
[https://en.wikipedia.org/wiki/Signalling_(economics)](https://en.wikipedia.org/wiki/Signalling_\(economics\))

2\. [http://www.paulgraham.com/gh.html](http://www.paulgraham.com/gh.html)

------
jtcond13
Writing a full reply since I don't agree with much of the advice given.

I've worked around/in data science teams at a large BigCo and I think that
you're far overestimating the bar here. There aren't enough people to who can
write data pipeline code (SQL/Shell/etc.), much less implement and
intelligently explain statistical/ML models. Also, the average decision maker
here does not understand the difference between 'created model in Pandas' and
'created model with Amazon's ML API'.

The modal background of data scientists in industry is closer to 'Econ BA +
knows Python' than 'Artificial Intelligence PhD'. Moreover, the former will
still enjoy a remunerative career if (s)he's sufficiently savvy about
identifying problems and showing off how they can be solved with technology.

There may be a point in time when companies can't get a return by throwing
math-savvy programmers at a problem, but that will be long after you and I
have passed from the scene.

------
nilkn
I don't think it's a waste of time. Even if you can't straight-up get a pure
data science job, you can still benefit from having this background:

(1) You could focus on building data processing platforms using, e.g., Spark.
This will get you very close to the data science folks and you could probably
end up doing some interdisciplinary work if you wanted it and demonstrated
enough interest and competence. At the very least, people who can build highly
scalable data processing systems and who also have a reasonable understanding
of how the data is being used are very valuable.

(2) There are lots of companies out there that don't engage in data
science/machine learning at all. You could join such a company and represent
the push towards developing a data science or ML division or team. If you're
successful this could also get you major credit as a manager as well as
putting you very close to real-world data scientists and ML projects.

------
EternalData
A lot of employers still use degrees as a rough proxy for ability and
dedication. This may be especially prevalent in data science since the field
itself tends to have a lot of Masters/PhDs occupying the field -- which will
tend to bias the hiring process towards viewing degrees as a strong positive
signal.

With that said, a lot of companies hiring for data science roles fall into the
category of software startups -- larger companies like Google or Facebook are
looking for specialists who tend to hold degrees. But at smaller companies,
you can be more of a generalist and there, the old mantra of "show me what
you've built" often applies. You could build out a data science career if you
found just the right company.

By no means is it easy, but I wouldn't say it's a waste of your time (unless
you have some incredible opportunity cost you're using up).

If you were to go about doing it, I found this blog post that can help you
with your plan of attack: [https://www.springboard.com/blog/learn-data-
science-without-...](https://www.springboard.com/blog/learn-data-science-
without-degree/)

------
zengid
As Mike Acton (Data Oriented Design Guru) once said in an interview "I don't
care what you learned in school.. I care about what you learned of your own
volition" [paraphrased from 1].

It never hurts to learn new things. Another HN poster suggested this channel
for beefing up on linear algebra, and I absolutely love it [2].

[1] [https://youtu.be/qWJpI2adCcs?t=58m](https://youtu.be/qWJpI2adCcs?t=58m)

[2]
[https://www.youtube.com/playlist?list=PLlXfTHzgMRUKXD88IdzS1...](https://www.youtube.com/playlist?list=PLlXfTHzgMRUKXD88IdzS14F4NxAZudSmv)

------
randcraw
To hit a target, first you have to see it clearly. The term "Data Science"
covers a broad collection of jobs, from statistician to machine
learning/pattern recognition/AI expert to DBA to business analyst to
visualization/animation expert to cloud/cluster/Hadoop expert to general data
wrangler.

The skills required for each DS role vary a lot. I wouldn't expect a cloud
expert to have learned about the Hadoop stack or HPC workflows in school, at
least not to a useful degree. The same goes for DBA or business analyst or
data wrangler.

