Computational Statistics
Learning Outcomes:
- Describe Statistical Computing methods such as random number generation, Monte Carlo simulations, and numerical optimization, essential for solving complex statistical problems.
- Explain computationally intensive techniques in Statistical Methodology like resampling, cross-validation, Bayesian analysis, and survival analysis, which are crucial for modern data-driven decision-making.
- Correlate real-world uses of computational statistics in fields such as finance, biology, and network security, where large datasets and complex models demand innovative statistical solutions.
Topics:
- How Computational Statistics Became the Backbone of Modern Data Science
- Numerical Foundations for Modern Statistical Analysis
- Statistical Algorithms and Parallel Computing for Data Analysis
- Visual Analytics and Statistical Databases
- Visual and Programmatic Foundations for Statistical Computing
- Advanced Resampling and Simulation Techniques in Statistical Analysis
- Adaptive Statistical Modeling: From Splines to GLMs
- Intensive Statistical Modeling for Complex and Uncertain Data
- Adaptive Learning and Efficient Risk Modeling in Statistics
- Selected Applications
Practicum:
- Exploring the Numerical Foundations of Computational Statistics in Modern Data Science
- Statistical Algorithms and Visual Analytics in Parallel
- Statistical Data Visualization and Object-Oriented Programming
- Smoothing, Semiparametric, Robust & Bayesian Computation”
- Advanced Statistical Modeling for Risk Analysis: SPA, SVM, and Heavy-Tailed VaR Estimation
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