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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

#51

Post by ianamartin »

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.

Whenever I see the “I’ve never met x” argument, I’m always looking out for the catch. Because it’s a bad argument to begin with. But the catch is almost always in a situation where x is everywhere, and you would have to have your head willfully stuck in a hole in the ground to not see it.

And this is no exception.

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

#52

Post by ackbar03 »

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.

I think he uses a calculator for that

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

#53

Post by thewhitetulip »

randomvectors wrote: post #46
thewhitetulip wrote: post #41

Earlier quoted context omitted.

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; i…

Thank you. That's what I was thinking. The jobs I'll apply won't be math oriented.

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

#54

Post by _2d30 »

notafraudster wrote: post #35
thomasfedb wrote: post #9

Earlier quoted context omitted.

Is tidyverse really the only option? I'm a big fan of data.table + magrittr as a very powerful data munging combo.

magrittr is part of the tidyverse, but I agree that data.table is a comparably powerful and sometimes faster option versus dplyr.

Almost always faster, actually.

https://github.com/Rdatatable/data.table/wiki/Benchmarks-:-G...

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

#55

Post by Der_Einzige »

jhbadger wrote: post #5
adamnemecek wrote: post #3

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

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).

Pretty sure that Python has a larger total ecosystem of packages than R.

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

#56

Post by thomasfedb »

Bootvis wrote: post #38
notafraudster wrote: post #35

Earlier quoted context omitted.

magrittr is part of the tidyverse, but I agree that data.table is a comparably powerful and sometimes faster option versus dplyr.

magrittr existed before the tidyverse and can be used on standalone perfectly fine. In all benchmarks I've seen data.table is faster than dplyr on all tasks. Curious to see other results.

At the scale of what I'm doing the benchmarks don't sway me, but I do like the syntax of data.table - it feels a bit like relational algebra.

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

#57

Post by ivirshup »

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.

I love Julia, but feel the interop story could use some work. If I want to have my python package depend on some Julia code, how easy is that? Last time I checked (a few months ago), it was pretty difficult.

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

#59

Post by hobofromabroad »

thatcat wrote: post #44

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

SAS ist still quite a big thing in the insurance sector, at least in Europe.

But the more Data Science focused roles (vs. Pure actuarial roles) are going more and more with R or Python.

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

#60

Post by randomvectors »

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…

> built-in dataframe support

Not an advantage if you ask me - exactly because data.frame is built in, people have been building their own versions (tibble, data.table) instead of improving it. That's how R ended up with 3 different structures that are similar but have inconsistent apis and behaviour.

> lots of domain-specific packages

That's true.

> more consistent interfaces for basic statistics and machine learning models

Can't disagree more - there is no one go-to library for ML in R (like sklearn in Python) and each package has it's own strange interface and implementation.

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