But statistics and ML lie at the other end of the spectrum. These roles
require a hierarchy of formal skills that are rarely mastered outside of
college. They're expected to keep up with the research literature or formal
techniques, which almost always requires the math skills of an engineer or
mathematician.

Remember, HR everywhere is technically clueless. If management doesn't tell
them the precise set of skills needed for the job, they'll minimize risk and
ask for more expertise and experience than is needed -- usually in the form of
excess degrees or prestige or buzzwords. The best cure for this is to bypass
HR and go straight to a technical manager who knows what s/he wants. That's
hardest at large corporations, who tend to outsource their HR needs to the
lowest bidder.

At a smaller company, a lack of degree will matter less. If you can convince
them you know what they need RIGHT NOW and can learn future material quickly,
that's what they want to hear. (That's probably what the bosses of the startup
did).

Or if you're targeting a specific project, then if you can show (e.g. via
Kaggle or an online portfolio) that you clearly have the needed skills and
you're not just a script kiddie, that speaks a lot louder than a mere degree
(especially if it's over a decade old).

------
daliwali
I hold a degree in mathematics. Small-minded HR drones have told me I'm not
qualified to do programming since I'm not formally trained in computer
science. I have been doing this since I was a kid.

Don't listen to them. Every professional will at some point in their career be
judged by those less capable.

------
eljefe6a
I teach data engineering and data science. I've taught at hundreds of
companies. Yes, there are self-taught people doing data science in the real
world. They're few and far between, but they are out there.

If you're coming from a programming background, I'd suggest becoming a Data
Engineer with the goal of becoming a Data Scientist. I've had several students
do that. They were general programmers who learned Big Data/data engineering
and eventually became more technical Data Scientists. You can start to learn
more about the whys here: [http://www.jesse-anderson.com/2017/03/what-happens-
when-you-...](http://www.jesse-anderson.com/2017/03/what-happens-when-you-
hire-a-data-scientist-without-a-data-engineer/).

------
traviswingo
Teaching yourself anything is definitely not a waste of time.

Don't get so caught up in the "degree."

I've met individuals with graduate degrees in computer science (i know OP
asked for data science, but the overall point here applies to any field) that
didn't hold a candle to self taught developers. If you're actually passionate
and interested about something, you will become extremely well-versed in it.
On the other hand, if you're not excited about data science, a degree with
probably benefit you more than without one since it will force you to learn
the topic.

In a nutshell, it's up to you to make yourself valuable and present that value
to the world - a degree is just a shortcut for recruiters to filter on, but
you can skip recruiters and talk to anyone in any company.

------
intellectronica
My experience has been that when it comes to the job market _knowing_ stuff is
extremely valuable, but _having learnt_ stuff isn't very valuable, unless you
have an excellent degree from a top tier university. What this implies is that
you should select online study options based on how they contribute to your
actual knowledge, rather than how they will appear to employers (in most
cases, they will appear like nothing). Once you know enough, build a portfolio
of projects to show what you know and look for a job based on that - if you
really know how to get stuff done in the field you'll have many options to
choose from.

------
wellwell
Some actual data: in 2012, 70% of employed data scientists had a Master's
degree or more

[http://cdn.oreillystatic.com/oreilly/radarreport/06369200290...](http://cdn.oreillystatic.com/oreilly/radarreport/0636920029014/Analyzing_the_Analyzers.pdf)

So no, not futile.

------
dpflan
Does anyone have experience with this scenario and actually completed Udacity
nanodegrees for Machine Learning or Data Science or AI?

Their programs express job placement as a perk of graduation.

