Upon completion of this course students:
• can evaluate bias, variance, and relative efficiency of given statistics using Monte Carlo simulation techniques making use of the R statistical software package.
• can draw valid conclusions on the (relative) performance of given statistics when presented with results on bias, variance, and relative efficiency under particular simulation conditions.
• can apply the (non-)parametric bootstrap and the permutation test to given data making use of the R statistical software package.
• can draw valid conclusions from the output resulting of a (non-)parametric bootstrap or permutation testing procedure.
• can find trends and patterns in (big) multivariate data making use of (unsupervised) dimension reduction and clustering techniques.
• can apply subset selection, shrinkage, and dimension reduction techniques to build prediction models in a context of big/high-dimensional data and can apply the resulting predictive models to yet unseen data.
• can assess model accuracy making use of resampling techniques like cross-validation.
The final grade will be based on two items:
- 3 written homework assignments, the best 2 of which each count for 20% of the final grade.
- A written exam (closed book) which counts for the remaining 60% of the final grade.
If you reach less than 50% of the points on the final exam, then you will fail the course, regardless of the points you collected with the homework assignments.
Introductory course in Statistics
Doing data analysis is performing computations and with the advent of powerful computing facilities newer and more complicated data analysis methods became possible. With data becoming ever more complex "Computational Statistics became the backbone of modern data science". Data scientists with strong computational skills will be able to solve the many non-standard problems they will encounter in their job.
- Hesterberg et al., The Practice of Business Statistics, Chapter 18.
- James, Witten, Hastie & Tibshirani, An Introduction to Statistical Learning.