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/Applied Biostatistics and Data Analytics for Pharmaceutical Sciences
Semester II · PCI syllabus 2026
Applied Biostatistics and Data Analytics for Pharmaceutical Sciences
BP201T
2 credits
Theory
Coming soon
Unit 1: 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
- 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
Unit 2: 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
Unit 3: 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
Unit 4: 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
Unit 5: 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
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