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AKTU Applied Biostatistics and Data Analytics for Pharmaceutical Sciences (Theory) (BP201T) Syllabus PDF – B.Pharm 2nd Semester
Applied Biostatistics and Data Analytics for Pharmaceutical Sciences (Theory) BP201T
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Official AKTU syllabus of Applied Biostatistics and Data Analytics for Pharmaceutical Sciences (Theory) (BP201T) for B.Pharm 2nd Semester – 2 credits, L-T-P 2-0-0. Read the unit-wise course content, course outcomes, evaluation pattern and recommended books online, or download the PDF.
Syllabus — Applied Biostatistics and Data Analytics for Pharmaceutical Sciences (Theory)
Download PDFBP201T · 2 credits · L-T-P 2-0-0
Course Objectives
- Explain fundamental statistical concepts relevant to pharmaceutical sciences.
- Develop the ability to interpret clinical and experimental data scientifically.
- Apply statistical methods for analyzing pharmaceutical and biomedical data.
- Demonstrate the use of Python-based tools for statistical analysis and data handling.
- Recognize the role of statistical analysis in evidence-based decision making and machine learning applications in healthcare.
Course Outcomes
| CO | Upon successful completion of this course, the students will be able to: |
| No. | |
| 1 | Classify pharmaceutical data using appropriate descriptive statistics. |
| 2 | Apply probability concepts and statistical distributions in clinical and pharmaceutical contexts. |
| 3 | Perform sampling and hypothesis testing for decision-making in research. |
| 4 | Analyze correlation and regression relationships in pharmaceutical datasets. |
| 5 | Demonstrate statistical analysis using Python and interpret outputs appropriately. |
Course Content
| Unit | Topics | No. of |
|---|---|---|
| I | 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 | 6 hours |
| II | 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 | 6 hours |
| III | 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 | 6 hours |
| IV | 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 | 6 hours |
| V | 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 | 6 hours |
Recommended References
- Lane, D.M., Introduction to Statistics. Houston: Rice University. Available at: https://onlinestatbook.com/2/index.html
- Walters, S.J., Campbell, M.J. and Machin, D., Medical Statistics: A Textbook for the Health Sciences. Hoboken, NJ: Wiley-Blackwell.
- Rowe, P., Essential Statistics for the Pharmaceutical Sciences. London: Pharmaceutical Press.
- Bolton, S. and Bon, C., Pharmaceutical Statistics: Practical and Clinical Applications. New York: Informa Healthcare.
- De Muth, J.E., Basic Statistics and Pharmaceutical Statistical Applications. Boca Raton, FL: CRC Press.
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