[https://www.udacity.com/nanodegree](https://www.udacity.com/nanodegree)

Educating for the "jobs of the future" is one of Udacity's goals, data
scientist being one of those jobs.

~~~
Raf_
I'm half-way through their Deep Learning Foundations nanodegree and I'm
generally happy with it.

Note that only selected nanodegrees come with the job placement guarantee, and
that the guarantee seems to essentially mean a refund, if you fail to find a
job within 6 months.
[https://www.udacity.com/nanodegree/plus](https://www.udacity.com/nanodegree/plus)

As a sidenote - the deepest (meta) learning I've gotten is that paying for the
course made me much more engaged and determined to invest time in
understanding the material and completing assignments.

------
Eridrus
It's possible, but definitely challenging. I did exactly this last year and
got several offers, including prestigious companies, but I didn't have my pick
of jobs as I did before and had to make some trade offs to be doing what I
wanted, but it's definitely possible if you're a talented dev.

------
inputcoffee
I can't answer the question directly, but I will say this: machine learning is
a lot of applied math.

Suppose you are setting up a convolutional network to recognize some special
object for a company. You will need to understand that math to know what
parameters to tweak.

Is it the learning rate? Is it the way you randomized the weights? Is it the
activation function?

Although, in fairness, I don't think even a PhD level candidate works out what
the reason is likely to be. More than likely they have a few heuristics in
their head (oh, it stops learning too soon, let's just drop the learning rate.
Oh, it never converges? that activation function can't propagate error and so
on).

The point is that you have to know the theory to be useful. It hasn't been
worked out. It is very much a living science project. That's the fun of it
though.

~~~
hadley
Machine learning is only a small part of data science.

~~~
inputcoffee
True.

However, I think what I said about Machine Learning is just as true -- perhaps
even "more" true -- of Data Science.

Data Science is applied statistics. Knowing the underlying math is key to
interpreting the results, knowing what to tweak and so forth.

(Wait, Hadley Wickham himself commented on my comment!)

------
theonemind
Well, imagine yourself on the interviewer side of the table. If you have a
candidate who genuinely knows more than you, will you honestly turn them away
for lack of a degree?

Obviously, you'll have problems getting past HR/filtering processes, and
knowing more than whoever interviews you is a high bar.

------
brownesauce
I would suggest joining an early stage start up and getting involved with
anything remotely to do with data science at every opportunity. I joined a
small company as an analyst with no programming experience and minimal
statistical knowledge. I was a graduate but not in a relevant subject and just
taught myself the relevant skills on the side. It was a lot of work but not a
waste of time. The programming side of the job can be learnt fairly quickly
but the maths and stats side takes longer. I don't think you can really
succeed in data science without both. Saying that, you certainly don't have to
have a degree to be able to use that knowledge. I did just do a statistics
degree though, and it has made the job a lot more pleasurable.

------
framebit
As a sidenote to your question, you may want to consider Data Engineering.
It's not a sexy as ML, but it pays well and it's in high demand because
somebody has to pipe all that data around so that the ML folks can do their
thing. IMO it's much easier to go from a more traditional software development
role into Data Engineering than into something as math-and-theory-heavy as ML
because Data Engineering is based in how computers work and some knowledge of
algorithmic scaling, not in heavy linear algebra/stats like ML.

------
iaw
I think it's harder but still feasible to obtain a job in data science, after
the first job things will roll a lot quicker.

What a self-taught DS would need to do in order for me to feel comfortable
hiring them is have a public body of work that I find impressive.

There are a huge number of publicly available datasets packed full of
interesting information. Someone that shows they can do the work with a few
findings on their github would be equivalent to a degree on a resume.

------
wdroz
You can do a "regular" programming job and seeking business cases at your
company where data science could help.

After, meet your boss and tell him something like "I can make this process
10-20% faster with a 3 month projects"

If he accept, you will have data science real world experience in your CV and
it will increase your weight on the CV stack when you apply for data science
jobs.

~~~
maxxxxx
I don't think the goal should necessarily be to get a job that has "data
science" in the title. There are plenty of projects where data science could
help but not many devs know about the available tools so if you know something
about data science you have an edge over other devs.

For example in my company there would be plenty of opportunities for applying
machine learning or computer vision. Nobody knows enough to know how to
approach these problem so nothing happens. We could use somebody who knows how
to move forward.

