
Free Big Data Education: A Data Science Perspective - Anon84
http://www.bigdatarepublic.com/author.asp?section_id=2809&doc_id=257527&
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up_and_up
> These free courses (some offer certifications) offer an excellent path
> toward obtaining the requisite background for becoming a data scientist.
> I’ve put together a BDR Data Science “pseudo degree program” for you to
> follow.

Here's my question as a have been thinking about pivoting into Big Data
recently. I have a non-technical BA degree, but taught myself programming and
have worked as a Software Engineer, Sr Software Engineer and Lead Engineer at
a variety of startups. Since I dont have the math background I have been
taking classes on Coursera in math/data science etc.

But I am torn on whether I need a MS degree in Applied Math/CS/Statistics etc.
What is recommended? I have already read many blog posts/quora etc on the
subject.

Also, do people compete on Kaggle etc to get their skills up?

These Free Big Data learning materials are great, but all the job postings for
these positions want graduate level math capability, which makes sense. Whats
the best way to jump int this?

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rm999
You don't need graduate level math, but if you want to use machine learning
it's basically mandatory to have a solid understanding up to calculus, linear
algebra, and probability/statistics. At least if you want to understand the
underlying algorithms, which in my opinion is essential to being a good data
scientist.

I did a masters in machine learning/computer science, but I think I could have
learned a lot of the stuff on my own. The value of the masters was in
projects, interaction with professors/other students, and the ability to do
research (which I think is very underrated). FYI: a lot of jobs will
discriminate against people without technical degrees and graduate degrees.
Independent projects or relevant work experience will help.

My career predates kaggle, but I did do a machine learning competition to
practice and apply my knowledge. Winning these will look good on a resume, but
only to a point: there's a lot more to being a data scientist than what these
competitions test.

~~~
up_and_up
> FYI: a lot of jobs will discriminate against people without technical
> degrees and graduate degrees.

Thanks for the info. Thats my dilemma I guess. Leveraging free online
resources can only take you so far. I guess my assumption was that I needed to
get a masters and I was wondering which field made the most sense: Applied
Math / Stats / CS. At this point it seems like CS would be the way to go.

~~~
rm999
CS would give you the most bang-for-your-buck, especially a program that can
let you specialize in machine learning.

You may be able to leverage your software engineering positions to skip an
additional degree. I've heard people argue that data scientists are software
engineers with additional specialized knowledge, which I think is largely
true.

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elliott99
Maybe my undergraduate institution failed mebut I don't feel that having taken
linear algebra, multivariable calc, applied stats, and graduate probability
over the summer that the PDF below contains readily accessible information to
me. Does it require graduate linear algebra? Graduate multi-v? We're my
courses not rigorous enough? Am just not smart enough? I graduated summa cum
laude, so maybe I should be okay.

All I am saying that is that there seems to be a huge leap between having
taken the prerequisites everyone says you should take and then seeing Cramer-
Rao bounds on page 70.

<http://alex.smola.org/drafts/thebook.pdf>

