How to Prioritize Analytical Work — Part

 By Medium - 2021-03-19

Description

I am going to write a series of short articles about best practices for data science (or interchangably analytics). I strongly believe that the primary challenge to having a good data science…

Summary

  • 1 Generally Analytical Work Segments Well into Four Buckets I am going to write a series of short articles about best practices for data science (or interchangably analytics).
  • Learning how to prioritize analytical work well is the first, basic thing you need to create a well-functioning data science department.
  • priorities change every other week or even every week, which is a major red-flag because most analytical projects take 2–4 weeks in a well-run organization with a culture of creating documentation and applying proper QA to the analytical work To prioritize your projects well, you first need to place them into a small set of buckets: rather it typically is changing small parts to see if the entire thing is improved.

 

Topics

  1. Backend (0.3)
  2. Database (0.23)
  3. Management (0.18)

Similar Articles

15 Essential Steps To Build Reliable Data Pipelines

By Medium - 2020-12-01

If I learned anything from working as a data engineer, it is that practically any data pipeline fails at some point. Broken connection, broken dependencies, data arriving too late, or some external…

Data Science Learning Roadmap for 2021

By freeCodeCamp.org - 2021-01-12

Although nothing really changes but the date, a new year fills everyone with the hope of starting things afresh. If you add in a bit of planning, some well-envisioned goals, and a learning roadmap, yo ...