Mastering Stacks and Queues: Essential Data Structures for Data Science

The Building Blocks for Streamlining Complex Data Science Workflows.

Photo by Google DeepMind on Pexels

I’ve come to understand that the ability to design an excellent algorithm depends on how well you can organize the data you’re working with into a structured and interconnected system — especially for professionals dealing with big data.

Stacks and queues are essential data structures that organize, manipulate, and store data elements in a specific order. They enable data scientists and computer programmers to efficiently execute diverse projects and streamline data handling processes.

Simply put, data structures and algorithms play a crucial role in the proficiency of data scientists.

In my second year at university, my team built a hospital management system. Initially, we used arrays to manage patient check-ins, but it quickly got messy — patients weren’t served in order, and records were hard to track.

That was the moment I indeed saw the importance of data structures and algorithms.

We restructured our logic using queues to handle regular patient check-ins (first-come, first-served) and stacks to manage medical record access — since the most recent patient details needed to be…