Linear Algebra for Data Science in R

DataCamp

(3)

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

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

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

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...

Course Reviews

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