data science has become one of the most popular technologies of the 21st Century. With a high demand for Data Scientists in industries, there is a need for people who possess the required skills in order to become proficient in this field. Besides mathematical skills, there is a requirement for programming expertise. But before gaining expertise, an aspiring Data Scientist must be able to make the right decision about the type of programming language required for the job. In this article, we will go through some of the required data science programming languages in order to become a proficient Data Scientist.
Programming forms the backbone of Software Development. Data Science is an agglomeration of several fields including Computer Science. It involves the usage of scientific processes and methods to analyze and draw conclusions from the data. Specific programming languages designed for this role, carry out these methods. While most languages cater to the development of software, programming for Data Science differs in the sense that it helps the user to pre-process, analyze and generate predictions from the data. These data-centric programming languages are able to carry out algorithms suited for the specifics of Data Science. Therefore, in order to become a proficient Data Scientist, you must master one of the following data science programming languages.
Here is the list of top data science programming languages with their importance and detailed description –
It is easy to use, an interpreter based, high-level programming language. Python is a versatile language that has a vast array of libraries for multiple roles. It has emerged out as one of the most popular choices for Data Science owing to its easier learning curve and useful libraries. The code-readability observed by Python also makes it a popular choice for Data Science. Since a Data Scientist tackles complex problems, it is therefore, ideal to have a language that is easier to understand. Python makes it easier for the user to implement solutions while following the standards of required algorithms.
Python supports a wide variety of libraries. Various stages of problem-solving in Data Science use custom libraries. Solving a Data Science problem involves data preprocessing, analysis, visualization, predictions, and data preservation. In order to carry out these steps, Python has dedicated libraries such as – Pandas, Numpy, Matplotlib, SciPy, scikit-learn etc. Furthermore, advanced Python libraries such as Tensorflow, Keras and Pytorch provide Deep Learning tools for Data Scientists.
For statistically oriented tasks, R is the perfect language. Aspiring Data Scientists may have to face a steep learning curve, as compared to Python. R is specifically dedicated to statistical analysis. It is therefore, very popular among statisticians. If you want an in-depth dive at data analytics and statistics, thenR is the language of your choice. The only drawback of R is that it is not a general purpose programming language which means that it is not used for tasks other than statistical programming.
With over 10,000 packages in the open-source repository of CRAN, R caters to all statistical applications. Another strong suit of R is its ability to handle complex linear algebra. This makes R ideal for not just statistical analysis but also for neural networks. Another important feature of R is its visualization library ‘ggplot2’. There are also other studio packages like tidyverse and Sparklyr which provides Apache Spark interface to R. R based environments like RStudio has made it easier to connect databases. It has a built-in package called “RMySQL” which provides native connectivity of R with MySQL. All these features make R an ideal choice for hard-core data scientists.
Referred as the ‘meat and potatoes of Data Science’, SQL is the most important skill that a Data Scientist must possess.
