Principal Component Analysis (PCA) in Python

Key Info
URL - the landing page for the learning resource: 
https://www.datacamp.com/tutorial/principal-component-analysis-in-python
Description - a brief synopsis, abstract or summary of what the learning resource is about: 

Principal Component Analysis (PCA) is a linear dimensionality reduction technique that can be utilized for extracting information from a high-dimensional space by projecting it into a lower-dimensional subspace. It tries to preserve the essential parts that have more variation of the data and remove the non-essential parts with fewer variations.
In this tutorial, you will learn about PCA and how it can be leveraged to extract information from the data without any supervision using two popular datasets: Breast Cancer and CIFAR-10.

According to Wikipedia, PCA is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables (entities each of which takes on various numerical values) into a set of values of linearly uncorrelated variables called principal components.

Authoring Person(s) Name: 
Aditya Sharma
Access Cost: 
No fee
Primary language(s) in which the learning resource was originally published or made available: 
English
More info about
Keywords - short phrases describing what the learning resource is about: 
Data analysis
Data skills education
Data visualization
Programming
Python
Subject Discipline - subject domain(s) toward which the learning resource is targeted: 
Medicine and Health Sciences
Published / Broadcast: 
Monday, June 3, 2019
Publisher - organization credited with publishing or broadcasting the learning resource: 
DataCamp
Media Type - designation of the form in which the content of the learning resource is represented, e.g., moving image: 
Interactive Resource - requires a user to take action or make a request in order for the content to be understood, executed or experienced.
Educational Info
Purpose - primary educational reason for which the learning resource was created: 
Instruction - detailed information about aspects or processes related to data management or data skills.
Learning Resource Type - category of the learning resource from the point of view of a professional educator: 
Lesson - detailed description of an element of instruction in a course, [could be] contained in a unit of one or more lessons, and used by a teacher to guide class instruction. Example: presentation slides on a topic.
Target Audience - intended audience for which the learning resource was created: 
Early-career research scientist
Research scientist
Software engineer
Technology expert group
Intended time to complete - approximate amount of time the average student will take to complete the learning resource: 
Up to 1 hour