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 2-3 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
