Data science has attracted many followers, a fact which is evident from the huge number of registrations in data science seminars, and workshops and not to forget data hackathons. Data scientist is one of the most lucrative jobs in the industry today and with the huge demand, there is a need for people who possess the required skills of programming and mathematical expertise.
Before gaining expertise, an aspiring data scientist must be able to make the right decision which programming language he/she will use for the job. There are a number of programming languages which can be used to write codes depending upon the task at hand. Here is a list of the top programming languages for advanced analytics, machine learning and data science.
Python is a simple, general purpose, a multi-paradigm programming language which relies on a huge number of libraries helping users to undertake a variety of tasks, including automation, multimedia, graphical user interface, databases, text, and image processing. Python is an easy language to learn and work with thus preferred by both students and recruiters. Python combines the interface with high-performance algorithms written in Fortran or C and has become the leading programming language for open data science being widely used in web development, scientific computing, data mining, and others.
R is an open source language and software environment widely used for statistical computing and graphics. Many recruiters demand R as a basic requirement in machine learning and data science. R offers a strong object-oriented programming skill which give it an advantage over other computing languages. R is hugely used to produce graphs and other mathematical symbols apart from creating arrays, data frames, vectors and matrices. R serves as an alternative to SAS and Matlab, the popularity of R can be widely understood by the fact that it has become the favourite choice for companies like Facebook and Google.
Structured Query Language (SQL) is used to deal with large databases, and is particularly helpful in updating, querying and manipulating databases. SQL is used in managing particularly large databases and reduces the turnaround time for online requests with its fast processing time. An efficient data scientist has to extract and wrangle a lot of data from the database, for that purpose, the knowledge of SQL is a must. SQL is an easy programming language to learn with easy to understand syntaxes, like SELECT name FROM users WHERE age > 30.
MATLAB is a numerical computing language developed and licenced by Mathworks designed for numerical computations with similar context to Python. Based off C, C++, and Java programming languages, MATLAB is a quick, stable and ensures solid algorithms for numerical computing language used by entire academia and industry. MATLAB is considered to be a well-suited language for mathematicians and scientists dealing with sophisticated mathematical needs like image processing, matrix algebra and signal processing.
Scala (scalable language) is a general-purpose, open source programming language having one of the largest user bases. Scala is an ideal choice of language with programmers who are working on high-volume datasets as it offers full support for functional programming and a strong static type system. Users can use Scala in conjunction with Spark, making Scala an ideal programming language when dealing with large volumes of data. Scala supports both OOP and functional programming and can be used to write web apps.
Julia is a high-level dynamic programming language tailor made for computing and numerical analysis. Julia is capable of general-purpose programming and has become a perfect choice for dealing with complex projects containing high volume datasets. For coders who like Python’s syntax working with humongous amounts of data, Julia is the next programming language they would want to learn.
The landscape of data science is evolving quickly, as businesses learn how important is data for them with the requirement for data scientists continue to grow. Adept skills and knowledge of data science languages will not only take a user’s data science career to new heights, it will also be a boom to organisations who seek to implement data science projects. We hope this conclusive list would have helped data scientists and data science enthusiasts!