Biostatistics and data science play complementary roles in healthcare analytics, each with distinct tools, methods, and applications. Biostatistics emphasizes study design, causal inference, and regulatory compliance through software like SAS, Stata, and R, while data science leverages Python, machine learning, and cloud platforms for predictive modeling and real-time insights. This analysis compares their technologies, methodologies, and case studies in clinical trials, epidemic tracking, and hospital operations, highlighting how integration of both disciplines drives innovation in precision medicine, public health, and biomedical research.