Biostatistics
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Follow on Instagram for UpdatesBiostatistics Syllabus — Unit-wise Content
The official GTU curriculum for Biostatistics (subject code BE03004051) as prescribed for Biotech · Semester 3 divides the course into 4 units, covering 60 listed topics. The unit-wise breakdown below is read from the GTU syllabus document for this subject code, so you can check what the course actually covers before deciding which papers to attempt.
Download the official syllabus PDF (BE03004051)Unit 1 Introduction to Biostatistics and Organization of Data
- Fundamentals of biostatistics
- Types of biological data (qualitative and quantitative)
- Graphical and pictorial data representation: bar charts
- pie charts
- scatter plots
- Measures of central tendency (mean
- mode) and dispersion (range
- standard deviation)
- Sampling methods and sample size determination
- Calculation of coefficient of variation
- mean error
- relative error
- concepts of precision
- accuracy, and bias in measurements
Unit 2 Probability Distribution and Statistical Inference
- Introduction to probability theory and Bayes' theorem
- Probability distributions binomial
- Poisson, and normal distributions
- their properties, and applications in biological data analysis
- Introduction to parametric and non-parametric hypothesis testing
- Testing of hypotheses: types I and II errors
- p-values, and statistical power
- Tests of significance: z-test
- t-test (single
- Nonparametric procedures: chi-square test
- sign test
- Wilcoxon signed-rank test
- Wilcoxon rank-sum (Mann-Whitney U) test
- Analysis of Variance (ANOVA): one-way and two-way ANOVA
- Correlation and regression analysis: simple and multiple regression models
- interpretation of regression coefficients
- Tests of significance for correlation coefficients
- Introduction to multivariate statistical methods (PCA
- cluster analysis)
Unit 3 Experimental Design
- Principles of experimental design: randomization
- replication, and blocking
- Advanced techniques: Double-blind and double-dummy procedures for clinical and biological experiments
- Design structures: Completely randomized design (CRD)
- randomized block design (RBD)
- Latin square design
- factorial experiments (full and fractional designs)
- crossover designs, and parallel group designs
- Optimization techniques and response surface methodology (RSM) for engineering and biological process design
Unit 4 Statistics in Data Analytics
- Application of statistical methods in biological and biomedical data analysis
- Introduction to Big Data analytics in biological sciences and healthcare
- Data analytics lifecycle: Discovery
- data preparation
- model planning
- model building
- evaluation and validation
- result communication, and operationalization
- Introduction to supervised and unsupervised learning for biological data: decision trees
- logistic regression
- Fundamentals of machine learning model validation: cross-validation
- train/test split
- confusion matrix
- ROC curves
- Overview of bioinformatics data analytics: handling omics data (genomics
- metabolomics) using statistical methods
- Ethical considerations in biological data analytics and reporting standards (e.g.
- STROBE guidelines)
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