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.
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English
پښتو, বাংলা, اردو, 3 more
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
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

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.
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
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