Switch from SAS, SPSS or STATA to R with our latest course

If you already know SAS, SPSS or Stata, you don’t need to spend time learning how to analyze data. You need a course that focuses on translating your knowledge into R. A course that facilitates switching from SAS, SPSS or STATA to R. That’s why DataCamp’s latest interactive course focuses on statisticians, data analysts, academic institutions, and companies that are switching (or planning to switch) from these commercial statistical software packages to the free and powerful language R.

Like all DataCamp courses, this new course is self-paced, and offers you a great learning experience via a unique combination of challenging interactive exercises and to the point videos. It is given by Bob Muenchen, one of the leading instructors in the R community, and author of R for SAS and SPSS Users (Springer) and R for Stata Users (Springer).

Supplementary to the course content, Bob offers free email support to all course subscribers. Furthermore, many online classes are yours for only 30 days, or for as long as you make an annual payment. This course is yours “forever”.  So you can always go back to all the course materials when you need a refresher or some additional information.

Check out the full course, or take the free preview.  

About an introduction to R for SAS, SPSS, and STATA users

R is a free and powerful software for data analysis and graphics that is rapidly disrupting the market for data analytical tools and software. It is flexible (no need to wait 6 months for updates), extremely comprehensive (over 6000 packages), cross-platform, and has a great community. However, if you come from another statistical software tool it can be a challenge to master the versatility of R. Enter DataCamp’s new interactive course Introduction to R for SAS, SPSS, and STATA Users. An ideal course for those switching from SAS, SPSS or STATA to R. This course:

  • Introduces R jargon using language you’re familiar with.
  • Points out the errors you’re most likely to make. For example, many R functions let you specify which data set to use in a way that looks identical to SAS, but which differs in a way that is likely to lead to perplexing error messages.
  • Demonstrates add-on packages that produce output that is similar your current software’s. R’s built-in functions tend to provide surprisingly sparse output.
  • Covers material to help you migrate to R, or to integrate the use of R into your current software.

In total the course contains over 16 hours of material, 20 chapters, and over a 120 interactive exercises.

Check out the full course, or take the free preview.  


About the instructor

Robert A. Muenchen is the author of R for SAS and SPSS Users and R for Stata Users. He is a consulting statistician with over 30 years of experience and is currently the manager of the Research Computing Support at the University of Tennessee. Bob has conducted research for a variety of public and private organizations and has assisted on more than 1,000 graduate theses and dissertations. His workshops have been attended by people from over 500 organizations. He has written or co-authored over 70 articles published in scientific journals and conference proceedings.

Bob has served on the advisory boards of the SAS Institute, SPSS Inc., the Statistical Graphics Corporation and PC Week Magazine. His suggested improvements have been incorporated into SAS, SPSS, JMP, STATGRAPHICS and several R packages. In other words, he is the ideal instructor if you want to switch from SAS, SPSS or STATA to R.

About DataCamp

The course is set up in DataCamp’s interactive learning platform that aims to enhance your learning experience by allowing you to learn by doing. All the concepts that are introduced during the video lecture are directly tested through challenging interactive assignments with tailored feedback to consolidate your knowledge step by step. You will effectively learn hands on instead of losing time with suboptimal solutions like a four-hour screencast or webinar.

Check out the full course, or take the free preview.  

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Revolution R Enterprise tutorial: Free 8h interactive tutorial on Big Data Analytics

In need for better ways to handle large data sets? Interested in manipulating, visualizing, and analysing large datasets with RevoScaleR? Then make sure to have a look at this free hands-on Revolution R Enterprise tutorial on Big Data Analytics by Revolution Analytics and DataCamp. Everything takes place in the online interactive learning interface of DataCamp, so no need to do any installations. We’ve set-up the Revolution R Enterprise (RRE) software in the cloud, allowing you to explore the power of Revolution R Enterprise and big data analytics in the comfort of your own browser via a live R environment.

revolution r enterprise tutorial

No hassle, just learning. All the material is presented by short videos and slides to explain major elements. In order to consolidate your learning, every section ends with interactive exercises that let you practice the covered concepts while giving you tailored feedback. Like all DataCamp tutorials and courses, this is a stand-alone tutorial that can be taken wherever you want, whenever you want. You have unlimited access to videos, slides and related content.   

