Applied Biostatistics and Data Analytics for Pharmaceutical Sciences

Shree Sai Prakashan is writing a full set of B.Pharm titles to this new NEP 2020 syllabus. Get notified the moment the book for this subject is released, or pre-book sets for your college.

Unit 1 · Descriptive Statistics 6 hrs

Types of data in pharmaceutical sciences (nominal, ordinal, interval, ratio) and sources of data (clinical trials, pharmacovigilance, quality control, PK studies); measures of central tendency and dispersion and their interpretation; skewness and distribution shape in biological measurements; and descriptive analysis using Python (NumPy, Pandas).

Unit 2 · Probability & Statistical Distributions in Healthcare 6 hrs

Basic probability concepts and laws, conditional probability and Bayes' theorem in clinical decision-making; random variables; the normal distribution in biological measurement; the binomial distribution in clinical-trial outcomes; the Poisson distribution for rare events such as adverse drug reactions; and visualising distributions in Python.

Unit 3 · Sampling & Statistical Inference 6 hrs

Population versus sample and sampling techniques, sampling error and bias; the Central Limit Theorem; confidence intervals; the hypothesis-testing framework (null and alternative hypotheses); Type I and Type II errors, p-values and statistical significance — calculated and interpreted in Python.

Unit 4 · Correlation & Regression 6 hrs

Correlation and the Pearson coefficient, interpreting positive and negative correlations; scatter plots and trend visualisation with dose–response data; simple linear regression and interpretation of regression coefficients; and the odds ratio in clinical risk analysis.

Unit 5 · Statistical Analysis Using Python — Case-based Learning 6 hrs

Demonstration of descriptive statistics, correlation and linear regression on pharmaceutical datasets using Python libraries (SciPy, Statsmodels, Scikit-learn); interpretation of output summaries and p-values; and preparation of statistical reports.