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The Statistics track in Big Data Analytics prepares researchers with robust statistical expertise to examine vast datasets, whether structured or unstructured, revealing hidden trends, undiscovered relationships, and actionable insights for improved decision-making.
This specialization offers comprehensive training in statistical principles and key Big Data Analytics techniques, including predictive modeling, data mining, text analysis, and statistical evaluation, blending statistical and computational approaches. Emphasis is placed on theoretical statistics alongside computational statistics, data mining applications, and their real-world implementation in business, social sciences, and healthcare, supported by active industry partnerships.
The Ph.D. program in Big Data Analytics (Statistics track) mandates 72 credit hours post-bachelor's degree, comprising 42 credits of core courses, 15 credits of focused electives, and 15 credits dedicated to dissertation research.