Computational Statistics (4/2)

Learning Outcomes :

1. Describe Statistical Computing methods such as random number generation, Monte Carlo simulations, and numerical optimization, essential for solving complex statistical problems.
2. 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.
3.  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:

1. How Computational Statistics Became the Backbone of Modern Data Science
2. Numerical Foundations for Modern Statistical Analysis
3. Practicum Module 1: Exploring the Numerical Foundations of Computational Statistics in Modern Data Science
4. Statistical Algorithms and Parallel Computing for Data Analysis
5. Visual Analytics and Statistical Databases
6. Visual and Programmatic Foundations for Statistical Computing
7. Practicum Module 2 – Statistical Algorithms and Visual Analytics in Parallel
8. Advanced Resampling and Simulation Techniques in Statistical Analysis
9. Adaptive Statistical Modeling: From Splines to GLMs
10. Practicum Module 3 – Statistical Data Visualization and Object-Oriented Programming
11. Intensive Statistical Modeling for Complex and Uncertain Data
12. Adaptive Learning and Efficient Risk Modeling in Statistics
13. Practicum Module 4 – Smoothing, Semiparametric, Robust & Bayesian Computation
14. Selected Applications
15. Practicum Module 5 – Advanced Statistical Modeling for Risk Analysis: SPA, SVM, and Heavy-Tailed VaR Estimation