A case study of software development methods for Data analysis
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Oluwatunmise Alabi
College:
The Dorothy and George Hennings College of Science, Mathematics, and Technology
Major:
Computer Science
Faculty Research Advisor(s):
Ching-yu Huang
Abstract:
This research project delves into an exploration of various software development methodologies and their practical application in data science and data analysis projects. Through a thorough examination of methodologies such as waterfall and Agile, this study aims to meticulously evaluate their effectiveness in managing and executing data-driven initiatives. The comparative analysis will encompass critical factors including project flexibility, adaptability to evolving requirements, the ability to pivot research goals for in-depth analysis, and the overall efficiency in delivering actionable insights.
Insights drawn from these case studies will offer invaluable guidance for developers and researchers striving to optimize software development methodologies for data-centric endeavors. Particular emphasis will be placed on ensuring the accessibility of these insights to relevant stakeholders, thereby aiming to broaden the accessibility of results from big data projects to a wider audience.
To assess the efficacy of different software development methodologies, case studies spanning across data science and human-computer interaction projects will be employed. By amalgamating the principles of human-computer interaction with the foundational aspects of data science projects, the research will establish a robust framework for evaluating the suitability of various methodologies for big data projects, while concurrently ensuring the accessibility of their outcomes.
Furthermore, the processes encompassing experiment design, data collection, extraction, transformation, and loading, through to data analytics and knowledge conclusion, will be seamlessly integrated within the software development paradigm, facilitating a comprehensive understanding of their interplay within the context of data-centric projects.