Algorithm Design and Analysis

Learn about the core principles of computer science: algorithmic thinking and computational problem solving.

( 0 Reviews )

Created by: Sampath Kannan

Quality Score

Content Quality

Video Quality

Qualified Instructor

Course Pace

Course Depth & Coverage

Overall Score : 0 / 100

Course Description

How do you optimally encode a text file? How do you find shortest paths in a map? How do you design a communication network? How do you route data in a network? What are the limits of efficient computation?
This course, part of the Computer Science Essentials for Software Development Professional Certificate program, is an introduction to design and analysis of algorithms, and answers along the way these and many other interesting computational questions.
You will learn about algorithms that operate on common data structures, for instance sorting and searching; advanced design and analysis techniques such as dynamic programming and greedy algorithms; advanced graph algorithms such as minimum spanning trees and shortest paths; NP-completeness theory; and approximation algorithms.
After completing this course you will be able to design efficient and correct algorithms using sophisticated data structures for complex computational tasks.
Week 1: Mathematical Preliminaries; Asymptotic analysis and recurrence relations; Sorting and Searching; Heaps and Binary Search Trees
Week 2: Algorithm Design Paradigms - Divide-and-Conquer algorithms, Dynamic Programming, Greedy Algorithms
Week 3: Graphs and graph traversals; minimum spanning trees; shortest paths
Week 4: Flows; NP-completeness; Approximation Algorithms

Intermediate Level

Time: 4 Weeks - 8-10 hours per week

Instructor Details

Sampath Kannan

0.0 Rating
0 Reviews

Sampath is the Henry Salvatori Professor and Department Chair in the Department of Computer and Information Science at the University of Pennsylvania. Sampath's research spans several subfields in algorithms. In his work on massive data set algorithms, Sampath explores what can be computed efficiently, and what is not computable. He is also interested in program checking, a paradigm for ensuring the correctness of a program by observing its behavior at run-time, and in algorithmic problems in computational biology, particularly the problem of reconstructing the evolutionary history of a set of species from phenotypic and molecular sequence observations.