This course will teach your the fundamentals of Monte Carlo methods for you to apply to your research, work or resume. You'll learn advanced techniques for Monte Carlo methods for generating random variables, integration and inference.
Learn how to create confidence intervals, monitor convergence and nonparametric bootstrap methods. Dive into the EM and MC-EM algorithms for handling missing data.
Explore the principles of Markov Chain Monte Carlo and learn how to apply MCMC to Bayesian analysis in logistic regression and change-point time series analysis
Code these all these algorithms "by hand"
All students need to obtain the lecture notes/slides for self-study
I'm a data scientist with experience spearheading statistical modeling methodologies for various consulting projects. I've a BA in Economics and MS in Statistics. On my spare time I like to contribute to Stack Overflow and Cross Validated.
I am also an Apache Spark developer certified by O'Reilly Media and DataBricks.