The evolution of data science development has transformed the way businesses harness big data, machine learning, and artificial intelligence to make informed decisions. From traditional statistical analysis to modern predictive analytics, the data science lifecycle has become more automated, scalable, and real-time. With the rise of cloud computing, data lakes, and edge analytics, organizations are now leveraging advanced data engineering tools and MLOps pipelines to streamline workflows. Today's data science practices emphasize responsible AI, ethical data usage, and explainable models, aligning with the growing demand for transparency and trust in AI systems. As data democratization and low-code/no-code platforms gain popularity, data science is becoming more accessible to citizen data scientists and cross-functional teams.