Which Programming Language is Best for Data Science?

Programming languages are the foremost requirement for creating any software. Between Syntax and semantics, it is essential to understand the importance and nuances of each language to apply the right one for the right software. We will look at the most popular languages these days and see if they would be a good fit for data science or not. Each year brings new technology, business complexities and innovations that spur on a new set of languages and frameworks. It is essential to understand for new programmers especially, the upskilling requirements for each coding. The key is to understand the level of usage that you would need for general purpose or specific purposes along with optimum performance and productivity.

Each year, KD Nuggets, leading business analytics, big data and data analytics website conducts a poll on the best programming language for Data Science. The survey threw R, Python, SAS, MATLAB, SPSS, My SQL and Java as the top contenders.

Each and few of these languages have their limitations. MATLAB, SPSS and SAS are expensive programs to learn, whereas Java is difficult to learn. My SQL and Hadoop languages are mostly related to databases which leave R and Python from the list. Compared to R, Python is a more straightforward language for a beginner to learn.

Why Python?

Apart from the fact that it is simpler to learn, Python uses daily English to write commands. With so many resources available everywhere, data sciences applications are not hard to come by. The language is readily available from introductory courses in MIT for programming to its widespread use in NASA.

Data science programming involves interweaving various aspects such as programming as per the internet, working with network applications, scripting and automating data processing jobs. Also, real-world data requires a lot of scrubbing with the removal of extreme variances and information from various sources. Python is a versatile language which allows support to various operations from mathematical to high-level data structure and data analysis. It has sections called as “Libraries” which would enable these multiple options to choose from. Some of the most commonly used libraries are - Numpy, Scipy, Pandas, Matplotlib, Scikit-learn, Nltk (Natural language Toolkit), Scrappy for web scraping, Pattern for web mining, Theano for deep learning. One can run operations such as SVM, Linear regression, Logistic regression on a data-set using Scikit-learn.

Python is also used for integration of data analysis tasks and web applications. Also used when production databases require incorporation of statistical code. For mathematically aligned programs, R is a known language, yet the learning curve is somewhat steep when compared to something simple like Python.

R Programming has its own set of supporters who swear by the intricacies it has to offer. By far out, Python has gained the top spot in the number of job opportunities, compensation and usage in software.

Another way of seeing this debate is the positives of Python makes it better for data manipulation and repetitive tasks, whereas R stands out for ad-hoc analysis and datasets exploration. For activities in pulling data, running automated analysis repeatedly, creating visualisations like maps and charts, Python is a boon. However, in a customised set of activities such as statistics massive projects, an analysis which require heavy dataset deep diving, R is the champ. R is essential when the data analysis tasks need standalone computing or analysis on individual servers.

Slowly but surely, this distinction between the two languages is breaking down, and one language can do many tasks of another. This will make choosing the language easier, and the commonly used language in your business or sector will make the difference over a period.