How to Choose the Right Programming Language for Data Science
For freshers and graduates looking to break into the world of data science, picking the right programming language can be a make or break decision. This guide to programming languages and their uses will help enhance students’ knowledge of data science-related languages and encourage them to make the right choice.
Data scientists, in particular, use high-level programming languages. These are primarily used to build analytics tools and technologies that help data scientists and other professionals extract insights from massive datasets and provide value to the business they’re affiliated to.
The difference between programming languages in data science and regular software development is that most languages can build software but data science-oriented languages can process, scrutinise and create forecasts from a given data dump. Data-centric programming languages are the backbone of building and processing algorithms that can get as specific as required by the field of data science.
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This high-level programming language is one of the most versatile as it contains a plethora of libraries that cater to different roles. It is considered easy to use as it is interpreter-based and has high levels of readability. The dynamic language has been around for nearly 30 years now and is used both by small businesses and industry titans like Google, Mozilla, Facebook and Netflix. Indeed also ranked it the third most profitable programming language in the world– yet another reason for it being so popular in the programming community.
When it comes to exploration of datasets and ad hoc analysis, R scores more points with data scientists. Yet another open-source programming language, R is geared towards statistical computing. It is also a key player in the process of developing numeric analysis and machine learning algorithms. It is often referred to as a ‘glue’ language, a reference to its role in connecting datasets, software packages and tools.
Java is another object-oriented, general-purpose language. This language tends to be highly versatile and is used in computer embedding, web applications and desktop applications. Java may seem to be disconnected to data science; however, there are many frameworks, including Hadoop, which run on JVM and constitute an integral part of the data stack. Hadoop is a software method for data processing and storage in distributed structures for large data applications. It allows large amounts of data to be processed and possesses the ability to handle virtually limitless tasks at once, thanks to its higher processing power.
This domain-specific language is most used for handling data within a relatable database management system. Databases are quite often the backbone of software or an application and are instrumental in determining just how well dependent technologies perform. The more commonly used databases are Oracle, MariaDB, MySQL and PostgreSQL.
Scala has been designed to address many of Java’s problems. Again, from web applications to machine learning, this language has many different uses, but this vocabulary mostly includes the development of the front end of applications. As the term itself is an approximation of “scalable language”, a nod to the fact that the language is considered to be scalable and, hence, perfect for processing big data.
Each of these languages have their indicative purposes, eg: Scala for front-end applications and R for statistical analysis. Thus, the final decision on which programming language to choose, depends on the student’s field of interest (front-end, statistical analysis, back-end etc), and the uses and benefits of the language in the said field. Check out the data science courses from Great Learning to upskill in this domain.
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