Hash Maps to Improve Time Complexity. The nested loop gives us a time complexity of O(n²) quadratic time. This reduce our time complexity to O(n) with the one loop and the insertion/lookup of our hashmap being O(1).

In this video we will explain a tutorial example of Big O . notation

In this video we talk a bit more about data structures and optimizations, specifically we'll get into linked lists vs arrays, how to do common operations on them, and what happens to the underlying memory.

In this Algorithms and Data Structures Tutorial in C++, I'll give you an Introduction to Algorithms and Data Structures and talk about what it is. We are goi...

Clustered indexes are physically sorted on the disk, you can have only one per table. Simple indexes are logically sorted and a table can have many of those (for significant fields).

The workflow of any Machine Learning algorithm is simple. However, this workflow could become complex if training your data seemed to run forever! This happens when you have big data. The challenges of big data manifest in both inference and computation. However, you cannot do away with data. In analysis, there is never “sufficient” data. As you get more and more data, you can start subdividing the data to gain better insights.

Every computer science student must learn about Big-O Notation, a way to conceptualize algorithm complexity that directly relates to performance of the algorithm. In this article, Camilo Reyes demonstrates how to apply Big-O algorithms to .NET Core applications.