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Course module: 880260-M-6
880260-M-6
Computational Statistics
Course info
Course module880260-M-6
Credits (ECTS)6
CategoryMA (Master)
Course typeCourse
Language of instructionEnglish
Offered byTilburg University; Tilburg School of Humanities and Digital Sciences; TSH: Department Cognitive Science and AI; TSH: Department Cognitive Science and AI;
Is part of
M Communication and Information Sciences
M Data Science and Society
Contact persondr. K. Van Deun
Lecturer(s)
Lecturer
dr. K. Van Deun
Other course modules lecturer
Coordinator course
dr. K. Van Deun
Other course modules lecturer
Academic year2019
Starting block
BLOK 2
Course mode
Full-time
Remarks-
Registration openfrom 14/10/2019 up to and including 24/01/2020
Aims
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.

Specifics

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. 


Required Prerequisites

Introductory course in Statistics
Content
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.


Compulsory Reading
  1. Hesterberg et al., The Practice of Business Statistics, Chapter 18.
  2. James, Witten, Hastie & Tibshirani, An Introduction to Statistical Learning.
Timetable information
Computational Statistics
Written test opportunities
DescriptionTestBlockOpportunityDate
Schriftelijk (60%) / Written (60%)EXAM_01BLOK 2116-12-2019
Schriftelijk (60%) / Written (60%)EXAM_01BLOK 2220-01-2020
Written test opportunities (HIST)
DescriptionTest/BlockOpportunityDate
Required materials
-
Recommended materials
-
Tests
Written (60%)

2 Papers (40%)

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Kies de Nederlandse taal