Course content

This interactive tutorial on Big Data Analytics (>8hours of material) gets you started with RRE and the RevoScaleR package, and is ideal for accomplished R users and data analysts that want to experience the functionality of Revolution R Enterprise. Learn how to use RRE to process, visualize, and model terabyte-class data sets at a fraction of the time of legacy products without requiring expensive or specialised hardware. This free Revolution R Enterprise enterprise tutorial covers:

  • The RevoScaleR package that ships with Revolution R Enterprise, and how it deals with Big Data challenges.
  • How to summarize, cross-tabulate and visualise variables in large data sets.
  • How to manipulate and transform large data sets.
  • Building statistical and machine learning models on large data sets.

This is just the first of many other Revolution Analtyics courses that will follow. New courses such as fundamentals of the R programming language, Introductory Statistics with Revolution R Enterprise, Predictive Modeling, and Advanced R Programming, are already in development.

Stay tuned for more, and don’t forget to give feedback on the current course!

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ggvis tutorial: become a data visualization expert with RStudio

The latest interactive course in the RStudio track is now available on DataCamp: ggvis tutorial. The first part of the tutorial is available for  free, so everyone can now learn interactively how to start creating stunning ggvis data visualizations in R. All courses in the RStudio track are self-paced, and combine challenging interactive exercises with to the point videos. Garrett Grolemund, master instructor at RStudio and R enthusiast, is your guide along this journey.

Check out the full course, or take the free preview.

ggvis tutorial

What is ggvis?

ggvis is the new standard tool for data visualization in R by RStudio. It lets you create static and interactive graphs to display distributions, relationships, model fits, and more. Similar to ggplot2, ggvis uses the grammar of graphics. The grammar provides an intuitive framework that lets you describe – and make – any plot that you can think of in your head. By learning the four components of the grammar, you empower yourself to make thousands of different types of ggvis data visualizations.

Best of all, ggvis plots are true web documents. You can save them as png’s for publication, but they come ready to be shared over the internet. Each ggvis plot can be viewed in a web browser, which opens opportunities not available in R’s native graphics device. For example, with a one or two lines of code, you can turn a ggvis plot into an animation or an interactive data exploration tool. This enables you to do rich data visualizations for analytics, communication and the web.

What is the ggvis tutorial?

This interactive ggvis tutorial will teach you how to use the ggvis package to make data visualizations like a pro. You’ll learn how to use the grammar of graphics to turn your data into sophisticated, layered graphics; and how to customize those graphics. Along the way, you will learn how to visualize statistical transformations of your data, as well as how to add interactive components to your graphs, such as sliders, checkboxes and more. Multiple ggvis examples are provided. Topics covered are:

  • Chapter One: The Grammar of Graphics
    Learn the philosophy that guides ggvis and discover a clear, logical way to think about data visualization.
  • Chapter Two: Lines and Syntax
    Examine each part of the grammar and learn the special syntax that ggvis introduces to make it easier to think about plots.
  • Chapter Three: Transformations
    Learn to build statistical transformations with ggvis’ compute functions, visualize the results, and how to integrate the dplyr package.
  • Chapter Four: Interactivity and Layers
    Create graphs that can be controlled through sliders, text fields, and other widgets. Build sophisticated, multi-layered graphs.
  • Chapter Five: Customizing Axes, Legends and Scales
    Change the appearance of axes and legends in your plots, and use ggvis’ scale system.

The DataCamp ggvis tutorial provides 10 videos, 30 exercises, and a surprise interview with one of the co-creators of ggvis. Go to the full course, and take the free preview. 

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The data.table R package cheat sheet

The data.table R package provides an enhanced version of data.frame that allows you to do blazing fast data manipulations. The data.table R package is being used in different fields such as finance and genomics, and is especially useful for those of you that are working with large data sets (e.g. 1GB to 100GB in RAM).

Although its typical syntax structure is not hard to master, it is unlike other things you might have seen in R. Hence the reason to create this cheat sheet. DataCamp’s data.table cheat sheet is a quick reference for doing data manipulations in R with the data.table R package and syntax, and is a free-for-all supplement to DataCamp’s interactive course Data Analysis the data.table Way.

data.table R package tutorial cheat sheet

The cheat sheet will guide you from doing simple data manipulations using data.table’s basic i, j, by syntax, to chaining expressions, to using the famous set()-family. You can learn more about data.table at DataCamp.com or read all about it in this data.table tutorial post.

