Introduction to Statistics and Data Analysis; Probability Theory: Non-deterministic models, Finite Probability Space and related concepts, Conditional Probability, Independent and mutually exclusive events, Bayes’ Theorem, Random Variables – One and Two dimensional, Expectation, Variance, Correlation, Statistical Distributions – Uniform, Normal, Binomial, Gamma, Exponential, Poisson, Chi-Square, Log-Normal, Stochastic Processes: Markov Chains, Binomial & Poisson; Queuing Systems: M/M/1 and M/M/K; Sampling Theory: Random Sampling and Applications, Mean, Median, Mode, Variance, Standard Deviation; Hypothesis Testing: Formulation of hypothesis - null and alternate hypothesis, Parametric and non-parametric tests and their applicability, Criteria for acceptance of hypothesis, Level of Significance, t-test, z-test and Chi-Square Tests with applications.