With an average of 256 programming languages, data scientists have a wide variety of languages to use in their analytical tasks. Some programming languages work best for software application development, other work best for development of games while others work best for data science. Statistical programming languages are high-level programming languages that enable data scientist to create code that is independent of the type of computer. Statistical programming languages are used to analyze data sets and help analysts to generate useful information from big data. Some of the most familiar statistical programming languages include R programming language, SQL, BASH, MATLAB, PHP, Ruby, Go, SAS, Java and Python (Tippmann, 2015). First, statistical programming languages and tools provide data analysts with an effective platform representing, modelling and processing massive amounts of data. The exponential growth in the internet, information systems, communication networks and social media platforms have enabled data scientists to collect massive amounts of structured, semi- structured and unstructured data. Statistical programming languages provide tools and means to derive knowledge, insights and conclusions from the data that may not be in the data set directly.

Secondly, statistical programming languages such as R and Python provide algorithms that help computer scientists to process raw data. These languages offer several analytical methods that reveal interesting patterns and trends from big data. Data scientists use numerous algorithms for machine learning, Artificial Intelligence and predictive analysis. Some of the algorithms include K Nearest Neighbors, K means clustering, Linear Regression and Logistic Regression. Statistical programming languages are also significantly crucial for statistical modelling models. Experts define statistical models as mathematical models that embodies statistical assumptions concerning the generation of sample data, or similar data gathered from larger populations. Statistical modelling helps data scientists to encode information extracted from the data more effectively and quickly.

The accuracy of the conclusions drawn from big data analysis is of paramount importance in the digital world. Statistical programming languages provide a standard form of processing data, thus allowing data scientists from across the globe to collaborate in data processing tasks. The standardization of data analysis also enables the comparison of the results obtained and to determine the accuracy of the knowledge generated (Tippmann, 2015). Despite the complexity of some programming languages such as R, they are relatively easy to use once the analysts understand the syntax of the language. It is relatively easy to generate mine insights from data when using statistical programming languages. Lastly, statistical programming languages allow swift and quick mining of big data. Several programming languages enhance the development of communities that consist of leading statisticians, data analysts and data scientists from different parts of the globe.

Some Advantages And Disadvantages The R Programming Language Has Over The Other Main Statistical Programming Languages

R is one of the most popular statistical programming language for statistical modelling and analysis. Like any other programming language, R has its advantages and disadvantages.

Advantages Of R

1. Open-source – R is an open-source programming language. Anyone can work with R and its tools without the need of paying a fee or acquiring a license. Software developers and data scientists from around the world are allowed to participate in the development of R by customizing the existing packages, resolving issues and developing new packages.

2. Exemplary support for data wrangling – R offers packages such as readr and dplyr that are used to transform unstructured and semi-structured data into a structured form.
3. The Array of Packages – R has a vast array of packages in the CRAN repository that appeal to all the areas of industry (Gardener, 2012).
4. Highly Compatible – R is highly compatible with other high-level programming languages such as C, C++ and Python. R can also be effectively integrated with existing information systems such as databases and Hadoop.
5. Platform independent – R is a cross-platform programming language. This means that R can work effectively with Mac, Linux and Windows operating systems.
6. Continuously growing – R is a constantly growing language that provides updates whenever a new feature is added. This is the main language why R is the most dominant among all the other developing statistical programming languages.

Disadvantages Of R Programming Languages

1. Basic security – R programming language lacks underlying security, an essential feature that is offered by other programming languages such as Python. As a result, R cannot be embedded into web-applications.
2. Complicated language – R is one of the most challenging languages to learn, especially for users who do not have experience in programming (Matloff, 2011).
3. Lesser speed- Due to the complexity of R programming language, programmers with limited experience in R may find it challenging to implement algorithms across the different packages.
4. Data Handling – the physical memory of R stores more objects and utilizes more memory as compared to other statistical programming languages such as Python.