Quantitative Methods for Business
( 25 Modules )

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

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Quantitative Methods for Business ( 25 Modules )

Quantitative Methods for Business
( 25 Modules )