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, searching, and indexing. This course will teach the fundamentals of data structures and algorithms with a focus on data science applications. This specialization is targeted towards learners who are broadly interested in programming applications that process large amounts of data (expertise in data science is not required), and are familiar with the basics of programming in python. We will learn about various data structures including arrays, hash-tables, heaps, trees and graphs along with algorithms including sorting, searching, traversal and shortest path algorithms.
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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, searching, and indexing. This course will teach the fundamentals of data structures and algorithms with a focus on data science applications. This specialization is targeted towards learners who are broadly interested in programming applications that process large amounts of data (expertise in data science is not required), and are familiar with the basics of programming in python. We will learn about various data structures including arrays, hash-tables, heaps, trees and graphs along with algorithms including sorting, searching, traversal and shortest path algorithms.
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, searching, and indexing. This course will teach the fundamentals of data structures and algorithms with a focus on data science applications. This specialization is targeted towards learners who are broadly interested in programming applications that process large amounts of data (expertise in data science is not required), and are familiar with the basics of programming in python. We will learn about various data structures including arrays, hash-tables, heaps, trees and graphs along with algorithms including sorting, searching, traversal and shortest path algorithms.
This specialization can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more:
MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder
MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
What's inside
Five courses
Algorithms for Searching, Sorting, and Indexing
(2 hours)
Algorithms for Searching, Sorting, and Indexing
(2 hours)
Trees and Graphs: Basics
(2 hours)
Trees and Graphs: Basics
(2 hours)
Dynamic Programming, Greedy Algorithms
Dynamic Programming, Greedy Algorithms
Approximation Algorithms and Linear Programming
(0 hours)
Approximation Algorithms and Linear Programming
(0 hours)
Advanced Data Structures, RSA and Quantum Algorithms
(0 hours)
Advanced Data Structures, RSA and Quantum Algorithms
(0 hours)Learning objectives
- Organize, store and process data efficiently using sophisticated data structures and algorithms
- Design algorithms and analyze their complexity in terms of running time and space usage
- Create applications that are supported by highly efficient algorithms and data structures for the task at hand
- Organize, store and process data efficiently using sophisticated data structures and algorithms
- Design algorithms and analyze their complexity in terms of running time and space usage
- Create applications that are supported by highly efficient algorithms and data structures for the task at hand
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