------
j7ake
Just think about how much better you need to be than someone with actual
credentials (e.g. PhD in machine learning and real presentable experience) and
then assess whether you are good enough to compete with them.

If you don't know how good you are relative to the competition with PhDs, then
it would be worth it to have a discussion with people who have a taste for the
field.

------
usgroup
Not a waste of time but you'll need to be entrepreneurial to get a job without
a higher degrees at the moment . In a few years time you'll be able to nail a
job in DS simply because it'll likely be more pervasive in every day SaaS
products and it'll become yet another thing you do as a dev that isn't
strictly part of your job title.

------
Pandabob
On a related topic, I'm graduating with a master's in (computational) physics,
and am already incredibly insecure about not having a PhD as many of the data
science positions seem to prefer those.

Would a four year PhD, let's say in ML, be a worthwhile investment from a data
science career point of view?

~~~
arcanus
> a four year PhD

Those exist? Be careful, very few doctorates are short and well-scoped.

Source: three letter, several of my friends were 7+, masters or not.

~~~
scott_s
[raises hand]

Including Master's (which was when I did most of my graduate course work), I
took about 7.5 for a PhD.

------
michaelalexis
anecdotal, but Jesse Anderson is a world class big data expert, former
Cloudera, etc. and my understanding is he is entirely self taught:
[http://www.jesse-anderson.com/](http://www.jesse-anderson.com/)

------
venture_lol
With data science, do you mean data science as in learning the tools, the
software behind data science? That's like learning any technologies or tools.

Along this line, you would just be a "tech", not a "scientist" That's not to
say you won't be real well compensated.

Data Science as in you are someone able to make sense of the myriads of
conflicting data, derive pattern, synthesize bits and bytes into action plans,
there is no degree in that :)

As an example on this line of thought, people may win the Nobel prize in
Economics even though they may have no idea on how to use Excel :)

------
xchip
Lets try! For example this the math behind a 2x2 neural network:

[http://htmlpreview.github.io/?https://github.com/aguaviva/Ar...](http://htmlpreview.github.io/?https://github.com/aguaviva/ArtificialIntelligence/blob/master/NeuralNetworkBackPropWithMatrices.html)

It is computing the derivatives of the error with respect to the weights.

If you feel comfortable reading that then you are good to go.

------
tmaly
I think if you really want to get into the field, self study can be great. Yes
you could learn some of the frameworks and libraries out there, but I think
you will miss the bigger picture if you do not grasp the fundamentals.

Even brushing up on probability and linear algebra has benefits. Your learning
a skill set that you can use in other areas of life. Heck, if you have kids or
will have kids someday, you will have the knowledge to teach them valuable
skills.

------
thekthuser
Most data scientists don’t have formal data science training. Most of the ones
that go through our free fellowship
([https://www.thedataincubator.com/fellowship.html](https://www.thedataincubator.com/fellowship.html)
\- warning, I work at TDI) have STEM backgrounds and still land data science
jobs at places like LinkedIn, EBay, Amazon, Capital One, Facebook, etc …

------
dansman
Break dow the word "data science" into non bull shit terms, actionable items,
and you will see how achievable it actually is.

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DrNuke
Bum on a seat using the Python free tools as a blackbox and the internet as a
reference then? In most business cases it would work just fine but employers
want to buy the most they can in advance, that's why degrees as a filter. Your
best shot is showing up with one or more interesting, unheard case studies to
gain attention.

------
rvivek
What I've seen is that more and more companies just care about skills rather
than degree. Self-teaching requires a lot of tenacity and most hiring managers
would love this soft skill as well. Skills-based hiring is the future. If you
can build real-world projects and demonstrate your skills, you should have a
good shot.

------
dpflan
How do you plan to study? Have you created your own curriculum or will you be
following one you've found?

