## Description

Algorithms Specialization is the name of a training set for thinking similar to a computer scientist, and with the help of this course, you will gain skills in the fundamental principles of designing and analyzing algorithms. Algorithms are the heart of computer science and this subject has countless practical applications. This course set provides an introduction to algorithms for learners who have little programming experience. Although this course is relatively hard, it emphasizes on the general and conceptual view of your understanding in the implementation of low level and partial mathematics. By the end of this course, you will be at a level where you can excel in technical interviews and speak fluently about algorithms with other programmers and computer scientists.

### Skills you will acquire in the Algorithms Specialization series:

• Algorithms
• Dynamic programming
• Greedy algorithm
• Divide and conquer algorithms
• randomization algorithm
• Sorting algorithm
• Charts
• Data structure
• Hash Table
• A subgraph of a graph
• Np-Completeness

### Course details:

Publisher: Coursera
teacher: Tim Roughgarden
English language
Education level: Intermediate
Number of courses: 4
Duration: 2 months including 10 working hours per week

### Course prerequisites:

Learners should know how to program in at least one programming language (like C, Java, or Python); some familiarity with proofs, including proofs by induction and by contradiction; and some discrete probability, like how to calculate the probability that a poker hand is a full house. At Stanford, a version of this course is taken by sophomore, junior, and senior-level computer science majors.

### Installation guide

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Subtitles: English and other languages

Quality: 720

Version 3/2024 has not changed compared to 2021/2 in the number of lessons and duration, but 219 text files have been added.

This collection includes 4 different courses.

Divide and Conquer, Sorting and Searching, and Randomized Algorithms

Graph Search, Shortest Paths, and Data Structures

Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming

Shortest Paths Revisited, NP-Complete Problems and What To Do About Them