Advanced Quantitative Methods for Decision Science
( 25 Modules )

Module #1
Introduction to Advanced Quantitative Methods
Overview of the course, importance of quantitative methods in decision science, and expectations.

Module #2
Review of Probability Theory
Review of probability concepts, conditional probability, Bayes theorem, and probability distributions.

Module #3
Review of Statistical Inference
Review of statistical inference concepts, hypothesis testing, confidence intervals, and p-values.

Module #4
Machine Learning Fundamentals
Introduction to machine learning, types of machine learning, and supervised/unsupervised learning.

Module #5
Linear Regression with Multiple Predictors
Extending linear regression to multiple predictors, multicollinearity, and model selection.

Module #6
Generalized Linear Models
Introduction to generalized linear models, logistic regression, and Poisson regression.

Module #7
Decision Trees and Random Forests
Introduction to decision trees, random forests, and ensemble methods.

Module #8
Clustering Analysis
Introduction to clustering analysis, k-means clustering, and hierarchical clustering.

Module #9
Dimensionality Reduction
Introduction to dimensionality reduction, PCA, and t-SNE.

Module #10
Survival Analysis
Introduction to survival analysis, Kaplan-Meier estimates, and Cox proportional hazards model.

Module #11
Time Series Analysis
Introduction to time series analysis, ARIMA models, and forecasting.

Module #12
Text Analytics
Introduction to text analytics, text preprocessing, and topic modeling.

Module #13
Network Analysis
Introduction to network analysis, graph theory, and centrality measures.

Module #14
Simulation Modeling
Introduction to simulation modeling, Monte Carlo methods, and process simulation.

Module #15
Optimization Techniques
Introduction to optimization techniques, linear programming, and dynamic programming.

Module #16
Stochastic Processes
Introduction to stochastic processes, Markov chains, and stochastic modeling.

Module #17
Game Theory
Introduction to game theory, strategic decision making, and Nash equilibrium.

Module #18
Machine Learning for Decision Science
Applications of machine learning in decision science, including reinforcement learning and deep learning.

Module #19
Big Data Analytics
Introduction to big data analytics, Hadoop ecosystem, and Spark.

Module #20
Cloud Computing for Decision Science
Introduction to cloud computing, AWS, and Google Cloud Platform for decision science.

Module #21
Visualization for Decision Science
Introduction to data visualization, Tableau, and Power BI for decision science.

Module #22
Ethics in Decision Science
Importance of ethics in decision science, bias, and fairness in algorithms.

Module #23
Case Studies in Decision Science
Real-world case studies of decision science applications, including healthcare, finance, and marketing.

Module #24
Capstone Project
Students will work on an individual or team project applying advanced quantitative methods to a real-world problem.

Module #25
Course Wrap-Up & Conclusion
Planning next steps in Advanced Quantitative Methods for Decision Science career

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Advanced Quantitative Methods for Decision Science ( 25 Modules )

Advanced Quantitative Methods for Decision Science
( 25 Modules )