Research Article

Building a Cloud and Deep Learning Portfolio for Aspiring Data Scientists

Authors

  • Vimal Pradeep Venugopal Independent researcher, USA

Abstract

This article presents a strategic framework for creating an effective cloud and deep learning portfolio for aspiring data scientists. The portfolio-centered approach addresses the industry shift from credential evaluation to demonstrated capability assessment, with particular emphasis on showcasing cloud infrastructure integration with machine learning applications. A structured methodology for portfolio development is outlined, encompassing strategic project selection using a T-shaped skill demonstration model, professional documentation standards, essential cloud service proficiencies, real-world business application focus, and financial optimization awareness. The framework further details multi-channel presentation strategies, a phased implementation timeline, strategic project selection recommendations, and the career acceleration benefits of building in public. This guidance provides aspiring data scientists with a systematic pathway to develop compelling portfolios that effectively demonstrate production-ready AI solution capabilities, thereby reducing time-to-employment and enhancing career progression opportunities.

Article information

Journal

Journal of Computer Science and Technology Studies

Volume (Issue)

7 (6)

Pages

942-951

Published

2025-06-25

How to Cite

Vimal Pradeep Venugopal. (2025). Building a Cloud and Deep Learning Portfolio for Aspiring Data Scientists. Journal of Computer Science and Technology Studies, 7(6), 942-951. https://doi.org/10.32996/jcsts.2025.7.111

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Keywords:

Cloud infrastructure integration, T-shaped portfolio development, production-ready AI solutions, multi-channel presentation, building in public