This course equips learners with essential quantitative methods and analytical skills for effective decision-making and research across social sciences supported by relevant software tools to solve real-world problems and inform policy and strategy.

CLO 1 :Explain fundamental quantitative concepts, including probability distributions, statis tical inference, and optimization techniques relevant to economics, business, and policy con texts. 

CLO 2: Apply quantitative methods and statistical software tools to perform data anal ysis, run models, and solve optimization problems in practical scenarios.

CLO 3 : Analyse com plex datasets using econometric models, time series forecasting, and multivariate analysis to identify trends, relationships, and causality. 

CLO 4 : Critically evaluate decision-making prob lems under uncertainty and design effective quantitative models to propose optimal solutions for real-world business and policy challenges.

Introduction to Quantitative Methods: Role and importance of quantitative techniques in re search and decision-making; Review of basic mathematical concepts (functions, matrices, deriva tives, probability);Application of quantitative reasoning in economics, business, and policy Probability and Statistical Foundations: Probability rules and distributions (normal, binomial, Poisson, exponential); Sampling theory and estimation; Hypothesis testing and confidence in tervals; Applications in social science and economic data; Regression and Econometric Tech niques: Simple and multiple linear regression; Classical linear regression assumptions and vi olations; Correlation and causality; Introduction to advanced econometrics (logit, probit, time series basics); Use of statistical/econometric software (Stata, R, or SPSS); Optimization and Linear Programming: Formulation of linear programming (LP) problems; Graphical and sim plex methods; Duality and sensitivity analysis; Applications in resource allocation, produc tion, and finance; Introduction to non-linear and integer programming; Decision Analysis un der Uncertainty: Decision-making under certainty, risk, and uncertainty; Decision trees and payoff tables; Expected monetary value (EMV) and utility theory; Game theory and strate gic decision-making; Transportation Problem (TP): Balanced vs. unbalanced transportation problems; Initial feasible solution methods (North-West Corner Method (NWCM), Least Cost Method (LCM), Vogel’s Approximation Method (VAM); Optimality tests: Stepping Stone Method, MODI (u–v) Method; Applications in supply chain and distribution systems Assignment Prob lem (AP): Formulation of the assignment problem; The Hungarian Method for solution; Varia tions (Unbalanced assignment problem, Maximization assignment problem, Multiple/Restricted assignments); Applications in scheduling, workforce allocation, and task assignment: Time Series and Forecasting: Components of time series (trend, seasonal, cyclical, random); Mov ing averages and exponential smoothing; ARIMA models (introductory level); Applications 

in forecasting demand, sales, and economic variables; Multivariate Analysis (Advanced Top ics): Factor analysis and principal component analysis (PCA); Cluster analysis and discrimi nant analysis; Structural equation modelling (introductory concepts); Applications in market research and social science; Simulation and Quantitative Research Applications: Monte Carlo simulation techniques; Queuing models and Markov chains; Application of quantitative meth ods in policy evaluation and business strategy; Case studies and real-world problem-solving using software (Excel, R, MATLAB, Python)