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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

#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 won’t prepare you for writing R in most R shops.

I don’t see the utility in knowing how to do the same thing in both python and R if you’re a beginner. This is even more true if you’re not taking advantage of the strengths/weaknesses of either language.

Instead, just learn one of the languages well, and then learn the other well. Shallow dives in both will make you weak in both.

Unfortunately, 90% of data science content seems to be geared at beginners.

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

#4

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…

That's pretty much the only group of people who will use this though. Those that are serious about it or have some background won't really look at another book on data science and probably do the necessary research themselves

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

#5

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).

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

#6

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.

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

#7
post #5

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).

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.

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

#8
post #6

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.

Plus a good package manager, insanely good interop, one good ide, etc etc.

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

#9

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…

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