AI News, Why you should learn R first for data science
Why you should learn R first for data science
Over and over, when talking with people who are starting to learn data science, there’s a frustration that comes up: I
There is an ever widening range of tools and programming languages and it’s difficult to know which one to select.
When I started focusing heavily on data science a few years ago, I reviewed all of the popular programming languages at the time: Python, R, SAS, D3, not to mention a few that in hindsight, really aren’t that great for analytics like Perl, Bash, and Java.
Even today, I just read a suggestion (by a well known data science blogger) to use arcane tools like UNIX’s Awk and Sed.
similar ranking by TIOBE (which ranks programming languages by the number of search engine searches) indicates a strong year over year rise for R.
Solomon is a data scientist at Facebook, and his blog posts demonstrating R are excellent.) As Revolution Analytics recently noted, “R is also the tool of choice for data scientists at Microsoft, who apply machine learning to data from Bing, Azure, Office, and the Sales, Marketing and Finance departments.”
It is also very popular among academic scientists and researchers, a fact attested to in a recent profile of the R programming language in the prestigious journal Nature.
The supply of academics, PhDs, and researchers who leave academia for business will create it’s own demand for people with R.
Moreover, as data science matures, data scientists in the business world will need to communicate more with academic scientists.
As we instrument the planet and transform the world into data-flows, the lines between academic science and business-oriented data science will likely blur.
To do this, you’ll need to master the 3 core skill areas of data science: data manipulation, data visualization, and machine learning.
Moreover, when you combine ggplot2 and dplyr together (using the chaining methodology), finding insight in your data becomes almost effortless.
While I think most beginning data science students should wait to learn machine learning (it is much more important to learn data exploration first), machine learning is an important skill.
When you’re ready to start using (and learning) machine learning, R has some of the best tools and resources.
One of the best, most referenced introductory texts on machine learning, An Introduction to Statistical Learning, teaches machine learning using the R programming language.
Just like there’s no single best tool in a toolbox, there’s no single programming language that’s perfect for every data problem you want to solve.
Having said that, after you master the core skills in data science in R, you’ll probably want to learn other languages to solve specific problems.
Here’s a quick review of other languages you might consider after you learn R: Python is a great multi-purpose programming language that you should definitely consider at some point.
won’t scale under circumstances where you have to support dozens of partners with new analyses and ad-hoc requests.
I’m also optimistic that R’s ggvis will allow R users to create highly dynamic and interactive visualizations, so at some point, R users may be able to learn R’s ggvis instead of D3.
Seeing other people create great work (and finding out that they’re using a different tool) might lead you to try something else.
Spending 100 hours on R will yield vastly better returns than spending 10 hours on 10 different tools.
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.
This 30 page guide will show you how to install R, load data, run analyses, make graphs, and more.
Swirl provides exercises and feedback from within your R session to help you learn in a structured, interactive way.
These free events teach you how do do useful things in R, and we’re always making more. Our previous webinars are archived into several tracks for your viewing convenience: Tip: Look at our two-part series on “Working with the RStudio IDE” at DataCamp to master all features of the IDE.
This book will teach you how to use the most modern parts of R to import, tidy, transform, visualize, and model data, as well as how to communicate findings with R Markdown.
You can search for R packages and functions, look at package download statistics, and leave and read comments about R functions.
If you need a quick reminder about how to wrangle data, make a graph, or do some other common task in R download one of our free R cheat sheets.
Or, take Hadley’s online course “Writing Functions in R” where he teaches you the fundamentals of writing functions in R so you can make your code more readable, avoid coding errors, and automate repetitive tasks.
Learn R Programming
Some of the popular alternatives of R programming are: Python is a very powerful high-level, object-oriented programming language with an easy-to-use and simple syntax.
While R is the first choice of statisticians and mathematicians, professional programmers prefer implementing new algorithms in a programming language they already know.
If you are trying to analyze a dataset and present the findings in a research paper, then R is probably a better choice.
But if you are writing a data analysis program that runs in a distributed system and interacts with lots of other components, it would be preferable to work with Python.
- On Saturday, August 24, 2019
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