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As R programming language becoming popular more and more among data science group, industries, researchers, companies embracing R, going forward I will be writing posts on learning Data science using R. The tutorial course will include topics on data types of R, handling data using R, probability theory, Machine Learning, Supervised – unSupervised learning, Data Visualization using R, etc. Before going further, let’s just see some stats and tidbits on data science and R.

"A data scientist is simply someone who is highly adept at studying large amounts of often unorganized/undigested data"
“R programming language is becoming the Magic Wand for Data Scientists”

Why R for Data Science?

Daryl Pregibon, a research scientist at Google said- “R is really important to the point that it’s hard to overvalue it. It allows statisticians to do very intricate and complicated data analysis without knowing the blood and guts of computing systems.” 

A brief stats for R popularity

“The shortage of data scientists is becoming a serious constraint in some sectors”
David Smith, Chief Community Officer at Revolution Analytics said –
“Investing in R, whether from the point of view of an individual Data Scientist or a company as a whole is always going to pay off because R is always available. If you’ve got a Data Scientist new to an organization, you can always use R. If you’re a company and you’re putting your practice on R, R is always going to be available. And, there’s also an ecosystem of companies built up around R including Revolution Enterprise to help organizations implement R into their machine critical production processes.”
The original blog can be seen here: dataperspective.

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Comment by Leigh Sneddon on January 1, 2016 at 7:30pm

Are there any free libraries of R code that do things like tree classification, logistic regression, support-vector matching, entropy-based variable selection, classification using linear discriminant functions, cross-validation to control over-fitting?

These are core activities and it would seem odd for every data scientist to be writing his/her own coded versions.

Thanks! 

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