R moves up to 5th place in IEEE language rankings
This article was posted by David Smith on revolution analytics.
IEEE Spectrum has just published its third annual ranking with its 2016 Top Programming Languages, and the R Language is once again near the top of the list, moving up one place to fifth position.
As David said last year (when R moved up to take sixth place), this is an extraordinary result for a domain-specific language. The other four languages in the top 5 (C, Java, Python amd C++) are all general-purpose languages, suitable for just about any programming task. R by contrast is a language specifically for data science, and its high ranking here reflects both the critical importance of data science as a discipline today, and of R as the language of choice for data scientists.
IEEE Spectrum ranks languages according to a large number of factors, including search rankings and trends, social media mentions, and job posting. (You can adjust the weighting of these factors to generate your own rankings using this interactive tool.) It also includes scholarly citations of the languages, a factur that influenced R's rise in this ranking
To read the full article, click here. For more articles about R, click here.
DSC Resources
- Career: Training | Books | Cheat Sheet | Apprenticeship | Certification | Salary Surveys | Jobs
- Knowledge: Research | Competitions | Webinars | Our Book | Members Only | Search DSC
- Buzz: Business News | Announcements | Events | RSS Feeds
- Misc: Top Links | Code Snippets | External Resources | Best Blogs | Subscribe | For Bloggers
Additional Reading
- What statisticians think about data scientists
- Data Science Compared to 16 Analytic Disciplines
- 10 types of data scientists
- 91 job interview questions for data scientists
- 50 Questions to Test True Data Science Knowledge
- 24 Uses of Statistical Modeling
- 21 data science systems used by Amazon to operate its business
- Top 20 Big Data Experts to Follow (Includes Scoring Algorithm)
- 5 Data Science Leaders Share their Predictions for 2016 and Beyond
- 50 Articles about Hadoop and Related Topics
- 10 Modern Statistical Concepts Discovered by Data Scientists
- Top data science keywords on DSC
- 4 easy steps to becoming a data scientist
- 22 tips for better data science
- How to detect spurious correlations, and how to find the real ones
- 17 short tutorials all data scientists should read (and practice)
- High versus low-level data science
Follow us on Twitter: @DataScienceCtrl | @AnalyticBridge
Comment
-
Comment by Jane Roughan on January 2, 2017 at 11:53pm
-
For me, I tend to use R + Python + D3 and Javascript for visualisations and SQL for quick analysis of operational data.
When I want to perform a quick analysis of files, I tend to use R. Previously for visualizations I have used D3 and Python as preferred, but have noticed an increase in vis packages for R of late.
When I want to multi-thread running of complex, time-consuming algorithms, I prefer Python with Spark.
But I am relatively new comer to data science in the last 1 and 1/2 years.
-
Comment by Vincent Granville on December 28, 2016 at 11:35pm
-
Even more interesting, what are the most popular language combinations? For data scientists, R + Python + SQL is probably one of the most popular. Usually though, multi-lingual programmers master and use one programming language more so than the other ones (in my case, Perl over R, SQL, SAS, Javascript, C, Shell command language and LaTeX -- even though my "native" language was C; and I use R for dataviz only not for machine learning as I have developed my own simple, scalable algorithms over time for pretty much any statistical procedure such as regression or clustering)
-
-
It would be interesting to compare this language ranking table with the one published here (displayed below). The one below does not include R, possibly because it does not consider R (or SQL or SAS or Matlab for that matter) as a programming language. Of course the methodologies to produce the two tables are very different.
Related TechTarget Content
- 10 Scrum benefits and how to realize them
- 5 proven patterns for resilient software architecture design
- Microsoft's acquisition of Nuance clears antitrust hurdle
- The right to disconnect vs. America's always-on culture
- The enterprise advantages of automated data collection
- Today's blockchain use cases and industry applications
- Salesforce Industries rolls out banking AI compliance tools
- DataRobot integrates AI modeling tools with Snowflake
- M&A wave rising in hot AI market
- AWS analytics helps nonprofit fight climate change
Posted 17 June 2021
© 2021 TechTarget, Inc.
Powered by
Badges | Report an Issue | Privacy Policy | Terms of Service
You need to be a member of Data Science Central to add comments!
Join Data Science Central