Data science is the field that combines statistics, programming, and domain knowledge to extract insights and value from data. It involves collecting, cleaning, analyzing, and visualizing data to support decision-making. The data life cycle includes stages like data collection, preparation, analysis, modeling, and interpretation. Understanding this cycle helps ensure that data-driven solutions are accurate, efficient, and impactful.

Data Analyst focuses on interpreting data, creating reports, and visualizing trends to support business decisions.
Data Scientist builds models, uncovers patterns, and uses statistical and ML techniques to solve complex problems.
ML Engineer designs, builds, and deploys machine learning models into production environments at scale.
Each role plays a vital part in transforming raw data into actionable intelligence and automation

Data science and machine learning power various business use cases across industries. In retail, they enable personalized recommendations and inventory forecasting. In finance, they detect fraud, assess risks, and automate trading. Healthcare uses them for disease prediction and medical image analysis. Marketing benefits through customer segmentation and campaign optimization. These applications help businesses enhance efficiency, reduce costs, and make data-driven decisions.