Applied Biostatistics and Data Analytics for Pharmaceutical Sciences (Theory)

Descriptive Statistics Types of data in pharmaceutical sciences (nominal, ordinal, interval, ratio) Sources of data in pharmacy: clinical trials, pharmacovigilance, quality control, PK studies Measures of central tendency: mean, median, mode, their calculation and interpretation Measures of dispersion (range, variance, standard deviation) and their interpretation using pharmaceutical data Skewness and understanding distribution shape in biological measurements Perform descriptive statistical analysis using Python libraries such as NumPy and Pandas, with focus on Interpretation of results

Probability & Statistical Distributions in Healthcare Basic probability concepts and laws (addition and multiplication rules), conditional probability and its interpretation Bayes' theorem and its application in clinical decision-making Concept of random variables (discrete and continuous) Normal distribution and its importance in biological and pharmaceutical measurements, Binomial distribution demonstrating applications in clinical trial outcomes Poisson distribution for modeling rare events such as adverse drug reactions Graphical visualization of these probability distributions using Python

Sampling & Statistical Inference Population versus sample, sampling techniques used in clinical research, Sampling error and bias Conceptual understanding of the Central Limit Theorem Confidence intervals and their interpretation Hypothesis testing framework (null and alternative hypotheses) Type I and Type II errors, p-value and statistical significance Demonstration, calculation and interpretation of these parameters using python with pharmaceutical data

Basics of Correlation & Regression Conceptual understanding of the following concepts with emphasis on their interpretation-correlation and Pearson correlation coefficients Interpretation of positive and negative correlations Scatter plots and trend visualization using pharmaceutical data such as dose-response relationships Simple linear regression concept, interpretation of regression coefficients Introduction to odds ratio and its application in clinical risk analysis

Statistical Analysis Using Python Case based learning Demonstration of descriptive statistics, correlation analysis and linear regression using Python libraries- SciPy / Statsmodels / Scikit-learn on pharmaceutical datasets Interpretation of the output summaries and p-values Preparation of statistical reports