Six Programming Languages for Data Scientists in 2020

Posted by Niti Sharma on December 27th, 2019

Data science is a multi-disciplinary domain. What it means is, to be a good data scientist you have to be a jack of all trades—statistics, mathematics, computer science and more. Programming underlies all major tasks performed by data scientists – data collection, manipulation, analysis, visualization, etc. Therefore, a handful of programming languages are rudimentary to establish a strong base in data science. 

 The gist of the tale is – become good at programming. The increasing demand and supply gap in the data science industry is another factor that calls for data scientists to become proficient in programming languages. 

Python – Python is the most popular language in the data science community. It’s an open-source and object-oriented language, which makes it easy to learn. Python’s large set of libraries specifically meant for data manipulation and analysis makes it a good choice for data scientists.  

 Additionally, Python has a large community where data scientists and developers can get their queries resolved.  

 Python has consistently remained the top language in the data science industry for the past few years, as per Stackoverflow’s annual reports. 

2. R – After Python, R is the second-most widely used language for data science applications. It is a vector language that allows data scientists to perform iterative tasks without calling a function (as is the case with most object-oriented languages). Various packages available with the language make it a good fit for statistical modeling and analysis. 

 With increasing popularity, R is finding applications in genetics, biology, pharmacy and more. 

3. SQL – This is a primary language to extract data from relational databases. As a large portion of the industry still relies on large scale relational databases to store and manage data, it is imperative for data scientists to SQL inside out. Working Data Scientists frequently use SQL to retrieve data and wrangling. 

4. Java – Java is still the go-to language for the development of enterprise-grade applications. Some Big Data software like Splunk, Flink, Hadoop, and Hive are built with Java. 

 The language offers a set of libraries and tools for machine learning, deep learning, and data manipulation and analysis. ADAMS, for instance, offers data mining and machine learning capabilities, while DL4j is extensively used for machine learning in data science. 

5. Scala – This language is an extension of Java. It runs in the JVM (Java virtual machine) environment and thus offers the benefits of Java packages. Further, the language combines benefits of objected-oriented and function programming, making it easy for data science professionals to leverage advance functionalities like string comparison, and pattern matching. 

6. Julia – Availability of over 1900 packages make Julia a formidable language for data science. It offers speed and ease of use, and its packages can easily interact with other languages including R and Python. 

 In addition to the above languages, C++ and MATLAB are also gaining popularity among Data Scientist for their simplicity and ease of use. Inevitably, as data science thrives new languages will be invented and keep coming to the fore. 

The conjecture is – data science professionals need to keep upskilling themselves to stay on top of their game with tutorials, online courses, and best data science certifications. Further, taking projects outside their scope of job role will increase exposure to new languages and thereby increase employability and job opportunities.