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Research Paper | Computer Science and Information Technology | United States of America | Volume 14 Issue 5, May 2025 | Popularity: 4.9 / 10
Data Science, Analytics, and the AI Revolution: Creating More Jobs and Empowering Careers
Gunasai Muppala
Abstract: In recent years, as AI has desperately cast its shadows on other significant sectors like business, education, and healthcare, people have started to fuss about the possibility of AI taking away human jobs?especially in data science and analytics. In this paper, the author contends that replacing jobs in these domains is not happening with AI but is transforming and expanding them. Data curation, contextual interpretation, and enforcing ethical frameworks heavily rely on human expertise for AI systems. Far from rendering data professionals obsolete, AI has increased their productivity by doing away with repetitive tasks and enabling powerful augmented analytics tools such as AutoML, AI-powered dashboards, and brilliant data discovery platforms. As an emerging technology, AI technology has created new roles crucial to the deployment and governance of the technology itself. Having said so, there are currently emerging positions (like machine learning operations (MLOps) engineers, AI-focused data analysts, and algorithm auditors) that are critical in ensuring AI systems work reliably, transparently, and culturally aligned. The paper investigates how AI transforms the existing data workflows to allow for innovation and reshape the professional landscape. This analysis not only encompasses the technical dimension but also brings into view policies and ethical oversight to improve the adoption of AI to stimulate inclusive economic growth. AI is to be treated as a collaborative partner that expands on human abilities and brings to the pinnacle the evolution of the workforce. AI by no means points to the dying of information science roles; it opens the door to a new period defined by clever teamwork and people-centered innovation.
Keywords: Artificial Intelligence, Analytics, Data Science, and Predictive Analytics
Edition: Volume 14 Issue 5, May 2025
Pages: 120 - 122
DOI: https://www.doi.org/10.21275/SR25430222648
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