Working with Algorithms in Python

Video description

Learn how to make your Python code more efficient by using algorithms to solve a variety of tasks or computational problems. In this video course, you’ll learn algorithm basics and then tackle a series of problems—such as determining the shortest path through a graph and the minimum edit distance between two genomic sequences—using existing algorithms.

Computer scientist George Heineman fully implements each algorithm from scratch in real …

Working with Algorithms in Python

Video description

Computer scientist George Heineman fully implements each algorithm from scratch in real time, narrating key concepts and steps along the way, and then demonstrates the execution performance of the algorithm implementations on the model problems.

Algorithms are essential to the way computers process data. The examples you’ll learn in this course are among the most common algorithms in computer science, but they illustrate many of the concerns you’ll face as you work to create algorithms on your own. All code is available on GitHub (https://github.com/heineman/python-algorithms).

The topics in this video course include:

Just enough mathematical concepts to understand how to analyze algorithms
Practical advice to identify code inefficiencies, using algorithm analysis
A description of fundamental data structures (such as binary trees, heaps, and graphs) and their use in efficient algorithms
Problem-solving strategies, including Divide and Conquer, Dynamic Programming, Greedy, and Brute Force approaches
Full implementations of each algorithm in Python within the context of a specific problem
A description of the most common algorithmic families, including constant-time, logarithmic time, linear time, polynomial time, and exponential time

George T. Heineman is an Associate Professor of Computer Science at Worcester Polytechnic Institute in Massachusetts.

Publisher resources

Download Example Code

BinarySearch

Efficient Searching using BinaryArraySearch and Binary Search Trees Part 1

Efficient Searching using BinaryArraySearch and Binary Search Trees Part 2

Creating a Balanced Binary Search Tree from a Sorted List

An Informal Introduction to the Analysis of Algorithms

O (n log n) Behavior

MergeSort: A Divide and Conquer Algorithm

Using MergeSort to Sort External Data

Mathematical Algorithms

Mathematical Algorithms: Exponentiation By Squaring

Using Exponentiation by Squaring to Determine Whether an Integer Is Prime

Brute Force Algorithms

Brute Force: An Algorithm for Solving Combinatoric Problems

Using Brute Force to Generate Magic Squares

K-Dimensional Trees

KD Trees: Efficient Processing of Two-Dimensional Datasets Part 1

KD Trees: Efficient Processing of Two-Dimensional Datasets Part 2

Using KD Trees to Compute Nearest Neighbor Queries

Graph Algorithms

Graph Algorithms: Depth First Search Part 1

Graph Algorithms: Depth First Search Part 2

Using Depth First Search to Construct a Rectangular Maze

AllPairsShortestPath

Graph Algorithms: All Pairs Shortest Path

Using Dynamic Programming to Compute Minimum Edit Distance

Heap Data Structure

The Heap Data Structure and Its Use in HeapSort

Using HeapSort to Sort a Collection

Single-Source Shortest Path

Single-Source Shortest Path: Using Priority Queues

Using Priority Queues to Compute the Minimum Spanning Tree

Summary

Course Summary

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