Ementorhub

What you'll learn

Articulate some recommended practices for ethical behavior and communication in statistics and data science.

Interpret important components of the MLR model, including the “systematic” and “random” components of the model.

Describe and implement testing-based procedures for model selections and select a “best” model based on a given procedure.

Skills you'll gain

Predictive Modeling

Statistical Methods

R Programming

Statistical Analysis

Data Ethics

Statistical Inference

Data Modeling

Forecasting

Probability & Statistics

Data Science

Statistical Modeling

Correlation Analysis

Linear Algebra

Statistical Hypothesis Testing

Regression Analysis

There are 6 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. Logo adapted from photo by Vincent Ledvina on Unsplash

Linear Regression Parameter Estimation

Inference in Linear Regression

Prediction and Explanation in Linear Regression Analysis

Regression Diagnostics

Model Selection and Multicollinearity