Learn Advanced Algorithms And Data Structures
Master advanced algorithms and data structures in Python
Learn Advanced Algorithms And Data Structures is an advanced Python course focused on efficient algorithm design and complex data-structure implementations. It emphasizes practical techniques such as deques for stack and queue operations, string‑matching methods like Rabin‑Karp and Knuth‑Morris‑Pratt, tries and binary indexed trees, balanced BST variants, and Hamiltonian algorithms to tackle challenging computational problems.
Hands-on projects and AI-assisted feedback are core to the experience, helping learners build portfolio-ready code and iterate on solutions with targeted guidance. The course is well suited to developers and computer‑science students seeking measurable, interview-relevant skills in algorithm implementation, performance analysis, and applied problem solving.
At a Glance
Learn Advanced Algorithms and Data Structures with Python is an advanced Codecademy course that focuses on efficient algorithm design and complex data-structure implementations using Python.
It covers advanced string-matching techniques, tree structures, deques, and problem-solving strategies, and pairs projects with AI-guided coding feedback to reinforce practical skills.
| Level | Advanced |
| Rating | 4.6 out of 5 |
| Duration | 5+ hours |
| Languages | English |
| Learners | 4K+ learners |
| Certificate | Certificate of completion (Plus/Pro required) |
| Access | Access while your subscription is active |
| Course includes |
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| Price | Subscription-based; paid plans unlock certificate and project features |
What This Course Teaches
This course frames outcomes as measurable competencies: learners practice implementing and analyzing algorithms and data structures in Python to solve well-defined, complex problems.
By course end, learners will implement deques for stack/queue use, write and analyze efficient string‑matching algorithms, build advanced tree structures and balanced BST variants, and apply Hamiltonian path/cycle algorithms in Python.
How the Course Is Structured
The course is organized as a short sequence of focused lessons that together form an advanced, project-oriented module set; the syllabus lists eight lessons and runs for about six hours total. Expect a compact structure: a brief welcome, topic modules on deques, string searching, trees, balanced BSTs, and Hamiltonian algorithms, plus wrap-up and next steps.
Curriculum overview
01Welcome to Learn Advanced Algorithms and Data Structures with Python▾
Introduces the course scope and explains what’s new in this advanced algorithms and data structures offering.
02Double-Ended Queues▾
Covers deque mechanics and their use-cases for efficient stack and queue operations.
- Practice project: Deque Palindrome Project
03String Searching Algorithms▾
Explores Rabin–Karp and Knuth–Morris–Pratt methods for efficient pattern matching.
- Practice project: Rabin‑Karp Algorithm Project (multi-pattern and 2D pattern extensions)
04Trees▾
Introduces trie structures and binary indexed trees to support fast search and prefix operations.
05Balanced Binary Search Trees▾
Discusses balanced BST variants such as B‑trees and splay trees and their trade-offs for storage and access.
06Hamiltonian Algorithms▾
Covers algorithms to find Hamiltonian paths and cycles and approaches to implement them in Python.
07Next Steps▾
Wraps up the course with guidance on continued study, portfolio building, and applications of the covered techniques.
Audience & Requirements
This course targets learners who already have solid Python experience and foundational knowledge of data structures and algorithms and who want to tackle more complex, performance‑oriented problems.
It is appropriate for developers preparing for technical interviews, engineers optimizing algorithmic solutions, and CS students aiming to deepen their algorithmic toolkit.
- Developers preparing for algorithm‑heavy technical interviews or senior engineering roles.
- Software engineers focused on performance and efficient data handling.
- Computer science students or competitive programmers looking to deepen algorithmic skills.
- Learners building portfolio projects that demonstrate advanced algorithm and data‑structure knowledge.
- Completion of Learn Data Structures and Algorithms with Python and Learn Intermediate Python 3 (or equivalent experience).
- Comfort writing idiomatic Python 3 and understanding of basic data structures and algorithmic complexity.
- Willingness to complete code projects and algorithmic quizzes to apply concepts.
Final Verdict
Given its strong rating and broad learner adoption, this is a solid, practice-focused advanced course that effectively combines algorithmic theory with hands-on projects and AI-guided feedback, making it a good choice for Python developers aiming to deepen algorithm and data-structure skills.
Because the course’s certificate and project features are tied to the platform’s subscription access, it delivers the most value to learners who plan to engage with multiple courses or maintain ongoing access; single-topic, one-off learners should weigh that access model against free or shorter alternatives.
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