Graph Theory: Undirected and Directed Graphs, Bipartite Graphs, Connectivity, Traversability, Trees, Spanning Trees, Rooted and Binary Trees, Algorithms – Kruskal’s and Prim’s Minimal Spanning Tree, Dijkstra’s Algorithm, Max-flow Min-cut theorem, Algorithms for computing maximum s-t flows in graphs; Probability Theory: Non-deterministic models, Finite Probability Space and related concepts, Conditional Probability, Independent and mutually exclusive events, Bayes’ Theorem, Random Variables – 1D, 2D, Mathematical Expectation, Variance, Correlation, Distributions – Binomial, Poisson, Normal, Gamma, Chi-Square; Sampling Theory: Purpose and nature of sampling -uses and applications, Mean, Median, Mode, Variance, Standard Deviation; Hypothesis Testing: Formulation of hypotheses – null and alternate hypothesis, Parametric/Non-parametric tests and their applicability, Criteria for acceptance of hypothesis, Level of Significance, t/z/Chi-Square Tests with simple applications.