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10 Modules / ~100 pages
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~25 Modules / ~400 pages
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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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