Introduction to the course. Data representation and plotting. Arithmetic mean. Geometric mean. Measure of Variability, Standard deviation. SME, Z-Score, Box plot. Moments, Skewness. Kurtosis, R programming. R programming. Correlation. Correlation and Regression. Correlation and Regression Part-II. Interpolation and extrapolation. Nonlinear data fitting. Concept of Probability: Introduction and basics. Counting principle, Permutations, and Combinations. Conditional probability. Conditional probability and Random variables. Expectation, Variance and Covariance Part - II. Binomial random variables and Moment generating function. Random variables, Probability mass function, and Probability density function. Expectation, Variance and Covariance. Probability distribution : Poisson distribution and Uniform distribution Part-I. Uniform distribution Part-II and Normal distribution Part-I. Normal distribution Part-II and Exponential distribution. Sampling distributions and Central limit theorem Part-I. Sampling distributions and Central limit theorem Part-II. Central limit theorem Part-III and Sampling distributions of sample mean. Central limit theorem - IV and Confidence intervals. Confidence intervals Part- II. Test of Hypothesis - 1. Test of Hypothesis - 2 (1 tailed and 2 tailed Test of Hypothesis, p-value). Test of Hypothesis - 3 (1 tailed and 2 tailed Test of Hypothesis, p-value). Test of Hypothesis - 4 (Type -1 and Type -2 error). T-test. 1 tailed and 2 tailed T-distribution, Chi-square test. ANOVA - 1. ANOVA - 2. ANOVA - 3. ANOVA for linear regression, Block Design.