Module #1 Introduction to Quantitative Methods Overview of quantitative methods, importance in business decision making, and course objectives
Module #2 Descriptive Statistics Measures of central tendency, variability, and data visualization
Module #3 Probability Theory Basic concepts of probability, conditional probability, and Bayes theorem
Module #4 Random Variables and Distributions Discrete and continuous random variables, probability distributions, and expected value
Module #5 Sampling and Sampling Distributions Types of sampling methods, sampling distributions, and central limit theorem
Module #6 Confidence Intervals Construction and interpretation of confidence intervals for population means and proportions
Module #7 Hypothesis Testing Basic concepts of hypothesis testing, test statistics, and p-values
Module #8 One-Sample Hypothesis Testing Testing hypotheses about population means and proportions using one-sample tests
Module #9 Two-Sample Hypothesis Testing Testing hypotheses about the difference between two population means and proportions
Module #10 ANOVA and Regression Analysis Introduction to ANOVA and regression analysis, including simple and multiple regression
Module #11 Model Building and Validation Model building, validation, and diagnostics in regression analysis
Module #12 Time Series Analysis Introduction to time series analysis, including trend analysis and seasonality
Module #13 Forecasting Methods Overview of forecasting methods, including moving averages, exponential smoothing, and ARIMA models
Module #14 Linear Programming Introduction to linear programming, including graphical method and simplex method
Module #15 Integer Programming Introduction to integer programming, including binary integer programming and branch and bound method
Module #16 Dynamic Programming Introduction to dynamic programming, including applications in operations research
Module #17 Decision Analysis Introduction to decision analysis, including decision trees and sensitivity analysis
Module #18 Simulation Modeling Introduction to simulation modeling, including discrete-event simulation and Monte Carlo simulation
Module #19 Optimization Techniques Overview of optimization techniques, including gradient descent and genetic algorithms
Module #20 Data Mining and Business Intelligence Introduction to data mining and business intelligence, including data warehousing and OLAP
Module #21 Predictive Modeling Introduction to predictive modeling, including logistic regression and decision trees
Module #22 Text Analytics Introduction to text analytics, including sentiment analysis and topic modeling
Module #23 Quantitative Methods in Finance Applications of quantitative methods in finance, including risk analysis and portfolio optimization
Module #24 Quantitative Methods in Marketing Applications of quantitative methods in marketing, including marketing mix modeling and customer segmentation
Module #25 Course Wrap-Up & Conclusion Planning next steps in Quantitative Methods for Business career
