Explore

Algorithms, Part I

Approx. 54 hours to complete

Save Course

Go to Course

Course Summary

This course is part 1 of a 2-part series on algorithms, covering elementary data structures, sorting, and searching algorithms. Students will learn how to analyze the performance of algorithms and how to implement them in Java.

Key Learning Points

Learn the basics of elementary data structures, sorting, and searching algorithms
Understand how to analyze the performance of algorithms
Implement algorithms in Java

Learning Outcomes

Ability to implement and analyze the performance of basic data structures and algorithms
Understanding of sorting and searching algorithms
Proficiency in Java programming

Prerequisites or good to have knowledge before taking this course

Knowledge of basic programming concepts
Familiarity with Java programming

Course Difficulty Level

Intermediate

Course Format

Online
Self-paced
Video Lectures

Similar Courses

Data Structures and Algorithms Specialization
Algorithms: Design and Analysis, Part 1
Introduction to Algorithms and Data Structures in C++

Related Education Paths

Notable People in This Field

Thomas H. Cormen
Donald E. Knuth
Steven S. Skiena

Related Books

Description

This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations. Part I covers elementary data structures, sorting, and searching algorithms. Part II focuses on graph- and string-processing algorithms.

All the features of this course are available for free. It does not offer a certificate upon completion.

Outline

Course Introduction
Course Introduction
Welcome to Algorithms, Part I

Union−Find
Dynamic Connectivity
Quick Find
Quick Union
Quick-Union Improvements
Union−Find Applications
Overview

Analysis of Algorithms
Analysis of Algorithms Introduction
Observations
Mathematical Models
Order-of-Growth Classifications
Theory of Algorithms
Memory

Stacks and Queues
Stacks
Resizing Arrays
Queues
Generics
Iterators
Stack and Queue Applications (optional)
Overview

Elementary Sorts
Sorting Introduction
Selection Sort
Insertion Sort
Shellsort
Shuffling
Convex Hull

Mergesort
Mergesort
Bottom-up Mergesort
Sorting Complexity
Comparators
Stability

Quicksort
Quicksort
Selection
Duplicate Keys
System Sorts

Priority Queues
APIs and Elementary Implementations
Binary Heaps
Heapsort
Event-Driven Simulation (optional)
Overview

Elementary Symbol Tables
Symbol Table API
Elementary Implementations
Ordered Operations
Binary Search Trees
Ordered Operations in BSTs
Deletion in BSTs
Interview Questions: Elementary Symbol Tables (ungraded)

Balanced Search Trees
2−3 Search Trees
Red-Black BSTs
B-Trees (optional)
Overview
Interview Questions: Balanced Search Trees (ungraded)

Geometric Applications of BSTs
1d Range Search
Line Segment Intersection
Kd-Trees
Interval Search Trees
Rectangle Intersection

Hash Tables
Hash Tables
Separate Chaining
Linear Probing
Hash Table Context
Overview

Symbol Table Applications
Symbol Table Applications: Sets (optional)
Symbol Table Applications: Dictionary Clients (optional)
Symbol Table Applications: Indexing Clients (optional)
Symbol Table Applications: Sparse Vectors (optional)

Summary of User Reviews

Discover the world of Algorithms with Coursera's Algorithms Part 1 course. This course has received overwhelmingly positive reviews from learners. Many users praise the course for its comprehensive coverage of algorithms and its engaging teaching style. Overall, the course is highly recommended for anyone who wants to learn more about algorithms and improve their problem-solving skills.

Key Aspect Users Liked About This Course

The course provides comprehensive coverage of algorithms and teaches learners how to apply these concepts in real-world scenarios.

Pros from User Reviews

Excellent course for beginners and experts alike
Great teaching style that engages learners throughout the course
Course materials are well-structured and easy to follow
Instructors are knowledgeable and responsive to learners' questions
The course provides a solid foundation for further study in the field of algorithms

Cons from User Reviews

Some learners found the pace of the course to be too fast
The course can be challenging for learners without a strong math background
The course requires a significant time commitment to complete
Some learners found the programming assignments to be too difficult
The course could benefit from more interactive elements and hands-on activities

Recommended for you

Recommender Systems and Deep Learning in Python

The most in-depth course on recommendation systems with deep learning, machine learning, data science, and AI techniques What do I mean by “recommender systems”, and why are they useful?...

Save Course

Object Oriented Programming in Java

Welcome to our course on Object Oriented Programming in Java using data visualization. This is an intermediate Java course. This course should take about 6 weeks to complete....

Save Course

Teaching Impacts of Technology: Data Collection, Use, and Privacy

By the end, you’ll be prepared to teach pre-college learners to be both savvy and effective participants in their digital world....

Save Course

Machine Learning

Machine learning is the science of getting computers to act without being explicitly programmed. Many researchers also think it is the best way to make progress towards human-level AI....

Save Course