The data science industry is full of a variety of programming languages that offer solutions to business problems. According to a report by Forbes, data science roles have increased by 650% since 2012, and are expected to grow to a colossal number of 11.5 million by 2026. While every project in the domain may demand a different set of tools and functionalities, some programming languages are common across all applications. Data science courses also offer an introduction to some of the basic languages prevalent in the industry. Python and R are great programming languages to start for anyone looking to make a career in data science, however, following is a dedicated list of programming languages that are required for a successful career in data science:
Python is by far one of the most popular languages amongst data scientists, both in terms of utility and preferences of recruiters. With an increase in innovation in machine learning, artificial intelligence, and predictive analysis, the demand for professionals who have a thorough knowledge of Python and all its functions is in abundance. Python is not only used for web development but is widely used in scientific computing, data mining, and other related applications.
For the recruiters of data science and machine learning, R is one of the most demanded skill sets. It owes its popularity to being an excellent alternative to otherwise expensive statistical software such as MATLAB or SAS. R was designed by statisticians and scientists to make their work more comfortable and is frequently used to unlock patterns in large blocks of data. For all of its utilities in data management and manipulation, R is a must known programming language for engineers looking to take a plunge in the data science career.
SQL (Structural Query Language) is at the core of data storage and retrieval and is one of the most favorite programming languages amongst data scientists. It is used in dealing with massively large databases, and in reducing the turnaround time for online requests by its fast processing time. SQL has been used with data manipulation since decades and is an excellent addition to the skills required for data science and machine learning applications.
One of the most widely used and practical languages, Java is used by many companies to develop backend systems and desktop apps. The demand for skills in Java has been on the constant rise and is thus considered the primary language of the enterprise software stack. Java is an excellent choice for starting a career in data science, as it is a relatively simple and readable programming language. Not only is Java portable to a number of software platforms, but it has also witnessed the most significant rise in demand in the last few years, particularly for DevOps engineers, software engineers, and software architects.
SAS is a market leader in the commercial analytical space and is one of the most popular languages in the data science industry. Its user-friendly GUI (Graphical User Interface) has a plethora of statistical functions that come in handy during data manipulation. SAS is easy to learn and implement, and is a must have language for beginners in the data science industry.
Despite being the oldest language in the list, C continues to be the most popular programming interface for software developers across the globe. Since it is a source of most of the modern day languages, C can be a good starting point for anyone interested in pursuing a career in data science. It is relatively simple to use and requires less capacity, and has a variety of applications, from embedded systems in electronic devices to data analysis and manipulation in data science.
Matrix Laboratory, or MATLAB, was developed by Mathworks and is a fast, stable computing environment that provides users with algorithms for complex math computations. While usually regarded as a hard-core language for mathematicians and scientists dealing with complex systems, MATLAB finds its use in a myriad of data science applications. Owing to its extensive use in statistical analysis, MATLAB is a must for a career in data science.
Julia is a high-level programming language that was modeled to address the needs of complex numerical analysis and computational science. It is rapidly gaining momentum amongst data scientists, and for all the good reasons. It has a base library that is integrated with open sourced Fortran and C libraries for random number generation, linear algebra, string and signal processing. A dynamic programming language, Julia is formed by an integration of the Jupyter and Julia infrastructure, and thus provides its users with a robust browser-based graphical interface.
One of the best-known languages with one of the largest user bases, Scala is highly flexible and is functional enough to complement other programming languages. It was engineered to run on the JVM (Java Virtual Machine); therefore, anything written on Scala can also run on the platform that Java runs on. Due to its flexibility and ease of use, Scala is becoming a favorite tool for anyone building complex algorithms or practicing machine learning at a larger scale.
Developed by F# Software Foundation in association with Microsoft and open contributors, F# is a cross-platform, open source, functional-first programming language. It enables users to handle complex computational problems with a simple and maintainable code, thus making it a favorite of data scientists. F# can be used for a wide range of applications and is supported by an active community and industry-specific companies that provide professional tools.
With the ever increasing developments in the field of data science, it has become essential for developers to continually upgrade their skills to build relevant solutions. It is one of the fastest growing fields in the software industry and shows no signs of slowing down. While a career in data science also requires knowledge of statistics, algebra, calculus, and databases, these ten programming languages are guaranteed to offer the necessary kick-start to those looking for an introduction in the industry.