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Become a data scientist in 8 steps: the infographic

This post was written by the team behind DataCamp, the online interactive learning platform for data science.  

After being dubbed “sexiest job of the 21st Century” by Harvard Business Review, data scientists have stirred the interest of the general public. Many people are intrigued by this job, namely because the name has an interesting ring to it. But it is exactly the name that also raises a lot of questions. Because what is a data scientist and what do data scientists do exactly? Many of us who devote their lives to data science have frequently been confronted with questions like these.

The answers to these questions are mostly not as straightforward as you would expect: a short search on Google with the string of words “How to become a data scientist” shows that the concept has different meanings to different people. In addition, many articles indeed suggest various tools, courses and applications for people to become a data scientist, and with good reason: the options are unlimited. But let’s face it, for someone that is not familiar with the field, this advice may sometimes seem like a jungle of information. What’s more, they could work demotivating: the descriptions are sometimes fearfully long and the many details often hit the readers as an overwhelming avalanche.

DataCamp’s Guide to Become a Data Scientist

With all this in mind, DataCamp decided to help those who can’t see the forest for the trees: we designed a step-by-step infographic that clearly outlines how you can become a data scientist in 8 easy steps.  This visual guide is meant for everyone that is interested in learning data science or for everyone that has already become a data scientist but wants some additional resources for further perfection.  The infographic is called “Become a data scientist in 8 easy steps”. Have a look at it!

How to become a data scientist























Source: blog.datacamp.com

If you are thinking about becoming a data scientist, do not be taken aback by the eight steps that are presented in the infographic. We would like to emphasize that becoming a data scientist takes time and personal investment, but that the journey is everything but dull! And don’t forget, there are plenty of courses available to set you on the right way.

If you are already a data scientist, drop us a line at info@datacamp.com if you think of other steps that you have undertaken in your professional journey.

Feel free to share!

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<a href="http://blog.datacamp.com/how-to-become-a-data-scientist-in-8-easy-steps-the-infographic/" ><img src="http://blog.datacamp.com/wp-content/uploads/2014/08/How-to-become-a-data-scientist.jpg" alt="Become a data scientist in 8 easy steps" /></a><br/>Source: <a href="http://blog.datacamp.com">blog.datacamp.com</a><br/>
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Complete dplyr tutorial for data analytics and data manipulation in R

DataCamp just launched its latest interactive course: dplyr. This new course was developed in close collaboration with Garrett Grolemund, RStudio’s master instructor. By taking this dplyr tutorial, you will be challenged one step at a time to master the essentials about transforming data sets fast and intuitively with the dplyr package. Start the course here.

The dplyr package is an exciting new chapter in the mission to bring painless data manipulation to the crowd. It is an R package that provides you with a fast and intuitive way to transform data sets with R. dplyr is the successor of plyr and is mainly authored by Hadley Wickham and Romain Francois. It is designed to be intuitive and easy to learn, thereby making “doing things” in R more user friendly.

This dplyr tutorial introduces five key functions to straightforwardly manipulate data: select, mutate, filter, arrange and summarize. Thanks to optimization in C++, these functions allow you to work extremely fast with larger data sets. These ‘dplyr verbs’ can be understood as the atoms that combine to powerful molecular operations which can handle around 90% of data manipulation tasks. As such, dplyr lets you, as a data scientist, accomplish more things, with more data, in less time. However, dplyr isn’t limited to these five functions; it also enables automated groupwise operations in R, it provides a standard syntax for accessing and manipulating database data with R, and much more. All of this and more is covered and explained in this DataCamp course (check out the contents of the course).

To help you fully grasp the power and ease-of-use of dplyr, DataCamp has developed a brand new interactive course together with Garrett Grolemund. Garrett is a Data Scientist and Master Instructor at RStudio, holds a Ph.D. in Statistics, and specializes in teaching. He is the author of Hands on Programming with R, as well as Data Science with R, an upcoming book from O’Reilly Media. He taught people how to use R at over 50 government agencies, small businesses, and multi-billion dollar global companies.

dplyr tutorial
A video of Garrett Grolemund explaining the dplyr package in the DataCamp course


The dplyr tutorial

In the dplyr tutorial, you will learn how to use dplyr to perform basic data manipulation tasks using the five dplyr verbs, as well as combining these to solve challenging problems. You’ll also learn about groupwise operations using group_by(), about the pipe operator to chain your operations, and about the tbl structure which provides a cleaner layout so you can better understand your data. Finally, you will learn how to use the dplyr syntax to access data stored in a database outside R.

