Introduction to the fundamental principles of data structures and algorithms and their efficient implementation. Developing algorithmic thinking. Basic toolkit for the design and analysis of ...

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, ...

Tour of the most important data structures, fundamental algorithms, and algorithm design techniques: lists, stacks, queues, dynamic arrays, hash tables, priority queues, disjoint set unions, binary ...

Library collection: 20+ new algorithms, including embedding algorithms for graph ML. Library structure and management: Our library continues to be open-source, on GitHub. We’ve improved the ...

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 ...

Data Science Foundations: Data Structures and Algorithms Courses DTSA 5501 Algorithms for Searching, Sorting, and Indexing DTSA 5502 Trees and Graphs: Basics DTSA 5503 Dynamic Programming, Greedy ...

Dr. Alin Deutsch of UC San Diego explains in a Q&A why graph database algorithms will become the driving force behind the next generation of AI and machine learning apps.

I see it time and again in Google interviews or new-grad hires: The way data structures and algorithms — among the most important subjects in a proper computer science curriculum — are learnt ...