Connect the Dots: Factor Analysis
4.4 (31 ratings)
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Connect the Dots: Factor Analysis

Factor extraction using PCA in Excel, R and Python
4.4 (31 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
6,653 students enrolled
Created by Loony Corn
Last updated 3/2017
English
English [Auto-generated]
Current price: $10 Original price: $50 Discount: 80% off
5 hours left at this price!
30-Day Money-Back Guarantee
Includes:
  • 1.5 hours on-demand video
  • 18 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • Use Principal Components Analysis to Extract Factors
  • Build Regression Models with Principal Components in Excel, R, Python
View Curriculum
Requirements
  • No statistics background required. Everything is built up from basic math
  • The models are implemented in Excel, R and Python. Install these environments to follow along with the demos
Description

 Factor analysis helps to cut through the clutter when you have a lot of correlated variables to explain a single effect.  

This course will help you understand Factor analysis and it’s link to linear regression. See how Principal Components Analysis is a cookie cutter technique to solve factor extraction and how it relates to Machine learning . 

What's covered?

Principal Components Analysis 

  • Understanding principal components
  • Eigen values and Eigen vectors
  • Eigenvalue decomposition
  • Using principal components for dimensionality reduction and exploratory factor analysis. 

Implementing PCA in Excel, R and Python

  • Apply PCA to explain the returns of a technology stock like Apple
  • Find the principal components and use them to build a regression model 


Using discussion forums

Please use the discussion forums on this course to engage with other students and to help each other out. Unfortunately, much as we would like to, it is not possible for us at Loonycorn to respond to individual questions from students:-(

We're super small and self-funded with only 2 people developing technical video content. Our mission is to make high-quality courses available at super low prices.

The only way to keep our prices this low is to *NOT offer additional technical support over email or in-person*. The truth is, direct support is hugely expensive and just does not scale.

We understand that this is not ideal and that a lot of students might benefit from this additional support. Hiring resources for additional support would make our offering much more expensive, thus defeating our original purpose.

It is a hard trade-off.

Thank you for your patience and understanding!

Who is the target audience?
  • Yep! Data analysts who want to move from summarizing data to explaining and prediction
  • Yep! Folks aspiring to be data scientists
  • Yep! Any business professionals who want to apply Factor analysis and Linear regression to solve relevant problems
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Curriculum For This Course
19 Lectures
01:36:19
+
Introduction
1 Lecture 01:45

We start with an introduction, what the course is about and what you'll be able to do at the end of it 

Preview 01:45
+
Factor Analysis and PCA
2 Lectures 15:03

Understand the link between Factor analysis and regression and how they are different

Preview 08:03

Introducing Principal Components Analysis

Factor Analysis and PCA
07:00
+
Basic Statistics Required for PCA
3 Lectures 21:07

Covariance and Covariance Matrices
11:45

Covariance vs Correlation
03:19
+
Diving into Principal Components Analysis
5 Lectures 21:26
The Intuition Behind Principal Components
05:16

Finding Principal Components
07:10

Understanding the Results of PCA - Eigen Values
04:05

Using Eigen Vectors to find Principal Components
02:30

When not to use PCA
02:25
+
PCA in Excel
4 Lectures 19:06

Computing Correlation and Covariance Matrices
03:27

PCA using Excel and VBA
05:51

PCA and Regression
02:56
+
PCA in R
3 Lectures 11:10
Setting up the data
05:16

PCA and Regression using Eigen Decomposition
03:58

PCA in R using packages
01:56
+
PCA in Python
1 Lecture 06:42
PCA and Regression in Python
06:42
About the Instructor
Loony Corn
4.3 Average rating
5,450 Reviews
42,491 Students
75 Courses
An ex-Google, Stanford and Flipkart team

Loonycorn is us, Janani Ravi and Vitthal Srinivasan. Between us, we have studied at Stanford, been admitted to IIM Ahmedabad and have spent years  working in tech, in the Bay Area, New York, Singapore and Bangalore.

Janani: 7 years at Google (New York, Singapore); Studied at Stanford; also worked at Flipkart and Microsoft

Vitthal: Also Google (Singapore) and studied at Stanford; Flipkart, Credit Suisse and INSEAD too

We think we might have hit upon a neat way of teaching complicated tech courses in a funny, practical, engaging way, which is why we are so excited to be here on Udemy!

We hope you will try our offerings, and think you'll like them :-)