Regression and Classification (Coursera)

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.

What You Will Learn

- Express why Statistical Learning is important and how it can be used.

- Identify the strengths, weaknesses and caveats of different models and choose the most appropriate model for a given statistical problem.

- Determine what type of data and problems require supervised vs. unsupervised techniques.

Syllabus

WEEK 1

Statistical Learning Introduction

Introduction to overarching and foundational concepts in Statistical Learning.

WEEK 2

Accuracy

Exploration into assessing models in different situations. How do we define a "best" model for given data?

WEEK 3

Simple Linear Regression

Introduction to Simple Linear Regression, such as when and how to use it.

WEEK 4

Multiple Linear Regression

A deep dive into multiple linear regression, a strong and extremely popular technique for a continuous target.

WEEK 5

Classification Overview
WEEK 6

Classification Models