Data scientists must harness any of the programming languages like Python and R
Data science is flourishing in recent times with the integration of cutting-edge technologies and programming languages. Popular and trending programming languages such as Python and R are transforming the methods of data scientists to drive meaningful insights. Data science programming languages are in huge demand for additional features and seamless functionalities. Data scientists have started preferring Python or R as their suitable programming language for data management. Thus, let’s look for the reason why using one of these best data science programming languages is most common among data scientists. It may vary from one data scientist to another depending on multiple factors.
Python as a Data science programming language
Python is one of the best data science programming languages for data scientists across the world. It is trending for its in-built mathematical libraries and functions for calculating complex mathematical problems and data analysis. Libraries for data scientists include Pandas, Numpy, SciPy, as well as Matplotlib are useful for scientific computing.
This programming language is an open-source and high-level language to offer object-oriented programming to data scientists. It helps with mathematics, statistics, as well as scientific functions with a simple syntax for easy adaptability. Data scientists who lack technical background can also use Python to create a quick prototype efficiently and effectively. The interactive mode of the data science programming language helps to successfully test codes with new modules.
R as a Data science programming language
R is gaining popularity among data scientists as one of the top data science programming languages. It is open-source with the functionality of statistical software and data analysis tool. It offers data scientists an intensive environment for extensive research and visualization of sufficient information from large datasets. R also includes statistical computing and graphics for data science projects.
The trending programming language helps in multiple data science applications for its data visualization tools and ETL (Extract, Transform, Load). Data scientists use R for its multiple packages related to data wrangling and opportunities for applying machine learning algorithms to drive meaningful and in-depth insights for yielding revenue in the highly competitive market. R is well-known for its interface with NoSQL databases and unstructured data analysis. There are multiple R libraries for data science projects such as Dplyr, Ggplot2, Tidyr, Caret, and many more. It is suitable for data scientists who want to handle, store, and analyze real-time data and use statistical modelling with R.
Python v/s R for Data science projects
Python and R are both trending and gaining popularity in data science among different groups of data scientists across the world. They need to have a deeper understanding of which programming language they want to use for a seamless workforce. R offers steep learning while Python offers a simple syntax. Beginners can start with Python while professionals can use R. Thus, it depends on the workload and the company objectives along with the programming language skills. R offers better data visualization while Python offers better data scraping.
Hence, two of these data science programming languages are suitable for creating data science projects of data scientists across the world. It totally depends on you which one to choose for the current project.