Statistics 697DS - ST-Statistical Methods/DataSci
Spring
2021
01
3.00
Hyunsun Lee
TU 6:00PM 8:30PM
UMass Amherst
82625
Fully Remote Class
hyunsunlee@umass.edu
This course provides an introduction to the statistical techniques that are most applicable to data science. Topics include regression, classification, resampling, linear model selection and regularization, tree-based methods, support vector machines and unsupervised learning. The course includes a computing component using statistical software. Students must have prior experience with a statistical programming language such as R, Python or MATLAB.
Open to Graduate students only. This class meets on the Newton Campus of UMass-Amherst.
Undergraduates may enroll with the permission of the instructor.
Prerequisites: Probability and Statistics at a calculus-based level such as Stat 607 and Stat 608 (concurrent) or Stat 515 and Stat 516 (concurrent), and knowledge of regression at the level of Stat 525 or Stat 625. Students must have an understanding of linear algebra at the level of Math 235.
Undergraduates may enroll with the permission of the instructor.
Prerequisites: Probability and Statistics at a calculus-based level such as Stat 607 and Stat 608 (concurrent) or Stat 515 and Stat 516 (concurrent), and knowledge of regression at the level of Stat 525 or Stat 625. Students must have an understanding of linear algebra at the level of Math 235.