Data Structures: An Active Learning Approach
This interactive text used in this course was written with the intention of teaching Computer Science students about various data structures as well as the applications in which each data structure would be appropriate to use. It is currently being taught at the University of California, San Diego (UCSD), the University of San Diego (USD), and the University of Puerto Rico (UPR).
This coursework utilizes the Active Learning approach to instruction, meaning it has various activities embedded throughout to help stimulate your learning and improve your understanding of the materials we will cover. You will encounter "STOP and Think" questions that will help you reflect on the material, "Exercise Breaks" that will test your knowledge and understanding of the concepts discussed, and "Code Challenges" that will allow you to actually implement some of the algorithms we will cover.
Currently, all code challenges are in C++ or Python, but the vast majority of the content is language-agnostic theory of complexity and algorithm analysis. In other words, even without C++ or Python knowledge, the key takeaways can still be obtained.
Upcoming start dates
- Self-paced Online
Who should attend?
- Reading and understanding pseudocode
- Performing time-complexity analysis using Big-O notation
- Working with basic probabilities
- Following formal mathematical proofs
- Programming in either C++ or Python
- Introduction and Review
- Introductory Data Structures
- Tree Structures
- Introduction to Graphs
- Implementing a Lexicon
- Coding and Information Compression
Course delivery details
This course is offered through The University of California, San Diego, a partner institute of EdX.
6-7 hours per week
- Verified Track -$50
- Audit Track - Free
Certification / Credits
What you'll learn
- The algorithms behind fundamental data structures (dynamic arrays, linked structures, (un)balanced trees/tries, graph algorithms, hash tables/functions)
- How to reason about appropriate data structures to solve problems, including their strengths and weaknesses
- How to analyze algorithms theoretically (worst-case, average-case, and amortized)
- The key distinctions and relations between "Abstract Data Types" and "Data Structures"
- Basic information theory and data compression utilizing the data structures covered
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