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Another Book on Data Science – Learn R and Python in Parallel

anotherbookondatascience.com

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Re: Another Book on Data Science – Learn R and Python in Parallel

#41

Post by thewhitetulip »

randomvectors wrote: post #39
thewhitetulip wrote: post #26

Earlier quoted context omitted.

Can you please list a few? I'm currently following ISLR book & course

Depends on what you're interested in, your goals and your starting point. You have two main learning lanes: 1. Theory. Math, calculus, linear algebra, probability, statistics, ML algorithms. ISLR is a very good beginner-ish resource that helps you understand the algorithms but doesn't go too deep into the math. As you go deeper, you may realise that you have gaps in your math knowledge and you need to cover a lot mor…

I'm sorry in advance if I'm taking too much of your time.

#2: coding is my hobby and have been writing well designed apps for a long time, so thats not an issue

#3: ISLR is teaching how to do ML algo in R, so there goes that point

#1 is what I'd like more information. Good important is the maths to work as a data scientist? I'm planning ISLR and then maybe ESL or some advance course

A few of my friends work on Data science and they said that maths isn't that important as in, one needs to know the formulas and why things work the way they do as in not rote learning the math.

It'd be great if you can list down intermediate courses, the learning market has drowned good tutorials and books with not so good guides!

Re: Another Book on Data Science – Learn R and Python in Parallel

#42

Post by krapht »

adamnemecek wrote: post #18
s_Hogg wrote: post #17

Earlier quoted context omitted.

Exactly: you're talking about semantics. In a great many cases, they aren't a first-order issue - which is where my objection to a blanket "don't waste your time" claim comes from.

What is there besides semantics?

Uh, adoption by the community at large? The best program is the one you didn't have to write because a package existed for it already. Just because a language has a foreign function interface doesn't mean it's easy to interact with other libraries.

R, Python, Matlab, and C++ are the big dogs in scientific programming, and the inertia behind having a large community behind than will continue to drive adoption.

Re: Another Book on Data Science – Learn R and Python in Parallel

#43

Post by danso »

rhizome31 wrote: post #29
randomvectors wrote: post #24

This just doesn't seem to have a place. 1. It's aimed at beginners. 2. If you're a beginner, you're best off picking one language and sticking with it for a while. 3. There are so many other beginner resources that are much better.

As a Python dev who often have to teach Python to data scientists or sometimes integrate their R code, I can imagine this could be a useful reference.

I've been in that position; for most situations, I can't imagine it being efficient for the data scientists to move over to Python themselves, if they don't have experience (or interest) in software engineering.

Re: Another Book on Data Science – Learn R and Python in Parallel

#45

Post by s_Hogg »

thatcat wrote: post #44

I'd be interested in a version that included SAS. Is SAS ever used outside of academia?

Yes, though at least where I am it tends to be a legacy system these days. So that means governments and large corporations might reasonably feature some use of it somewhere within their walls.

Re: Another Book on Data Science – Learn R and Python in Parallel

#46

Post by randomvectors »

thewhitetulip wrote: post #41
randomvectors wrote: post #39

Earlier quoted context omitted.

Depends on what you're interested in, your goals and your starting point. You have two main learning lanes: 1. Theory. Math, calculus, linear algebra, probability, statistics, ML algorithms. ISLR is a very good beginner-ish resource that helps you understand the algorithms but doesn't go too deep into the math. As you go deeper, you may realise that you have gaps in your math knowledge and you need to cover a lot mor…

I'm sorry in advance if I'm taking too much of your time. #2: coding is my hobby and have been writing well designed apps for a long time, so thats not an issue #3: ISLR is teaching how to do ML algo in R, so there goes that point #1 is what I'd like more information. Good important is the maths to work as a data scientist? I'm planning ISLR and then maybe ESL or some advance course A few of my friends work on Data s…

> then maybe ESL or some advance course

> A few of my friends work on Data science and they said that maths isn't that important

Without the math you won't understand anything in ESL.

Which might be okay if the job doesn't require you to go into that much depth - some data science jobs are more focused on research (very math-heavy), some on ETL and/or engineering, others on business understanding and communication; it's a really broad title.

Re: Another Book on Data Science – Learn R and Python in Parallel

#47

Post by randomvectors »

thatcat wrote: post #44

I'd be interested in a version that included SAS. Is SAS ever used outside of academia?

SAS is still used by companies who don't want to use open source tools and would rather pay a lot of money for an established product name and have a support line that they can call if anything goes wrong. Understandably, it's slowly dying out.

Re: Another Book on Data Science – Learn R and Python in Parallel

#48

Post by randomvectors »

pjmlp wrote: post #27
huac wrote: post #19

Earlier quoted context omitted.

You, uh, don't like PyTorch and TensorFlow? I can't tell if this is sarcastic.

They are written in C++.

Not sure how that's relevant. Everything is written in something else.

Python itself is written in C. The Julia github repo shows Julia 68.2%, C 16.3%, C++ 10.4%, Scheme 3.2%. R is a mix of C, C++, R and some Fortran I think...

Re: Another Book on Data Science – Learn R and Python in Parallel

#49

Post by tomrod »

randomvectors wrote: post #47
thatcat wrote: post #44

I'd be interested in a version that included SAS. Is SAS ever used outside of academia?

SAS is still used by companies who don't want to use open source tools and would rather pay a lot of money for an established product name and have a support line that they can call if anything goes wrong. Understandably, it's slowly dying out.

Support, and also indemnification.

SAS lives on borrowed time.

Re: Another Book on Data Science – Learn R and Python in Parallel

#50

Post by pjmlp »

randomvectors wrote: post #48
pjmlp wrote: post #27

Earlier quoted context omitted.

They are written in C++.

Not sure how that's relevant. Everything is written in something else. Python itself is written in C. The Julia github repo shows Julia 68.2%, C 16.3%, C++ 10.4%, Scheme 3.2%. R is a mix of C, C++, R and some Fortran I think...

It is relevant from the point of view of what a developer is able to achieve without being forced to drop down to a 2nd programming language, aka "2 language syndrome".

And how many Python libraries are just plain wrappers, not really written in Python.

I use TensorFlow from .NET ML and C++ API.

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