Follow

Linear Algebra for Data Science with examples in R

This course is meant to instill a working knowledge of linear algebra terminology and to lay the foundations of advanced data mining techniques like Principal Component Analysis, Factor Analysis, Collaborative Filtering, Correspondence Analysis, Network Analysis, Support Vector Machines and many more. In order to fully comprehend these important tools and techniques, we will need to understand the language in which they are presented: Linear Algebra. This is NOT a rigorous proof-based mathematics course. It is an intuitive introduction to the most important definitions and concepts that are needed to understand and effectively implement these important data mining methodologies. So that we know how to stir the pileā€¦



By Shaina Race Bennett

Career Relevance by Data Role

The techniques and tools covered in Linear Algebra for Data Science with examples in R are most similar to the requirements found in Business Analyst job advertisements.


Similarity Scores (Out of 100)

Fast Facts

Tools
R

Techniques
Algorithms Applied Mathematics Data Science Data Analysis Data Visualization Functions Programming Principal Component Analysis Cluster Analysis Data Mining Variables Dimension Reduction

Similar Opportunities
Computing for Data Analysis

EdX - Georgia Institute of Technology

Wrangling Data in the Tidyverse

Coursera - Johns Hopkins University

Machine Learning for Data Analysis

Coursera - Wesleyan University

Data Analysis for Decision Making

EdX - University System of Maryland

Data Analysis Tools

Coursera - Wesleyan University