Build practical numerical computing and retail data analysis skills with NumPy and Pandas. Designed for aspiring data analysts, business intelligence professionals, and Python enthusiasts, this hands-on course guides you from NumPy foundations to advanced Pandas techniques through case studies and retail datasets.

You’ll learn to create and manipulate NumPy arrays using slicing, reshaping, stacking, and broadcasting; apply linear algebra operations and implement gradient descent for analytical problems. You’ll then use Pandas to import data from multiple sources, clean and transform retail datasets, convert data types, filter and sort records, and merge or concatenate data for comprehensive analysis. As you progress, you’ll construct groupby aggregations and pivot tables to assess retail performance, manipulate string fields, parse datetime data for time-based insights, encode categorical data, reshape datasets, and export finalized results for business reporting and decision-making. The course’s distinctive two-in-one structure connects efficient numerical analysis in NumPy with business-ready data management in Pandas. This practical progression helps you develop both technical depth and the ability to prepare and analyze retail data in professional settings.