Best GTD for Data Scientists

What GTD actually asks of you

Get every commitment out of your head and into one trusted inbox, then decide the very next physical action for each.

Lists are organized by the verb — call, draft, review — never by topic. The non-negotiable part is the review: without it the lists rot into noise within two weeks.

How data scientists put it to work

1. 1

   An experiment log with its hypothesis

   Every experiment captured with what it hypothesized and what it actually found — including the nulls. The notebook of failed directions is the most valuable file in the project.

2. 2

   The model decision trail

   Why a feature was dropped, why a threshold moved — captured where the analysis happened. Six months later, when someone asks why the model ignores that variable, the answer is linked, not remembered.

3. 3

   Findings that resurface

   When a new question arrives, old analyses that touch it surface on their own. Half of data science is rediscovering that you already answered a version of this question last quarter.

The capture habit, the linking, and the retrieval are the parts tentus automates — gtd without the daily upkeep.

Other methods worth trying