Question

Expand on your discussion post with a 5 page APA formated research paper on the following topic:

Why are statistical programming languages important to data scientists? What are some advantages and disadvantages the R programming language has over the other main statistical programming languages (i.e. Python, SAS, SQL)?

Answer 1

Abstract

This abstract give the detail summary of the how and why the statistical programming is important to data scientists ,who are data scientists, and gives the detail description of what is the statistical programming.How this R programming still rules the data scientists , what survey conducted by the companies .In which language is used to represent this programming .How R programming is related and how it is the heart of powerful. How companies using this programming languages and how important this languages to companies. What are the applications of R programming .Some advantages and disadvantages the R programming has over the other statistical programming languages like python ,SQL.

DATA SCIENTISTS

Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data. Data science is related to data mining and big data. Data scientists are a new breed of analytical data expert who have the technical skills to solve complex problems – and the curiosity to explore what problems need to be solved. They're part mathematician, part computer scientist and part trend-spotter. Data scientists are big data wranglers, gathering and analyzing large sets of structured and unstructured data. A data scientist's role combines computer science, statistics, and mathematics. Statistical Programming refers to computation techniques that help in data analysis. R and SAS etc., are statistical programming packages that offers a wide variety of statistical and graphical techniques to explore large data sets and make graphical displays of it for better and quick understanding. Statistical programming refers to computation techniques that help in data analysis. R, Matlab and SAS etc.,Are statistical programming packages that offers a wide variety of statistical and graphical techniques to explore Large data sets and make graphical displays of it for better and quick understanding. These package support Statistical techniques like linear and nonlinear modeling, classification, clustering, time-series analysis, and others.

Statistical programming important to data scientists:

According to the Spectrum Survey by IEEE, R programming language - the king of statistical computing languages for analysing and visualizing big data takes 6 ^th place in “The 2015 Top Ten Programming Languages”. In 2014, R programming was at the 9^th position and the drastic move this year reflects the significance of R language emerging as a powerful statistical tool in data science.

R Programming Language- A powerful statistical programming tool written by Statisticians for Statisticians

Organizations in every industry have started realizing the fact that - the secret to success is being able collect, store and analyses data at a faster pace than the competitors. The consequence of this big data revolution is that the hiring demand for data scientists with Hadoop, NoSQL, Python programming, R programming and other big data skills is heating up. to get the 2016 data scientist salary report delivered to your inbox!

With big data analytics and machine learning driving intelligence in almost every Internet connected device, software application and smartphones, R is a powerful statistical tool that data scientists use to find answers from the large treasure troves of data. R programming helps data scientists with statistical analysis of data more quickly and powerfully when compared to any other statistical computing tools.

R language is used by more than 2 million statisticians and  data scientists across the world, and with the wider adoption of R language for business applications, the usage of this statistical software is increasing exponentially. R programming language was developed for statistical analysis at a small-scale in academic settings. R language is a powerful statistical computing tool for visualizing data, exploring large data sets and creating novel statistical models.

"R is more about sketching, and not building. You won’t find R at the core of Google’s page rank or Facebook’s friend suggestion algorithms. Engineers will prototype in R, then hand off the model to be written in Java or Python."- says Michael Driscoll, CEO of Metamarkets.

R is on the rise as a powerful business analytics tool with contributions from popular statisticians to the open source community over 20 years. R language is among the most powerful and popular data science tools because it presents different faces to different users. R programming language has been kicking around since 1997 as an alternative to expensive statistical programming tools like SAS or Matlab.

R is the first programing language that takes input through a command line which might seem unfriendly to non-coders in the beginning, however beginners can directly make calls to pre-defined software packages that have ready-made commands for data visualization and statistical analysis. Pre-set R packages can be adapted by beginners to learn R programming in a fun and interactive way. R software packages act as a middle ground between the world of coding experts and the ease of commercial black-box solutions.

Advantages and disadvantages of R programming over the other languages :

ADVANTAGES:

Various benefits of R language are mentioned below, which will help you to grasp the concept:

1. Open Source

R is an open-source programming language. This means that anyone can work with R without any need for a license or a fee. Furthermore, you can contribute towards the development of R by customizing its packages, developing new ones and resolving issues.

2. Exemplary Support for Data Wrangling

R provides exemplary support for data wrangling. The packages like dplyr, readr are capable of transforming messy data into a structured form.

3. The Array of Packages

R has a vast array of packages. With over 10,000 packages in the CRAN repository, the number is constantly growing. These packages appeal to all the areas of industry.

DISADVANTAGE:

1. Basic Security

R lacks basic security. This feature is an essential part of most programming languages like Python. Because of this, there are several restrictions with R as it cannot be embedded into a web-application.

2. Complicated Language

R is not an easy language to learn. It has a steep learning curve. Due to this, people who do not have prior programming experience may find it difficult to learn R.

3. Lesser Speed

R packages and the R programming language is much slower than other languages like MATLAB and Python.