Advanced Quantitative Methods
( 24 Modules )

Module #1
Introduction to Advanced Quantitative Methods
Overview of advanced quantitative methods, importance, and applications

Module #2
Review of Linear Algebra
Refreshers on vector spaces, eigenvalues, eigenvectors, and matrix operations

Module #3
Multivariate Analysis
Principal component analysis (PCA), factor analysis, and cluster analysis

Module #4
Time Series Analysis
Autoregressive (AR), moving average (MA), and ARIMA models

Module #5
Stochastic Processes
Random walks, Markov chains, and Poisson processes

Module #6
Monte Carlo Methods
Random sampling, simulation, and estimation techniques

Module #7
Bootstrap Resampling
Bootstrap confidence intervals and hypothesis testing

Module #8
Machine Learning Fundamentals
Supervised and unsupervised learning, model evaluation metrics

Module #9
Regression Analysis
Simple and multiple linear regression, model diagnostics

Module #10
Generalized Linear Models
Logistic regression, Poisson regression, and binomial regression

Module #11
Survival Analysis
Hazard rates, Kaplan-Meier estimation, and Cox proportional hazards model

Module #12
Panel Data Analysis
Fixed and random effects models, panel regression

Module #13
Instrumental Variables and Regression Discontinuity
Identification strategies for causal inference

Module #14
Spatial Analysis
Spatial autocorrelation, spatial regression, and geostatistics

Module #15
Network Analysis
Graph theory, network centrality measures, and community detection

Module #16
Text Analysis
Text mining, sentiment analysis, and topic modeling

Module #17
Big Data Analytics
Hadoop, Spark, and NoSQL databases for large-scale data analysis

Module #18
Data Visualization
Effective communication of quantitative results using visualization tools

Module #19
Mixed Models and Bayesian Methods
Mixed-effects models and Bayesian inference using Markov chain Monte Carlo (MCMC)

Module #20
Causal Inference and Structural Equation Modeling
Identification strategies for causal effects and SEM

Module #21
Machine Learning Advanced Topics
Neural networks, deep learning, and ensemble methods

Module #22
High-Dimensional Data Analysis
Dimensionality reduction, feature selection, and sparse modeling

Module #23
Computational Finance and Risk Analysis
Options pricing, risk modeling, and portfolio optimization

Module #24
Course Wrap-Up & Conclusion
Planning next steps in Advanced Quantitative Methods career

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Advanced Quantitative Methods ( 24 Modules )

Advanced Quantitative Methods
( 24 Modules )