Member-only story
8 Data Structures every Data Scientist Should Know
From basic data structures to Abstract Data Types
As a Data Scientist, a key part of the job is to be able to understand the best way to organise and structure data so that it can be efficiently stored or accessed depending on the task. This could be for the purpose of inputting the data into a model, storing results from a model implementation, or being able to visualise the data later on. Therefore we need to be able to know what Data Structures we can use along with their benefits or trade-offs. Below is a list of eight Data Structures/types that are useful to be able to know and implement for any part of a Data Scientists' job.
Inbuilt Python data structures
The first place to start is Inbuilt Python data structures that are likely to be encountered at the beginning of any Data Scientists' journey and interaction with Python. These are the inbuilt Data Structure within Python that can be built on to implement Abstract Data Types or more complex Data Structures later on.
List
The list is one of the first data structures that you are likely to encounter in your python programming journey. In essence, they act like a real-life list, where you can store multiple points of data within a single structure…
