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If you want to upgrade your data analysis skills, which programming language should you learn?

For a growing number of people, data analysis is a central part of their job.

Increased data availability, more powerful computing, and an emphasis on analytics-driven decision in business has made it a heyday for data science. According to a report from IBM, in 2015 there were 2.35 million openings for data analytics jobs in the US.

Excel cannot handle datasets above a certain size, and does not easily allow for reproducing previously conducted analyses on new datasets.

The main weakness of programs like SAS are that they were developed for very specific uses, and do not have a large community of contributors constantly adding new tools.

For those who have reached the frontiers of these programs, there is a next step: learn R or Python. R and Python are the two most popular programming languages used by data analysts and data scientists.

(For a more technical discussion of the debate and others’ opinions on the matter, see here.) In a nutshell, he says, Python is better for for data manipulation and repeated tasks, while R is good for ad hoc analysis and exploring datasets.

Another advantage of Python is that it is a more general programming language: For those interested in doing more than statistics, this comes in handy for building a website or making sense of command-line tools.

For someone interested in becoming a general-purpose programmer, Python is a better choice.  But for data analysis, the differences between R and Python are starting to break down, he says.

Python vs (and) R for Data Science

As requested, I’m publishing this guide for those wishing to choose between Python and R Programming languages for Data Science.

Hadley Wickham https://twitter.com/hadleywickham, Chief Data Scientists of RStudio ,had replied “Replace ‘vs’ with ‘and’.” Prompted by this, using Python/R together is a third choice I will cover.

brief history: The first thing to keep in mind when comparing the users of Python vs R, is that: That is assuming that all of R programmers would call there use “Scientific and Numeric”.

To further dive into the Python “Hype” read my article on my Python Hype Survey Results: If we only look at scientific and numeric community, that brings us to our second, which community?

Some examples of sub-communities using Python/R: While each domain seems to serve a specific community, you would find R more prevalent in places like Statistics and Exploration.

Not so long ago, you could be up-and-running and doing some fairly meaningful exploration with R in far less time it would take to install Python and do similar exploration.

Now that you can get up and running in an environment friendly to providing reporting and analysis out of the box, there has been a barrier removed that sat between those who wish to do the task and they language they love.

Python now can come packaged in a platform independent way and provide quick-down-and-dirty analysis quicker then ever before.

Not just open source’d libraries, but the impact of collaborative communities contributing to open source.

Surely, you will miss something, someone will complain, friends will be lost, and the whole analysis will be tossed away with gusto!

We conducted an experiment: compare the execution times on a complex exploratory effort while mirroring each part.

Times when: Some ways to use the 2 together are: Then we can actually pass the pandas data frame and it is automatically (by rpy2) converted into a R Dataframe, passed with the “-i df” switch: sources: http://nbviewer.jupyter.org/gist/brianray/734bd54f468d9a6db9171b2cfc98405a Someone on Kaggle wrote a Kernel on Predicting whether a developer uses R or Python.

He came up with some interesting observations based on the data: When I had corresponded with Alex Martelli, Googler and Stack Overflow lord, he had explained to me why Google had started with a few languages they officially supported.

Point being, and my general advice in all things, follow what you love, love what you follow, lead the pack, and love what you do.

One qualifying statement, although I’ve never been a tool first thinker, if you are working on something important it may not be the best time to experiment.

R vs Python for Data Science: The Winner is …

At DataCamp, our students often ask us whether they should use R and/or Python for their day-to-day data analysis tasks.

Both Python and R are popular programming languages for statistics.  While R’s functionality is developed with statisticians in mind (think of R's strong data visualization capabilities!), Python is often praised for its easy-to-understand syntax.

In this post, we will highlight some of the differences between R and Python, and how they both have a place in the data science and statistics world.  If you prefer a visual representation, make sure to check out the corresponding infographic ”Data Science Wars: R vs Python”.

The purpose was to develop a language that focused on delivering a better and more user-friendly way to do data analysis, statistics and graphical models.

There is also CRAN, a huge repository of curated R packages to which users can easily contribute.  These packages are a collection of R functions and data that make it easy to immediately get access to the latest techniques and functionalities without needing to develop everything from scratch yourself.

Nevertheless, Python for data science is rapidly claiming a more dominant position in the Python universe: the expectations are growing and more innovative data science applications will see their origin here.

While these figures often give a good indication on how these two languages are evolving in the overall ecosystem of computer science, it’s hard to compare them side-by-side.  The main reason for this is that you will find R only in a data science environment;

It’s great for exploratory work, and it's handy for almost any type of data analysis because of the huge number of packages and readily usable tests that often provide you with the necessary tools to get up and running quickly.

Make sure to install NumPy /SciPy (scientific computing) and pandas (data manipulation) to make Python usable for data analysis.  Also have a look at matplotlib to make graphics, and scikit-learn for machine learning.

They can communicate ideas and concepts through R code and packages, you don’t necessarily need a computer science background to get started.  Furthermore, it is increasingly adopted outside of academia.

You can easily share notebooks with colleagues, without having them to install anything.  This drastically reduces the overhead of organizing code, output and notes files.

As a common, easy to understand language that is known by programmers and that can easily be learnt by statisticians, you can build a single tool that integrates with every part of your workflow.

Each course is built around a certain data science topic, and combines video instruction with in-browser coding challenges so that you can learn by doing. You can start every course for free, whenever you want, wherever you want.

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