But why? If you would never join NASA then why train to be an astronaut. Do something useful in life.
A book to learn R and Python in parallel for Data Science
11–20 of 90 posts
Re: A book to learn R and Python in parallel for Data Science
#12I read a good portion of the first chapter and skimmed the rest. I am very much enjoying this book and hope that you continue to write more chapters.
Re: A book to learn R and Python in parallel for Data Science
#13I sometimes wonder is there any reason to learn R at all, since python eco system has absorbed most of its advanced statistical functionality, coupled with the factor that python environment is much more general, with capabilities to fetch, decode/encoded data,work with binary data databases, web frameworks for presenting etc.
Re: A book to learn R and Python in parallel for Data Science
#14Python and Julia might make more sense today.
Re: A book to learn R and Python in parallel for Data Science
#15I sometimes wonder is there any reason to learn R at all, since python eco system has absorbed most of its advanced statistical functionality, coupled with the factor that python environment is much more general, with capabilities to fetch, decode/encoded data,work with binary data databases, web frameworks for presenting etc.
> since python eco system has absorbed most of its advanced statistical functionality
This isn't true at all...
Also all advance statistical books are either SAS or R. If it's R then there is always a package that the author created.
Just look at Chapman & Hall/CRC or Springer publisher and look at their books.
Go here: https://www.jstatsoft.org/index
Count the number of R packages in those papers versus Python.
I don't even need a source. I'm a statistician and I'm going to get a paper there and publish a R package for my master thesis.
Re: A book to learn R and Python in parallel for Data Science
#16I sometimes wonder is there any reason to learn R at all, since python eco system has absorbed most of its advanced statistical functionality, coupled with the factor that python environment is much more general, with capabilities to fetch, decode/encoded data,work with binary data databases, web frameworks for presenting etc.
I use both Python and R. tidyverse/ggplot2 alone are enough reason to use R, and are substantially faster for tasks that utilize those packages than the equivalent in Python (in my opinion).
Although I haven't had as much reason to use base R. For more ML-related tasks I do go back to Python.
Re: A book to learn R and Python in parallel for Data Science
#17Nobody would write R code the way this book is teaching. For that matter, nobody looking to do linear regression for data science in Python is doing their own matrix math, either.
A lot of MOOCs teach matrix algebra that way, which I admit I'm not fond of.
Re: A book to learn R and Python in parallel for Data Science
#18This is an interesting concept. It makes perfect sense to learn both simultaneously. On the other hand, it must be confusing at times. Imagine learning two languages at the same time, from the same book. It's an experiment I haven't tried, but i'm curious about the outcome.
Also interesting to learn two languages, in parallel, where neither is particularly good at parallelism :)
Humans are very good at doing things successively and calling it parallel.
Re: A book to learn R and Python in parallel for Data Science
#19I sometimes wonder is there any reason to learn R at all, since python eco system has absorbed most of its advanced statistical functionality, coupled with the factor that python environment is much more general, with capabilities to fetch, decode/encoded data,work with binary data databases, web frameworks for presenting etc.
I use mixed effects models pretty extensively. While there is an implementation in statsmodels the implementation in lme4 is more user friendly and has a more mature ecosystem of post-hoc tests.
Re: A book to learn R and Python in parallel for Data Science
#20I sometimes wonder is there any reason to learn R at all, since python eco system has absorbed most of its advanced statistical functionality, coupled with the factor that python environment is much more general, with capabilities to fetch, decode/encoded data,work with binary data databases, web frameworks for presenting etc.
I use both Python and R. tidyverse/ggplot2 alone are enough reason to use R, and are substantially faster for tasks that utilize those packages than the equivalent in Python (in my opinion). Although I haven't had as much reason to use base R. For more ML-related tasks I do go back to Python.
Seconded on all points. I do branch out to SQL for stuff, too, and I find that R and Python play nicely with it, too. But as long as ggplot exists and Python doesn't have it, R will never really leave my side.