Advanced Statistical Methods
( 25 Modules )

Module #1
Introduction to Advanced Statistical Methods
Overview of the course and importance of advanced statistical methods in data analysis

Module #2
Review of Linear Regression
Refresher on simple and multiple linear regression, including assumptions and diagnostics

Module #3
Generalized Linear Models
Introduction to generalized linear models, including logistic regression, Poisson regression, and log-linear models

Module #4
Non-Linear Regression
Exploration of non-linear regression models, including polynomial regression, splines, and generalized additive models

Module #5
Time Series Analysis
Introduction to time series analysis, including auto-correlation, partial auto-correlation, and moving average models

Module #6
ARIMA Models
In-depth coverage of ARIMA models, including identification, estimation, and forecasting

Module #7
Vector Auto-Regressive Models
Introduction to vector auto-regressive models, including stationarity, cointegration, and impulse response analysis

Module #8
Machine Learning for Time Series
Application of machine learning algorithms to time series data, including recurrent neural networks and long short-term memory networks

Module #9
Survival Analysis
Introduction to survival analysis, including Kaplan-Meier curves, censoring, and Cox proportional hazards model

Module #10
Cluster Analysis
Introduction to cluster analysis, including hierarchical and k-means clustering, and cluster validation

Module #11
Dimensionality Reduction
Introduction to dimensionality reduction techniques, including principal component analysis, t-SNE, and manifold learning

Module #12
Introduction to Bayesian Statistics
Overview of Bayesian statistics, including prior distributions, posterior distributions, and Bayesian inference

Module #13
Bayesian Linear Regression
Application of Bayesian methods to linear regression, including convergence diagnostics and model selection

Module #14
Markov Chain Monte Carlo
In-depth coverage of Markov chain Monte Carlo methods, including Gibbs sampling and Metropolis-Hastings algorithm

Module #15
Hierarchical Models
Introduction to hierarchical models, including linear mixed effects models and generalized linear mixed models

Module #16
Spatial Statistics
Introduction to spatial statistics, including spatial auto-correlation, spatial regression, and geostatistics

Module #17
Network Analysis
Introduction to network analysis, including graph theory, network centrality measures, and community detection

Module #18
Resampling Methods
Introduction to resampling methods, including bootstrapping, cross-validation, and permutation tests

Module #19
Advanced Hypothesis Testing
In-depth coverage of advanced hypothesis testing, including multiple testing corrections and non-parametric tests

Module #20
Meta-Analysis
Introduction to meta-analysis, including fixed effects, random effects, and meta-regression models

Module #21
Structural Equation Modeling
Introduction to structural equation modeling, including path analysis and latent variable models

Module #22
Latent Class Analysis
Introduction to latent class analysis, including finite mixture models and latent class regression

Module #23
Big Data Analytics
Introduction to big data analytics, including distributed computing, Hadoop, and Spark

Module #24
Statistical Computing
In-depth coverage of statistical computing, including R, Python, and SQL

Module #25
Course Wrap-Up & Conclusion
Planning next steps in Advanced Statistical Methods career

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Advanced Statistical Methods ( 25 Modules )

Advanced Statistical Methods
( 25 Modules )