I used R for about 4 years before I moved to Python to use it for deep learning. I have been using Python for about 2 years now.

>Are R and Python considered redundant, or are there some situations where one will be preferred over the other? If I become proficient at using Python for data wrangling, analysis, and visualization, will I have any reason to continue using R?

It depends. I haven't really found anything that I can do in Python that I could not already do in R. I still use R because I like it better as a functional programming language and because it has a wide variety of more specific statistical packages (many for biology) that are just not available for Python yet. There are some specific cases where I just find it more intuitive and simpler to implement a solution in R. And generally, I just prefer ggplot2 over any of the various Python plotting packages. Also, R has high level API for things like TensorFlow so it's not like you can't do deep learning in R.

The biggest advantage for Python is its speed and ability to work within a larger programming framework. A lot of companies tend to use Python because the models they build are integrated into a larger system that needs the capabilities of a fully-fledged programming language. Python is generally faster and has better management of big data sets in memory. R is actually moving more in the direction to fix these issues but there are still limitations.

>Where should I start? I'm looking for a resource that isn't aimed at complete beginners, since I've been using R for a few years, and took a C class before that. At the same time I wouldn't claim to be an experienced programmer. I'm interested in learning Python both for data analysis and for general programming.

I learned Python syntax using Learn Python 3 the Hard Way. I learned about Pandas and data wrangling etc using Pandas for Everyone and Pandas Cookbook. If I was to suggest just one book, it would be Pandas for Everyone. You can learn Python syntax from YouTube, MOOCs, or online tutorials. The Pandas Cookbook is just extra practice. To be honest though, the general conventions used by Pandas for data analysis and manipulation are very similar to R in many ways. Especially if you've used anything in Hadley Wickham's Tidyverse. Finally, I made a Pandas cheatsheet while I was learning and including equivalent R functions in some places. I would be happy to share this Google Sheets file with you if you are interested.

>What IDE(s) should I use, and what are some must learn packages? I'm hoping to find something similar to RStudio.

I started off using PyCharm. I've heard good things about Spyder. But now, I actually still use RStudio! It is fully integrated with Python thanks to the Reticulate package. You can pass data structures between the languages and use both in RMarkdown. You can also use virtual environments which are popular with Python. Once you install the package:

library(reticulate)
use_virtualenv("path_to_my_virtual_env") # Start virtual environment

You can now run Python scripts directly in the RStudio console

# If you want a Python REPL to use interactively just like in R run:<br />
repl_python()<br />

It's really easy to use and even comes with auto-complete and everything else.

Hope that helped.

Now this is an interesting and difficult question, which depends on many things. For starters, if you find this process frustrating that is unfortunate, because learning how to code is usually a trail and error + revise your work process. In other words, its supposed to be both frustrating and rewarding, like a hard (text-based) video game. For me its half of the fun.

What you want to learn really depends on the context. If you are really diving into econ, Stata is still very common. More stats-heavy, new, or interdisciplinary fields tend to use R. If you work with big, live datasets, or work with computer scientists, learning Python is always a plus. But obviously start with one.

I am proficient in stata simply because I had classes in it, it is difficult for me to advice how to self study. I learned most of the basics through An Introduction to Modern Econometrics Using Stata, and later on most of my R through R for Stata Users (Statistics and Computing) . I also learned some R through Discovering Statistics Using R, but I find Field obnoxiously failing to be funny, so I wouldn't reccomend it.

I'm now in the process of learning more Python, to do some more programming work on the side. As a start I used Learn Python 3 the Hard Way recommended to me by a very proficient friend of mine. This however does not give you much of an intro to stats in python, only the very very simple basics you can use as a vantage point for further work.

If you have learned the basics, tbe hest way to learn more is just to fool around. What is your field of interest? I like a lot of macro, so I used to just go to Quandl, pick some free databases, import them, and run some fun stuff. This is the best way to learn, especially if you for example try to merge free World bank databases with a different database from Quandl, as it will give you a lot of errors whilst merging and conversion problems later on.

If you are a bit more proficient you can start using websites like upwork to get some assignments. Usually it doesn't earn you much at the start, but the experience of actual assignments is the best way to self-teach. A different manner I like to do (if you are still studying) is offering your services (for free) to a professor. Ask him/her if there are still projects they are working on for which they need some to look at. Usually you will be treated solely as someone for the code, but it generally gives you a lot of experience and the right contacts.

These are just some of my thoughts. If you could provide some more context of where exactly you want to go, I could go into more detail.

Edit: What I forgot to say is that if it is not possible to study a course, I would recommend doing at least one MOOC to get you at a basic level.

First, read our Wiki. It is very thorough and answers a lot of these common questions such as

day to day? The Practice of System and Network Administration
And the topical reference books listed below.

Books to help in shaping a sysadmin? The above &:
The Phoenix Project
Time Management for System Administrators


Topical Books I see mentioned often and have been very helpful to me:
Powershell in a month of lunches
Learn Python the hard way
Unix and Linux System Administration Handbook
Windows Server 2016: Inside Out

Group Policy
AbsoluteBSD
FreeBSD mastery:ZFS
CCNA
RHCSA/RHCE
Pro Puppet
SSH Mastery

On my docket:
FreeBSD Mastery: Advanced ZFS

Michael W. Lucas and Thomas Limoncelli are very good sysadmin writers, you can't go wrong with a topic they have chosen to write about.

Most of the *nix stuff assumes a baseline knowledge of how to use a unix-based system. I learned as I went but did pick up an old copy of Unix Visual Quickstart Guide not too long ago at a used books sale, which seems like a good starting place for someone overwhelmed with sitting at a terminal and being productive.
I notice I don't have any Virtualization books, perhaps someone else can fill in good books. Most of my knowledge regarding virtualization and network storage has been a mix of official docs, video training, and poking at it. Seems innate but it isn't.

I suspect that page will change soon, because Learn Python 3 the Hard Way was recently published (july 7 2017).

Não, esse aqui:

https://www.amazon.com.br/Use-cabeça-Python-Paul-Barry/dp/857608743X

Tem uns meses e encontrei por uns 40 reais na Cultura, em Porto Alegre... Infelizmente não conheço nenhum dos dois, mas quanto ao primeiro, geralmente os materiais da O’rilley de TI costumam ser bons.

Outro que utilizei e recomendo MUITO é esse:

https://www.amazon.com.br/Learn-Python-Hard-Way-Introduction/dp/0134692888

Automate the boring stuff with Python

Learn Python 3 the hard way

You said you want to get into Data Science, there’s a pretty good website that teaches about that and also teaches more about Python beyond the beginner stuff called DataCamp

I've had that book for about 4 years. It's helpful, but I think there are better ones out there. The good thing is that it's free (online).

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I would suggest Learn Python 3 the Hard Way: A Very Simple Introduction to the Terrifyingly Beautiful World of Computers and Code