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

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

#21

Post by cwyers »

wjn0 wrote: post #11
clarityPhone wrote: post #2

I skimmed through the book, and think it does a very poor job at showcasing how R and Python are juxtaposed in industry. To be fair, the book advertises showing R and Python code side-by-side. And that’s what it does. But it does it unlike how the languages are most often used in industry. As a quick example, I saw no tidyverse code, which is essentially the only thing keeping R in the game. Learning R from this book…

I agree for the most part, but R does have a few things beyond the tidyverse: built-in dataframe support, lots of domain-specific packages, more consistent interfaces for basic statistics and machine learning models, etc. Python is definitely better for matrices (because of NumPy) and anything involving custom gradient descent methods (because of TensorFlow). I think 90% of data science content is for beginners becau…

Even if you ignore the tidyverse, the example code for "roll your own linear regressions by hand" uses the R6 object system, which is... not even one of the two popular object systems for R (which are S3 and S4). No beginner needs to learn how to write classes in R.

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

#22

Post by randomvectors »

mruts wrote: post #6
adamnemecek wrote: post #3

Julia is hands down much better than either of these languages. Don't waste your time.

I whole heartedly agree. Python is garbage for data science. If an industrial grade NN library was written for it, plus some quant libraries, I think most people would switch. I work in finance doing data science-y things and have yet to meet anyone who doesn’t think that Python is a pile of garbage. People used to make the easy to learn argument, but Julia is even easier. And more elegant, extensible, and faster.

> If an industrial grade NN library was written for it

Can you give tell us which language and industrial grade NN library you're using?

Because from where I'm sitting I see that Python is the only language that gets first grade support for both Tensorflow and Pytorch. It's so ahead for working with NN that it's not even close.

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

#23

Post by randomvectors »

adamnemecek wrote: post #7
jhbadger wrote: post #5

Earlier quoted context omitted.

It's a matter of ecosystem of packages. R has a huge number of packages for many fields. Python has fewer, but might work for particular use cases. I was excited for Julia, and played around with it since 0.2, but it really hasn't generated very many packages of note in my particular field (bioinformatics).

julia has really good interop with both of python and R as well as cpp, matlab, mathematica and others. Also it's not just about the numbers.

So you're telling us not to waste our time with R or Python... but to constantly interop with R and Python?

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

#25

Post by adamnemecek »

randomvectors wrote: post #23
adamnemecek wrote: post #7

Earlier quoted context omitted.

julia has really good interop with both of python and R as well as cpp, matlab, mathematica and others. Also it's not just about the numbers.

So you're telling us not to waste our time with R or Python... but to constantly interop with R and Python?

I mean if there are packages that you need then be my guest. But like if you are starting a new project, julia is a more productive language.

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

#26

Post by thewhitetulip »

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.

Can you please list a few?

I'm currently following ISLR book & course

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

#27

Post by pjmlp »

huac wrote: post #19
mruts wrote: post #6

Earlier quoted context omitted.

I whole heartedly agree. Python is garbage for data science. If an industrial grade NN library was written for it, plus some quant libraries, I think most people would switch. I work in finance doing data science-y things and have yet to meet anyone who doesn’t think that Python is a pile of garbage. People used to make the easy to learn argument, but Julia is even easier. And more elegant, extensible, and faster.

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

They are written in C++.

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

#28

Post by anonytrary »

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.

This is my initial thought. I can't imagine learning more than one syntax at once. This is probably aimed towards eidetic folks (tbh, probably not, but it should be).

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

#29

Post by rhizome31 »

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.

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

#30

Post by huac »

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++.

are you suggesting that data scientists use C++ for day to day work? those libraries have first-class wrappers in Python (there is R support, but not at the same level).
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