Transforming Ed Data into Actionable Insights
Turning raw educational data into meaningful insights requires more than just technical expertise—it takes a strategic approach to analytics. Nihil Kottal, a Data Science MS student at Rutgers Graduate School-Camden, has developed EduMetric Solutions, a Power BI tool designed to streamline data analysis in higher education. By integrating datasets from the Bureau of Labor Statistics, the National Center for Education Statistics, and Grad Reports, Nihil applies advanced data cleaning techniques to improve accuracy and reliability. His project enhances the way institutional performance, program effectiveness, and return on investment are evaluated, making data-driven decision-making more accessible.
At the core of EduMetric Solutions is a combination of Natural Language Processing (NLP) and clustering techniques that bring structure to unstructured data. Nihil employs Non-negative Matrix Factorization (NMF) to extract key themes from college reviews and uses sentiment analysis through TextBlob to assess qualitative feedback. Additionally, K-means clustering and cosine similarity measures categorize universities based on thematic content and textual review patterns, offering a deeper understanding of student experiences. These methodologies uncover patterns that might otherwise go unnoticed, adding a new dimension to higher education analytics.
Beyond its technical foundations, EduMetric Solutions provides an interactive way to explore educational data. Its Power BI dashboards allow users to compare institutions, analyze trends, and calculate the return on investment for various degrees in real time. Nihil’s project simplifies complex data interactions, empowering students, educators, and policymakers with insights that align educational choices with workforce demands. By leveraging data science, he is helping to reimagine how higher education is analyzed and understood.
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Mastering Data: The Data Science Program
The Master of Science in Data Science at Rutgers University–Camden is an interdisciplinary program that equips students with skills in data analysis, machine learning, and statistical modeling. The curriculum integrates tools from mathematics, computer science, public policy, and prevention science, emphasizing practical applications across various industries. Core courses include Foundations of Data Science, Statistical Methods for Data Science, Data Visualization, and Applied Data Mining and Machine Learning. Students also engage in a thesis or capstone project, applying their knowledge to real-world problems.
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