Data Analysis is one cogent area of data science that requires mostly quantitative study and language programming. As a result, many data scientist the world over have chosen to adopt the R language for their coding tasks and projects. R is a major programming tool that has been recognized for analysis of data as it makes use of a large repertoire of software developed by other computer programmers. Hence, scientists now rely on the language for their series of mathematical calculations and visualization of graphical elements as they portray their projects online or through the physical storage devices (Venables, Smith and R Core Team, 2022)…
Azeez Olanrewaju Shoderu
Mental Wealth: Professional Life (Data Ecology) Module Task 3 Discussion University of East London, UK through UNICAF Scholarship
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Introduction
Data Analysis is one cogent area of data science that requires mostly quantitative study and language programming. As a result, many data scientist the world over have chosen to adopt the R language for their coding tasks and projects. R is a major programming tool that has been recognized for analysis of data as it makes use of a large repertoire of software developed by other computer programmers. Hence, scientists now rely on the language for their series of mathematical calculations and visualization of graphical elements as they portray their projects online or through the physical storage devices (Venables, Smith and R Core Team, 2022).
Acceptance of R
Statisticians also employ R to carry out their advanced plots in Stata and graphical representation of their statistical analysis borrowing from the arrays of packages that R has in its inventory (Evans, 2014). It is no surprise any longer why the field of data science and even professional bodies like the Association of Computational Linguistics (ACL), Data Science Association and Royal Statistical Society (RSS) all approve of this language for data analysis.Data Analysis with RStudio
To analyze data into R, it is imperative to download the R software from the internet and install on any of our gadgets or laptops. After successfully opening the programming language, some coders keep on making use of the R console. However, R can be used from a much better IDE (Integrated Development Environment) like RStudio which embeds in itself improved user interface editor that eases writing codes and developing projects (Peng, 2015).
Data Visualization in R
According to Evans (2014), “Graphics and `data visualization’ are an integral part of statistics, and R makes it easy to produce common plots quickly, as well as giving a powerful interface for more esoteric output. The basic command is the generic function plot().” This is not to say that the R language only makes use of the plots for data visualization; there are invariably other graphic systems used in R such as histograms, curves, piecharts, among others.
Decision Making in Data Science
With all the data analysis and analytics carried out in the R language for data science, the major goal is to provide improved understanding and increased decision making process. In fact, many organizations now make use of these data gotten from the field, analyzed and visualized by processing languages like R to better comprehend the state of affairs in the company, to advance further to predicting future occurrences for utmost profitability and even satisfied customer (Brown, Swift and Smart, 2018).
Data Influencing Business Decisions
In many businesses now, data science has proven very useful in assisting top managers and directors to make accurate decisions. Machine learning for instance now has techniques that employ automated decision making from algorithms gotten virtually through clients while shopping or interacting with carts, social media platforms, websites, recommendation systems and so on. In the recruitment process of Amazon as a case study, applicants who made use of phrases like ‘women’s chess club captain’ were penalized and other applicants who made know that they studied at women’s colleges were downgraded for the post (Dastin, 2018).
Reference List
Brown, D., Swift, A. and Smart, E. (2018). Data Analytics and Decision Making. UK: Institute of Industrial Research University of Portsmouth.
Dastin, J. (2018). Amazon scraps secret AI recruiting tool that showed bias against women. Retrieved from https://www.reuters.com/article/us-amazon-com-jobsautomation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-againstwomen-idUSKCN1MK08G
Evans, R. (2014). R Programming. In Michaelmas. http://www.stats.ox.ac.uk/~evans/teaching.htm
Peng, R.D. (2015). R Programming for Data Science. Victoria, Canada: Lean Publishing.
Venables, W.N., Smith, D.M. and R Core Team. (2022). An Introduction to R Notes on R: A Programming Environment for Data Analysis and Graphics. Version 4.1.3. Australia: University of Adelaide.