Ementorhub

What you'll learn

Apply resampling methods in order to obtain additional information about fitted models.

Optimize fitting procedures to improve prediction accuracy and interpretability.

Identify the benefits and approach of non-linear models.

Skills you'll gain

Statistical Analysis

Dimensionality Reduction

Performance Tuning

Regression Analysis

Advanced Analytics

Data Science

Statistical Machine Learning

Statistical Modeling

Sampling (Statistics)

Machine Learning Methods

Probability Distribution

Statistical Methods

Statistical Inference

Predictive Modeling

There are 5 modules in this course

This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.

Generalized Least Squares

Shrink Methods

Cross-Validation

Bootstrapping