Data science is inherently difficult because it requires bridging advanced math, software engineering, and specific business domains. The greatest obstacles involve dirty or scarce data, misalignment between technical models and business goals, and the constant need to adapt to rapidly evolving technologies and algorithms
clustering and segmentation are techniques used in data analysis to group data points based on similarities, but they are applied in different contexts and have distinct goals.