There are so many programming languages out there, it seems like data scientists need their own Tower of Babel to understand them all. According to a 2016 poll by KDnuggets, the most popular programming languages were, in order, R, Python, Java, Unix shell/awk/gawk, C/C++, and Scala. The highest percentage of users was for R and Python with 49% and 45.8% shares.
The Most Popular Tools For Data Science
The 17th annual KDnuggets Software Poll asked “What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months?” 2016 % share – % of voters who used this tool, % change – change vs 2015 poll, and % alone – voters who used only the reported tool among all voters who used that tool.
With so many programming languages to learn and use, it can be difficult to decide which one is right for your project. Here, we’ll talk about four useful programming languages and discuss each one’s strengths and weaknesses.
Bank of America uses Python to build new products and interfaces within the bank’s infrastructure and to crunch financial data, according to DZone data. Python, is popular because it is accessible. It helps programmers build products and lets them customize multiple features. Python is best for medium-scale data processing; it often doesn’t perform well on core infrastructures or large projects.
Java is one of the big ones, the programming language used in Silicon Valley. Java is so well-known because it works: it does best on large systems with its well-developed tools that help boost productivity. One of the benefits of Java for data scientists is that its code looks the same on many platforms. Java fails, though, if you want to use visualizations or perform statistical modeling.
“Microsoft acquired Revolution Analytics (the leading commercial provider of software and services for R) in early 2015, and started building R functionality into Microsoft’s core tools like SQL Server and AzureML. This made R capable of not only scientific research, but also commercial implementation language,” – Igor Isupov, Senior Data Scientist at Ciklum
R got its start nearly two decades ago as an inexpensive alternative to costly statistical modeling software. But now, plenty of data scientists in companies like Google, Bank of America, and Facebook are helping spread R’s popularity across sectors. Just a few lines of code help you create streamlined graphics, data sets, and modeling manipulations. But watch out. Some say R can be slow and clunky when working with large data sets.
One of the newest programming languages available, Julia has the potential to be insanely fast and uniquely expressive. While Julia is mostly underused, it has potential to be more scalable, easy-to-learn, and faster than either Python or R. While Julia will be a powerful language in the long term, though, it doesn’t have the tools and packages needed to compete with the big dogs right now.
“Julia’s community is simply not big enough (yet) so when you get into trouble or bug it’s not as easy to find an answer at resources like Stackoverflow as it is for R/Python/Java. Compare 1,500 packages for Julia vs 11,000 packages for R vs 100,000 Python packages. Of course, one does not need all of those and sometimes 10 packages are enough for daily routine. Still, these numbers speak for themselves,” adds Igor.
If you’re not an expert in a programming language that makes the most sense for your project, you don’t have to use the second-best option. Ciklum engineers have expertise in nearly every programming language. Contact us today and we’ll help you choose the one that will help you complete your project most effectively.