The purpose of this course is to provide students with skills to analyze and interpret small and large biological data sets. Fundamentals in probability and statistics will be taught through the use of homework problems, case studies and projects focused on computational analysis of biological data. Topics covered include: Populations and samples; random variable; discrete and continuous probability distributions; exploratory data analysis; descriptive statistics; confidence intervals; expectations; variances; central limit theorem; independence; hypothesis testing; fitting probability models; pvalues; goodness-of-fit tests; correlation coefficients; non-parametric tests; ANOVA; linear regression; bootstrapping; and maximum likelihood estimation.
This Course covers lesson :
- Applied Linear Algebra
- Nonlinear Optimization
- Numerical Methods
- Vector and Complex Analysis
- Differential Equations and Applications
- Approximation Techniques
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e-book enables you to access interactive reading material to strengthen the skills learned in the sessions.
Online Lab enables you to practice the application of concepts you have learnt in the sessions in the virtual environment.
Tutorials enables you to get easy learning with clear, crisp, and to-the-point content on a wide range of technical and non-technical subjects without any preconditions and impediments.