Course Number: YCBS 255
This course introduces fundamental statistical machine learning concepts and tools using Python. Emphasis is placed on the following subjects: descriptive statistics, statistical distributions, random number generation, basic data visualization; linear regression; basic classification; error estimation: cross-validation, bias-variance trade-off; shrinkage methods; dimension reduction; beyond linearity: smoothing splines, local regression, additive models; tree and ensemble methods; powerful classifiers; unsupervised learning.
This course is part of the Professional Development Certificate in Data Science and Machine Learning.
Students must have completed the Introduction to Python for Data Science course prior to registering for Computational Applied Statistics (YCBS 255). As part of the registration process, you will be asked to provide proof of Python course completion.
- Mouloud Belbahri
- Vahid Partovi Nia
Applies Towards the Following Programs
- Professional Development Certificate in Data Science and Machine Learning : Required Courses