Data Analysis with Python
You need to first download the free distribution of Anaconda3. I highly suggest if you are starting python - start with Python 3 (3.4 right now) and make sure you use the python 3 distribution of Anaconda.
From there, if you are not going to pay for or take any courses, I suggest a few books - and read in this order (I am saving you a few months/years that I wasted), 1.) Introducing Python by Bill Lubanovic, this will serve as a good and quick foundation in the language and some of the real-world applications of it. 2.) (This one will look daunting at first but make sure to read it cover to cover if you have never programmed C/C++ before, because this book will explain most of the idiosyncrasies of the language)Learning Python by Mark Lutz - it is Verbose and I am reading it for my 3rd time (not cover to cover anymore, but I will consume a few chapters to refresh my mental syntax understanding).
After you have read these two books and actually have a solid grip of your understanding of: list comprehensions, generators, decorators - then learn Haskell (not to actually use Haskell daily, but to understand base functional programming). Then move back to python and use functools and you will have a broader understanding of why Python is an awesome language(OOP, Procedural and Functional) and appreciate many of the semantics of the language that newcomers don't understand without learning other languages.
The data analysis (Python For Data Analysis is the best book I have read on the subject) is built in "batteries included" in Python. So you really need to learn the language to truly tap into the data aspect fully.
Read more: Python training Marathahalli
From there, if you are not going to pay for or take any courses, I suggest a few books - and read in this order (I am saving you a few months/years that I wasted), 1.) Introducing Python by Bill Lubanovic, this will serve as a good and quick foundation in the language and some of the real-world applications of it. 2.) (This one will look daunting at first but make sure to read it cover to cover if you have never programmed C/C++ before, because this book will explain most of the idiosyncrasies of the language)Learning Python by Mark Lutz - it is Verbose and I am reading it for my 3rd time (not cover to cover anymore, but I will consume a few chapters to refresh my mental syntax understanding).
After you have read these two books and actually have a solid grip of your understanding of: list comprehensions, generators, decorators - then learn Haskell (not to actually use Haskell daily, but to understand base functional programming). Then move back to python and use functools and you will have a broader understanding of why Python is an awesome language(OOP, Procedural and Functional) and appreciate many of the semantics of the language that newcomers don't understand without learning other languages.
The data analysis (Python For Data Analysis is the best book I have read on the subject) is built in "batteries included" in Python. So you really need to learn the language to truly tap into the data aspect fully.
Read more: Python training Marathahalli
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