Building fast and highly performant data science applications requires an intimate knowledge of how data can be organized in a computer and how to efficiently perform operations such as sorting, ...

Description The design, implementation, and analysis of abstract data types, data structures and their algorithms. Topics include: data and procedural abstraction, amortized data structures, trees and ...

Learn when and how to use different data structures and their algorithms in your own code. This is harder as a student, as the problem assignments you'll work through just won't impart this knowledge.

Big-O, Big-Omega, Big-Theta notations, and their meanings. Basic data structures: Heaps, and Binary Search Trees. Algorithm Design Approaches: Divide and Conquer including analysis using recurrences ...

In contrast to other big quantum data algorithms 31, 32, 33, the size of the qRAM required to perform topological and geometric analysis is relatively small: because the computational complexity ...