The course is set up in DataCamp’s interactive learning platform that aims to enhance your learning experience by allowing you to learn by doing. The course is comprised of 10 sections distributed over five chapters and each section has an instructional video by Garrett, followed by a vast set of interactive exercises. As such, the concepts that are introduced during the video lecture are directly tested through challenging assignments with tailored feedback to consolidate your knowledge step by step. You will effectively learn hands on instead of losing time with suboptimal solutions like a four-hour screencast or webinar.

dplyr tutorial
The DataCamp interactive learning environment

This is the first course of the RStudio datacamp track that will cover some of the company’s flagship products: dplyr, ggvis, rmarkdown, and the RStudio IDE. These other courses are scheduled to launch later this year.

So, if you want to learn more about the powerful dplyr package to solve challenging data analysis problems, head over to DataCamp and start right away!

dplyr tutorial

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New Course! A hands-on introduction to statistics with R by A. Conway (Princeton University)

The best way to learn is at your own pace. Combining the interactive R learning environment of DataCamp and the expertise of Prof. Conway of Princeton, we offer you an extensive online course on introductory statistics with R.  Start learning now…

Whether you are a professional using statistics in your job, an academic wanting a refresher on specific statistical topics, or a student taking statistics classes, this new DataCamp course will match your needs. It is a comprehensive and friendly course, that requires no background knowledge in statistics or R. The aim is to provide you with a solid foundation for future learning, as well as being able to put one’s work into context. All this takes place in your browser thanks to the DataCamp online learning environment. Try it for free!

Statistics with R

So, how does it all work? You can choose to subscribe to the course as a whole, or to take individual modules according to your own specific needs. The course consists of 7 modules, ranging from the Student’s T-test over ANOVA to simple and multiple linear regression, finally ending with a last module on Moderation and Mediation.  In total there are more than 250 interactive R exercises, which are accompanied by videos and slides. This adds up to 24 hours of material on statistics with R .

statistics with R

Interested?  To give you the opportunity to get a taste of the course content and to try out the DataCamp learning experience, we present you the first module for free. Furthermore, if you are a student, we want you to know that you get a  75% discount on the whole course.

So what are you waiting for? Grab this learning opportunity and check out the course! Remember that the first module is free, that you can buy separate modules according to your needs, and if you buy all 7 modules at once, you get a significant discount.  On top of that, students can get a 75% reduction on the whole statistics with R course.

On Professor Andrew Conway

Prof. Conway is a Senior Lecturer at Princeton and has been teaching to undergrads and graduate students for 20 years. His experience is reflected in the quality of this course. The content of this course has been on Coursera, and back then more than 200,000 individuals followed it, making it the second most popular Coursera course using R.  Psychology students at Princeton are already following the DataCamp course this semester.

On DataCamp

The course is set up in DataCamp’s interactive platform that aims to enhance the learning experience by offering a learning-by-doing approach. The material is presented by short videos and slides to explain major elements. In order to consolidate your learning, every section ends with interactive exercises that let you practice the covered concepts while giving you tailored feedback.

You will discover R’s capabilities and how they interplay with each other step by step. You can learn at your own pace, stopping to take a break or replay a segment at any time. The system tracks your progress so you can stop at any time; it will start up where you left off. This way, you will learn effectively instead of losing time with one-speed-fits-all solutions like a four-hour screencast or webinar. What’s more, in order to consolidate your learning, every section ends with interactive exercises that let you practice the covered concepts while giving you tailored feedback.



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Complete data.table tutorial: Data analysis the data.table way

Together with the key people behind the data.table package, Matt Dowle and Arun Srinivasan,  DataCamp developed a brand new interactive course to bring your data analysis skillset up to date with the essentials of the powerful data.table package. Learn more on the data.table tutorial… 

The popularity of the data.table package is increasing and with good reason. Not only is the number of package downloads rising rapidly, but data.table is also talk of the R town given the numerous presentations of Matt and Arun at conferences such as useR!2014, EARL, R/Insurance and R/Finance.

