I think it's a good first impression of either languague
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
#32This 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
I can list a bunch. But the answer depends on a lot of things.
1. Which language?
2. Do you have any background in programming and if yes how much and in which language? Beginners can range from "I don't know what a loop is" to "I've been a front end developer for a few years and want to try something new". These people will obviously need to approach things very differently.
3. What's your math background? Do you know what a derivative is and how matrix multiplication works? Do you want to go in depth or do you need just a general understanding of the algorithms? Some people will need to start at precalculus if they really want a solid foundation. The same people also most likely won't have the patience or interest to stick with the theory for long enough.
4. How will you use what you're learning? Is there a specific goal related to this and does it have a time horizon?
...
Anyway, I'm rambling. Data science is just programming and statistics. Your general learning lanes are
1. Theory - calculus, linear algebra, statistics, ML algorithms.
2. Programming.
2a. Good software development principles - writing maintainable code, version control, testing, design patterns etc.
2b. Tooling - learning the language-specific ecosystem of libraries. This is what most beginner resources (including the OP) focus on and is also the one that constantly expires and has the least transferability to other skillsets and fields. Obviously, it's still necessary - using the right tools and knowing them well goes a long way.
You mentioned ISLR - it's a good beginner-ish theory book that helps you understand how the algorithms work without going too deep into the math.
Re: Another Book on Data Science – Learn R and Python in Parallel
#33This 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).
Yeaah, to your last thought - without being too cynical, looks like the author wanted to write a beginner data science book but realised that the good beginner data science books are already written (as well as many bad ones).
Re: Another Book on Data Science – Learn R and Python in Parallel
#34Earlier quoted context omitted.
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).
Having worked at CERN, many do in fact use C++ for day to day work, hence why CINT and ROOT exist.
However my point was that with Julia, those libraries would have been written in Julia.
All the Python libraries that one throws around for these use cases are C, C++ and Fortran libraries, that happen to have Python wrappers.
Any programming language can have wrappers for them, there is nothing written in Python per se.
Re: Another Book on Data Science – Learn R and Python in Parallel
#35I 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.
magrittr is part of the tidyverse, but I agree that data.table is a comparably powerful and sometimes faster option versus dplyr.
Re: Another Book on Data Science – Learn R and Python in Parallel
#36I also skimmed through the book and it does very poor work in case of python I think they have to change this concept
Re: Another Book on Data Science – Learn R and Python in Parallel
#37I also skimmed through the book and it does very poor work in the case of python I think that they should change R bcoz 90% are the beginners
Re: Another Book on Data Science – Learn R and Python in Parallel
#38Earlier 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.
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.
Re: Another Book on Data Science – Learn R and Python in Parallel
#39This 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
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 more probability, calculus and linear algebra.
2. Programming.
2.1. Good software engineering practices - writing maintainable code, design patters, version control, unit testing etc.
2.2. Tooling - knowing the language-specific ecosystem of libraries (the OP is an attempt to teach you this in two languages at the same time). This is what most beginner resources focus on; your knowledge here has the least transferability and tends to go out of date quickly. Still, using the right tools and knowing them well goes a long way.
Re: Another Book on Data Science – Learn R and Python in Parallel
#40I 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…
> Python is definitely better for matrices (because of NumPy)
How so?