Like this Open Source Data Science Masters:
[http://datasciencemasters.org/](http://datasciencemasters.org/)

------
Bedon292
To tag on to this question a little bit. If someone wanted to teach
themselves, even without the purpose of getting a job. What books / references
would be recommended?

I saw a mention to David Barber's book in one of the threads here, but what
else?

~~~
master_yoda_1
The problem is most of the people here don't understand how tough a problem
is. Once you figure that out then search for books for answer. Here is a good
book to start with Python Machine Learning.

Also don't read any book because it is free, Barber book is heavy in maths,
you need at least a college level calculus and advanced statistics/
probability course to understand it.

------
orasis
Machine learning is the new electricity. There will be tons of positions
available.

------
Mz
I suspect it depends in part on where you want to apply. Generally speaking,
large corporations and government entities tend to want formal credentials,
like degrees. This may be less true of smaller or newer operations.

------
badjasper
I have been working software and IT engineering for almost 35 years. I'm self
taught and, never took a college course until about 5 years ago. I have not
been out of work for many years now and, the reason for that is the fact that
most companies desire people who can hit the ground running. College degrees
and books are fine for getting the basics but, what you learn in college is
FAR different than what is in the real world. Companies want people who have
been in the trenches and learned with "Trial by fire".

If you want to get a start as a self educated person in IT then, learn what
you can on your own and then reach out to contracting firms. Get a few entry
level contract gigs under your belt in order to pad your resume with some
experience and then move up the ladder.

~~~
j45
Quite often, Self-directed education + track record > Formal education

------
darkxanthos
I'm a lead data scientist and I don't have a degree. I do have
programming/technical chops though which helped a ton.

------
qubex
You seem to assume that the only use for knowledge is garnering employment:
this is patently false, as you could easily learn something and apply it for
your own pleasure in the non-professional domain.

P.S. It's called ’statistics’.

~~~
cwyers
> P.S. It's called ’statistics’.

No, it's not. Read Breiman's "Statistical Modeling: The Two Cultures."

[http://www2.math.uu.se/~thulin/mm/breiman.pdf](http://www2.math.uu.se/~thulin/mm/breiman.pdf)

"Statistics" has largely been concerned with the "data modelling culture"
Breiman talks about; a lot of data science is focused on algorithmic
modelling, things like neural nets, random forests, and so on. A lot of these
techniques have been refined outside of modern statistics because of
statistic's focus on data modelling.

This also ignores all of the things that fall outside of the purview of the
modelling steps altogether, things like data cleaning, data engineering, and
so on. All of those are properly "data science" but often fall outside of
what's in a statistics textbook.

If you are going to do data science, you should know statistics. You should
know a lot of it. But that is far from the only thing you should know.

As to your larger point... yeah, well, jobs allow people to eat and get health
insurance and all that, so it's understandable that OP might want to be able
to do those things and not just apply it for his own pleasure. My take on that
is that it's hard, both to acquire the skills needed and to signal to
employers that you have them. If you're going that route, you need to build a
solid portfolio of work. Kaggle might be a good place to start.

~~~
mr_overalls
I agree with this observation. And given data science's distinguishing
emphasis on algorithms and process, maybe it would be more properly called
"data engineering."

------
ElijahLynn
Nothing is a waste of time so long as you learn from it.

------
rezashirazian
Learning something you like is never a waste of time.

------
muninn_
It depends on what else you're doing. If you're a Scala dev and regularly work
with something like Spark and Hadoop you could probably find an entry level
data science job at a non-FB/Google company because your programming and
framework experience are much needed. But if you're just a Java dev and you're
taking an Udemy nanodegree or something you would have to know somebody or get
very lucky.

It's possible to maybe help another team and sequel that into a data science
job internally, but outside, forget it.

------
iamacynic
i fell into an ML consulting gig that was very lucrative once.

something to ponder: you just have to know enough to actually deliver on
something management wants, and know more about it than everyone else at the
company.

~~~
bbcbasic
True. I'm an AV security expert at my mother in law's house.