Data.table allows you to reduce your programming time as well as your computing time considerably, and it is especially useful if you often find yourself working with large datasets.  For example, to read in a 20GB .csv file with 200 million rows and 16 columns, data.table only needs 8 minutes thanks to the fread()function.  This  is instead of the hours it would take you with the read.csv() function. Once you understand its concepts and principles, the speed and simplicity of the package are astonishing!

On the data.table tutorial

However, to get the most out of data.table’s functionalities, you first have to overcome its learning curve: even though the syntax is not extremely difficult, it does take some practice to fully grasp it so its built-in functionalities can make your life easier. This is exactly why DataCamp has made an interactive online course on the data.table package for R and it has done so in collaboration with the key people behind it, namely Matt Dowle, main author, and Arun Srinivasan, co-author and major contributor. The data.table tutorial, which is unique as it is the only one of its kind, is called Data Analysis: the data.table way. It is designed to help you get started with the essentials of the data.table package. Among other things, you will learn all there is to know about operations such as selection and grouping in DT[i, j, by], and intermediate topics like chaining, setting keys and the different join types.

data.table tutorial

The course is set up in DataCamp’s interactive learning platform that aims to enhance the learning experience by centering on learning-by-doing. The course is supplemented by short videos and slides to explain major elements.  You will discover the functionalities and how they interplay with each other step by step. This way, you will effectively learn hands on instead of losing time with suboptimal solutions like a four-hour screencast or webinar. What’s more, in order to consolidate your learning, every section ends with interactive exercises that let you practice the covered concepts while giving you tailored feedback.

So, if you are looking for a qualitative course that brings you up to speed with one of the hottest packages in R today, go to DataCamp, take the data.table tutorial, and add the power of data.table to your data analytical skillset!

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New R course on Coursera: Data Analysis and Statistical Inference

Yesterday (Monday 1st of September), a new session of Data Analysis and Statistical Inference, taught by Doctor Mine Çetinkaya-Rundel from Duke university, has started on Coursera. Just like with the previous run, all labs take place in DataCamp’s interactive learning environment.

The R course Data Analysis and Statistical Inference will teach you how to make use of data in the face of uncertainty. Throughout the course, you will learn how to collect, analyze, and use data to make inferences and conclusions about real world phenomena. No formal background is required, but mathematical skills are definitely a plus.


The course makes intensive use of R for its statistical computing; its corresponding interactive exercises, available on DataCamp, were developed in close collaboration with doctor Çetinkaya-Rundel. In sum, this course is perfectly tailored to your needs if you are a starting data scientist and you are looking to expand your basic statistical knowledge.

We hope to welcome you in our online classroom and R course soon.

P.S. In case you prefer to complete this R course self paced in your own time, we recommend you to have a look at the open intro course. Here you can find similar material that is also supplemented with the interactive exercises of DataCamp.

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Computational finance with R course: an interactive tutorial

As of today (Tuesday 26th of August), a new session of Professor Eric Zivot’s course on computational finance and financial econometrics starts on Coursera. Just like the previous run of the course, most R labs and R assignments will take place in DataCamp’s interactive learning environment.  It’s a great course to get you started in doing finance with R.

Designed by Professor Eric Zivot (University of Washington), Introduction to computational finance focuses on mathematical and statistical tools and techniques that are used in quantitative and computational finance. With the help of real-life examples, you will be introduced to the dos and don’ts of financial data analysis, estimations of statistical models, the construction of optimized portfolios, and doing finance with R. The course requires no formal background, but some basic mathematical skills will definitely come in handy.


DataCamp’s interactive R exercises are developed in close collaboration with Professor Zivot himself.  They therefore have the same high-quality standards as academic courses, but presented in DataCamp’s fun and learning-by-doing environment. All students that choose to enroll for the course on Coursera will be directed to DataCamp to practice their skills and to complete assignments.

If you always wanted to learn more about computational finance, or if you are just interested in doing financial econometrics with R, this course is a must-do for sure. We hope to welcome you in our online classroom soon!

PS. In case you prefer to only do the interactive exercises, the course is also available on DataCamp as a stand-alone version which does require prior knowledge about finance,R or finance with R.

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