BP201TSemester 22 creditsTheoryKEY SUBJECT
Applied Biostatistics and Data Analytics for Pharmaceutical Sciences
Complete unit-wise syllabus for BP201T as per the PCI B.Pharm NEP 2020 curriculum (Semester 2 — Organic Chemistry + Biostatistics).
URL:
https://pharmacode.vercel.app/syllabus/semester-2/bp201t-applied-biostatistics-and-data-analytics-for-pharmaceutical-sciences/Unit-wise Syllabus
5 Units1
Descriptive Statistics6 Hours- Types of data in pharmaceutical sciences (nominal, ordinal, interval, ratio); sources of data: clinical trials, pharmacovigilance, quality control, PK studies
- Measures of central tendency: mean, median, mode; measures of dispersion: range, variance, standard deviation
- Skewness and distribution shape in biological measurements
- Descriptive statistical analysis using Python (NumPy and Pandas) with interpretation of results
2
Probability & Statistical Distributions in Healthcare6 Hours- Basic probability concepts and laws; conditional probability; Bayes' theorem and clinical decision-making
- Concept of random variables (discrete and continuous); Normal distribution in biological and pharmaceutical measurements
- Binomial distribution in clinical trial outcomes; Poisson distribution for rare events (ADRs)
- Graphical visualization of probability distributions using Python
3
Sampling & Statistical Inference6 Hours- Population versus sample; sampling techniques in clinical research; sampling error and bias
- Central Limit Theorem (conceptual); confidence intervals and interpretation
- Hypothesis testing framework: null and alternative hypotheses; Type I and Type II errors; p-value and statistical significance
- Demonstration and interpretation using Python with pharmaceutical data
4
Basics of Correlation & Regression6 Hours- Pearson correlation coefficient; interpretation of positive and negative correlations; scatter plots and trend visualization using pharmaceutical data (dose-response relationships)
- Simple linear regression: concept, interpretation of regression coefficients
- Introduction to odds ratio and its application in clinical risk analysis
5
Statistical Analysis Using Python — Case-Based Learning6 Hours- Demonstration of descriptive statistics, correlation analysis, and linear regression using Python libraries (SciPy, Statsmodels, Scikit-learn) on pharmaceutical datasets
- Interpretation of output summaries and p-values; preparation of statistical reports
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