Principles

Home » Book » Principles

 

I find joy in learning more about the science of predictive modeling, coding it, seeing improvement in performance, etc. However, the practice of predictive modeling will fall short without great guiding principles. This volume of the ebook presents key principles that will help you maximize the impact of your predictive modeling efforts.


Table of Contents for Volume 1: Principles

Note: You will notice that most of the table of contents is not yet hyperlinked. This is because I am still working on those posts! I am adding new content every week, please check back soon.

2.1. Modeling & Prediction
> 2.1.1 What is a model
> 2.1.2 The Notion of Predictability
> 2.1.3 Frequentist & Bayesian Perspectives
> 2.1.4 Classical, Modern, Machine
> 2.1.5 Artificial Intelligence
> 2.1.6 Principles of Inference
> 2.1.7 Principle of Parsimony
> 2.1.8 Science vs. Art
> 2.1.9 Accuracy & Precision
2.2. Focusing on Solution Design
> 2.2.1 An empathetic connection with the user
2.3. Model Selection & Specification
2.4. Dealing with Uncertainty
2.5. Practical Matters
> 2.5.1 Blueprinting & Prototyping
> 2.5.2 Prototyping
> 2.5.3 Managing Expectation
> 2.5.4 Managing Scope
> 2.5.5 Communication
>> 2.5.5.1 Audience Assessment & Modular Communication
> 2.5.6 Documentation
>> 2.5.6.1 Excel Documentation
>> 2.5.6.2 SQL Documentation
>> 2.5.6.3 Project Documentation
>> 2.5.6.4 Notes & Assumptions
> 2.5.7 Monitoring & Maintenance
> 2.5.8 Folder Organization
2.6. Responsibility & Ethics
> 2.6.1 With great power…

 


Recent Posts for Principles

Since most of the table of contents is not yet hyperlinked, you can see some of the more recent posts below for easier access.

2019
0
POSTED: January 13, 2019
There is a revolution happening in analytics and Artificial Intelligence (Ai) is at its center. My g
2018
0
POSTED: December 30, 2018
The concepts that I am referring to as blueprinting and prototype are hardly deserving of such fan