Business Analytics

Introduction to Business Analytics-Evolution, Scope, Models, Data Visualization, Statistical Methods for Summarizing Data, Measures of Central Tendency, Measures of Dispersion, Measures of Shape, Measures of Association, Probability Distributions and Data Modeling- Discrete and Continuous Probability Distributions, Random Sampling from Probability Distributions, Data Modeling and Distribution Fitting.

Sampling and Estimation - Statistical Sampling, Estimating Population Parameters, Sampling Distributions, Confidence Intervals, Prediction Intervals. Statistical Inference- Hypothesis Testing, Confidence Intervals, one sample and two sample tests, z-test, t-test, Chi-square test. Analysis of Variance: Theory and computations of ANOVA, ANOVA table, Two-way ANOVA, Blocking designs, Design of Experiments.

Predictive Analytics- Modeling Relationships and Trends in Data, Simple Regression and Correlation: Introduction, Estimation using the regression line, Correlation Analysis. Multiple Regression: The k-variable multiple regression model, The F-test of a Multiple Regression model.

Forecasting Techniques- Qualitative and Judgmental Forecasting, Forecasting Models for Stationary Time Series, Forecasting Models for Time Series with a Linear Trend, Forecasting Time Series with Seasonality. Introduction to Data Mining, Monte Carlo Simulation and Risk Analysis.

Prescriptive Analytics - Linear Programming Problem-Formulation, Graphical solutions, Simplex method, Revised Simplex method and Sensitivity Analysis, Transportation Problem-Formulation and solution, Assignment Problem- Formulation and solution. Deterministic Dynamic Programming problem- Bellman’s principle of optimality - Computational procedure for Shortest Route problem, Reliability Problem, Equipment Replacement Problem. Integer Programming Problem- Formulation, Branch and Bound algorithm, Cutting Plane Algorithm.