Statistics has quietly become one of the most decisive tools in the pharmaceutical sciences. From the moment a molecule is screened in a laboratory to the day a medicine is approved for the public, every step rests on data — measured, summarised, tested, and interpreted. A pharmacist who cannot read a p-value, question a confidence interval, or recognise a poorly designed clinical trial is at a real disadvantage in modern practice, whether in industry, regulatory affairs, hospital pharmacy, or research. This book has been written specifically for undergraduate pharmacy students encountering biostatistics and data analytics for the first time, following the prescribed course structure of descriptive statistics, probability and distributions, sampling and inference, correlation and regression, and applied statistical analysis using Python. Each unit blends conceptual explanation with pharmaceutical examples — dissolution data, adverse drug reactions, clinical trial outcomes, dose-response relationships — so that the mathematics is never divorced from its purpose. Wherever a formula appears, it is followed by a worked example drawn from a pharmaceutical context. Wherever a Python library is introduced, the code is explained line by line so that a student with no prior programming background can follow along. Each chapter closes with a summary and review questions intended for self-assessment.