Data Science Method Framework — navigate the dizzying plethora of methods for the best approach

In data science there are a lot of methods that solve similar types of problems but how do you choose the right one?

I must admit I love tools. As a hobby, I like building and fixing things and have acquired many tools over the years. Some might wonder how many hammers do you need? How are they different? For some jobs, that specialized tool reduces the effort tremendously and is much more effective than a standard tool. The same can be said for algorithms and methods in Data Science.

If you have been educated as a data scientist and understand the underlying math and have experience using the methods, you will know what situations call for what methods. But if you are just starting your journey in data science or are a citizen data scientist (i.e. your day job is as a marketer) then the large toolbox of methods can be overwhelming. I’m an engineer by education and wanted to put some structure to help me (and hopefully others) organize my toolbox so I have a place to start. I found a couple of excellent resources that helped me frame my understanding… the Udacity nano degree “Predictive Analytics for Business” and the book “The Field Guide to Data Science” by Booz | Allen | Hamilton are 2 among the many useful resources I ran across. Building on those and other sources I created this Methodology Framework spreadsheet.

How to use the Methodology Framework

I’ve simplified this to make it easier to use and don’t pretend to understand all the math behind the data science algorithms. This is a pragmatic approach to understanding what tools to use for the business problem you are trying to tackle.

[Read the full post at Marketing Optimized]

(If you like this content check out my other blog posts

Originally published at on May 6, 2021.

Seasoned marketing operations professional with a love for marketing automation and data science.

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