Linear Algebra for Data Science in R

via

DataCamp

Course Features

Duration

4 hours

Delivery Method

Online

Available on

Limited Access

Accessibility

Mobile, Desktop, Laptop

Language

English

Subtitles

English

Level

Intermediate

Teaching Type

Self Paced

Video Content

4 hours

Course Description

Linear algebra is a key tool in data science, applied math and data science. In this course, you will learn how to work with vectors and matrices, solve matrix-vector equations, perform eigenvalue/eigenvector analyses and use principal component analysis to do dimension reduction on real-world datasets. All analyses will take place in R, which is the most popular programming language.

Course Overview

Virtual Labs

International Faculty

Post Course Interactions

Hands-On Training,Instructor-Moderated Discussions

Skills You Will Gain

Prerequisites/Requirements

Courses to be completed : Introduction to R

What You Will Learn

In this course, you’ll learn how to work with vectors and matrices, solve matrix-vector equations, perform eigenvalue/eigenvector analyses and use principal component analysis to do dimension reduction on real-world datasets

This course is an introduction to linear algebra, one of the most important mathematical topics underpinning data science

Course Content

Expand all sections

Module 1: Introduction to Linear Algebra

Module 2: Matrix-Vector Equations

Module 3: Eigenvalues and Eigenvectors

Module 4: Principal Component Analysis

Course Instructors

Eric Eager

Data Scientist at Pro Football Focus

Eric Eager is a data scientist for Pro Football Focus, where he analyzes data for all 32 National Football League teams and over 40 college football teams. Before joining PFF in 2018, he was a profes...