Descriptive Statistics: Measures of central tendency - Problems on measures of dispersion –Karl Pearson correlation, Spearman’s Rank correlation, simple and multiple regression(problems on simple regression only)
Probability Distribution: Concept and definition - Rules of probability – Random variables –Concept of probability distribution – Theoretical probability distributions: Binomial, Poisson,Normal and Exponential – Baye’s theorem (No derivation) (Problems only on Binomial, Poissonand Normal)
Decision Theory: Introduction – Steps of decision-making process – types of decision-makingenvironments – Decision-making under uncertainty – Decision-making under Risk – Decisiontree analysis (only theory).Design of Experiments: Introduction – Simple comparative experiments – Single factorexperiments – Introduction to factorial designs
(only theory)Cluster Analysis: Introduction – Visualization techniques – Principal components –Multidimensional scaling – Hierarchical clustering – Optimization techniquesFactor Analysis: Introduction – Exploratory factor analysis – Confirmatory factor analysisDiscriminant Analysis: Introduction – Linear discriminant analysis
Foundations of Analytics: Introduction – Evolution – Scope – Data for Analytics – Decisionmodels – Descriptive, Predictive, Prescriptive – Introduction to data warehousing – Dashboardsand reporting – Master data management(only theory)
Linear Programming: structure, advantages, disadvantages, formulation of LPP, solution usinggraphical method. Transportation problem: Basic feasible solution using NWCM, LCM andVAM, optimisation using MODI method.Assignment Model: Hungarian method – Multiple solution problems – Maximization case –Unbalanced – Restricted.
Project Management: Introduction – Basic difference between PERT & CPM – Networkcomponents and precedence relationships – Critical path analysis – Project scheduling – Projecttime-cost trade off – Resource allocation