Is R Programming Enough for Data Science?

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Updated January 3, 2018

There is a lot of talk about the data science industry right now and with good reason. It is on of the fastest growing tech industries with a project 2,700,000 jobs to come into the market over the next decade.

With so many jobs coming available it is no wonder there are so many inquires about what skills and certifications are needed to land one of those jobs in the future.

Recently one of our subscribers asked,

“For a starter, is R and SQL knowledge enough?”

The short answer is no. In order to understand why, let me breakdown the programming language “R”.


1 ) What is R?

R made its first appearance in 1993, created by Ross Ihaka and Robert Gentleman. The language was developed for statistical computing, which is why it is extremely popular in the data science world.

Fun Fact: The original source for the code was written in C

R and its libraries have a variety of statistical and graphical techniques, including linear and nonlinear modeling, classical statistics testing, time-series analysis, classifications, clustering, and more.


2 ) What is R used for in Data Science? 

R is used to for heavy data lifting. Very large sets of data that need to be extracted.

Say you want to pull data from the World War II casualties records of all Germans, Americans and British. You would have to run a program in each on of these databases. But what if there is no “database” to be found. What if all you can find is lists of names scattered across multiple countries.

R would allow you to do a statistical analysis on the text documents in order to scrub through the data in order to find the amount of fallen men from each nation. R allows you to deconstruct the document into names, dates, categories, etc. When you start an analysis in R you may not know where it will take you, but R makes it easy to explore different ideas about the data quickly.

Curious About all the terms and jargon in the data industry. Check out of Full Series on Data Jargon

Side note: Python is made for data manipulation (squeezing data into charts and graphics for presentations) and working on repeated tasks (running a monthly log of product purchases and logging where the products were purchased, what demographic bought the product, etc…)

You see R is really great for one time searches into specific data sets. When created its purpose was to perform or solve statistical computing problems or ideas.


3 ) Should I Learn R?

The R programming language is extremely useful for the data analyst and data scientist. The brief examples stated above help you to understand the depth and complexity that R lends you when solving and extracting large data sets. But if you are wondering if learning R is the best way to break into the industry the answer is NO. Allow me to quote Dan Kopf from Quartz Media

R… is good for statistics-heavy projects and one-time dives into a dataset. Take text analysis, where you want to deconstruct paragraphs into words or phrases and then identify patterns. “I often don’t know where I’ll end up when I start a process like that, and R makes it easy to try a lot of different ideas quickly,” Groskopf says.

As you see learning R helps you attack complex and unique data sets. If you are wondering if you should dive into the data industry by learning R, the answer is NO. You should learn the foundations. Get started with the basics.

There is a lot of Statistical Mathematics, Microsoft Excel, and SQL knowledge that will benefit you before you dive deeper into the data science industry.

I would highly recommend taking the Data Analytics Masters Course by edureka in order to get a strong foundation in the basics of data analytics before you try to pile on loads of coding knowledge that you don’t know how to apply to any data sets.

R is an incredibly powerful language and I admire you curiosity and courage to take on learning code. It will be challenge, but ultimately the most reward pursuit.


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