Specialization: Data Science Foundations: Data Structures and Algorithms Instructor: Sriram Sankaranarayanan, Assistant Professor Prior knowledge needed: Mathematical Background: We expect that the ...

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, ...

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 search ...

Developing algorithmic thinking. Basic toolkit for the design and analysis of algorithms: Running time, Recurrence relations, Big-O notation, Correctness, Finite induction, Loop invariants. Tour of ...

Get an overview of data structures and algorithms and how they work together in your ... A case could even be made that a data structure’s basic ... this is known as asymptotic analysis.

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.

An introduction to the analysis and implementation of algorithms and data structures including linear data structures, trees, graphs, hash tables, searching algorithms, sorting algorithms, ...

Here, the authors propose a quantum machine learning algorithm that provides an exponential speed up over known algorithms for topological data analysis. Extracting useful information from large ...