Biostatistics and data science are distinct yet increasingly interconnected fields shaping modern healthcare and research. Biostatistics, with its focus on study design, causal inference, and statistical rigor, has long been essential to clinical trials, epidemiology, and public health. Data science, powered by machine learning, big data analytics, and computational methods, excels in prediction, automation, and large-scale data processing. This guide explores their historical evolution, core methodologies, and applications, highlighting how their integration is driving advances in precision medicine, epidemiological forecasting, translational research, and biomedical innovation.