Big Data Analysis on Job Trends Using R

Title:Big Data Analysis on Job Trends Using R

Volume: 14 Issue: 1

Author(s): Somula Ramasubbareddy, Anish S. Walia, Ashish K. Luhach*, Govinda Kannayaram and Swetha Evakattu

Affiliation:

• Department of Electrical and Communication Engineering, The PNG University of Technology, Lae,Papua New Guinea

Keywords: Data analysis, data summarization, data aggregation, job trends, data science, R.

Abstract:

Background: Nowadays, the demand for data science-related job positions have seen a huge increase due to the recent data explosion incurred by the industries and organizations globally. The necessity to harness and utilize the amount of information hidden inside these huge datasets for effective decision-making has become the need of the hour. However, this scenario is where a data analyst or a data scientist comes into play. They are domain experts who have the skillset and expertise to extract hidden meaning from data and convert them into useful insights. This work illustrates the use of data mining and advanced data analysis techniques such as data aggregation, summarization along with data visualization using R tool to understand and analyse the job trends in the United States of America (USA) and then drill down to analyse job trends for data science-related job positions from year 2011 to 2016.

Objective: This paper discusses the general job trends in the US and how the job seekers are migrating from one place to another place using Visa for different titles, majorly for business analytics.

Methods: Analytics is done using R programming, different functions of the programming on various parameters and inference is drawn on the result.

Results & Conclusion: The aim of this analysis is to predict the job trends in line with demand, region, employers, wages in USD.

Cite this article as:

Ramasubbareddy Somula , Walia S. Anish , Luhach K. Ashish *, Kannayaram Govinda and Evakattu Swetha, Big Data Analysis on Job Trends Using R, Recent Advances in Computer Science and Communications 2021; 14 (1) . https://dx.doi.org/10.2174/2213275912666190801113359