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Data Science Foundations: Data Structures and Algorithms Specialization

Category: Courses
Author: AD-TEAM
Date added: 09.01.2023 :06:11
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Data Science Foundations: Data Structures and Algorithms Specialization



Data Science Foundations: Data Structures and Algorithms Specialization | Coursera
English | Size: 5.58 GB
Genre: eLearning


WHAT YOU WILL LEARN
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
Explain fundamental concepts for algorithmic searching and sorting

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.

The courses in this specialization can be taken for academic credit as part of CU Boulder's Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder's departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics







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