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Data Structures and Performance

Master comprehensive data structure implementations and performance optimization techniques for efficient Java programming and application development.

Master comprehensive data structure implementations and performance optimization techniques for efficient Java programming and application development.

This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Object Oriented Java Programming: Data Structures and Beyond Specialization or Object Oriented Programming in Java Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.

4.8

(2,221 ratings)

1,05,342 already enrolled

Instructors:

English

پښتو, বাংলা, اردو, 3 more

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Data Structures and Performance

This course includes

41 Hours

Of Self-paced video lessons

Intermediate Level

Completion Certificate

awarded on course completion

Free course

What you'll learn

  • Implement and analyze complex data structures in Java

  • Apply Big-O notation for algorithm performance analysis

  • Create efficient text processing and manipulation tools

  • Design and test robust data structure implementations

  • Optimize code performance using appropriate data structures

Skills you'll gain

Data Structures
Algorithm Analysis
Big-O Notation
Linked Lists
Binary Trees
Hash Tables
Performance Optimization
Java Programming

This course includes:

8.3 Hours PreRecorded video

16 assignments

Access on Mobile, Tablet, Desktop

FullTime access

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There are 6 modules in this course

This comprehensive course focuses on implementing and analyzing industry-level data structures in Java. Students learn about efficient data organization and retrieval through hands-on experience with linked lists, trees, and hash tables. The curriculum covers critical concepts including asymptotic analysis, benchmarking, and algorithm efficiency, while building a practical text editor application. Special emphasis is placed on understanding performance implications and making informed implementation choices.

Introduction to the Course

Module 1 · 2 Hours to complete

Working with Strings

Module 2 · 8 Hours to complete

Efficiency Analysis and Benchmarking

Module 3 · 6 Hours to complete

Interfaces, Linked Lists vs. Arrays, and Correctness

Module 4 · 10 Hours to complete

Trees! (including Binary Search Trees and Tries)

Module 5 · 6 Hours to complete

Hash Maps and Edit Distance

Module 6 · 6 Hours to complete

Fee Structure

Instructors

Leo Porter
Leo Porter

5 Courses

Distinguished Computer Science Educator and Educational Innovation Pioneer

Dr. Leo Porter serves as a Professor of Computer Science at UC San Diego, where he co-founded the Computing Education Research Laboratory focused on understanding how students learn computing and creating inclusive learning environments. His journey includes service as a surface warfare officer in the U.S. Navy's Pacific fleet and Operation Iraqi Freedom before earning his M.S. and Ph.D. in computer science from UC San Diego in 2007. His groundbreaking research in computer science education, particularly on Peer Instruction and active learning pedagogies, has earned numerous accolades, including Best Paper Awards at SIGCSE and the International Computing Education Research Conference. Recently, he co-authored "Learn AI-Assisted Python Programming" with Daniel Zingaro, addressing the integration of AI tools in programming education. His research spans computer architecture, educational technology, and student learning assessment, with particular emphasis on using data-driven approaches to predict student outcomes and identify critical course concepts. As a Distinguished Member of the ACM, he has influenced over 500,000 learners through popular Coursera and edX courses while maintaining active research in both computer science education and computer architecture.

Christine Alvarado
Christine Alvarado

4.7 rating

39 Reviews

3,91,013 Students

5 Courses

Champion of Inclusive Computer Science Education

Christine Alvarado serves as Associate Teaching Professor in Computer Science and Engineering at UC San Diego and Associate Dean for the Division of Undergraduate Education. Her distinguished career combines technical expertise with a passionate commitment to diversifying computer science education. After earning her Ph.D. from MIT in 2004, she began her academic career at Harvey Mudd College before joining UCSD in 2012. Her innovative work includes founding the CSE Early Research Scholars Program, which has engaged over 339 early undergraduates in computing research, with significant participation from women, non-binary students, and underrepresented racial groups

Data Structures and Performance

This course includes

41 Hours

Of Self-paced video lessons

Intermediate Level

Completion Certificate

awarded on course completion

Free course

Testimonials

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Frequently asked questions

Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.