Abstract
This study uses exploratory data analysis and an exploratory prediction model to examine data scientist salaries. The study examines salary determinants, salary trends, and a forecast model for data scientist salaries. The collection includes wage estimates, job descriptions, company evaluations, and industry data. Descriptive statistics and visualizations reveal variable distributions and trends. Linear regression is used to estimate salaries using geography, industry, firm rating, and job description. However, the original model has a large prediction error, requiring refining. The findings present significant implications for job seekers, companies, and policymakers, necessitating a thorough understanding and response from all these stakeholders. Addressing issues related to data availability and biases becomes imperative, as these could potentially distort the insights and the resulting decisions. Therefore, it's crucial to emphasize and encourage future research in this area to ensure a more equitable and comprehensive approach to employment and policy development.
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