This repository is established to document and share my individual learning journey from the Data Structures course at my university. It emphasizes consolidating and disseminating knowledge on data structures, including concepts, design, and their application in efficiently managing and structuring data for effective big data processing and AI utilization, all without directly citing or employing the instructor's proprietary materials.
The main objective of this repository is to organize my learnings, reflect on the taught subjects, and deepen my comprehension of data structures
- Foundational Concepts: Study of the basic to intermediate theories of data structures and algorithms, crucial to computer science curriculum.
- Practical Application: Through practice, understand the core of data structures and how they are implemented in software development.
- Advanced Topics: Application of data structures principles in developing algorithms to enhance programming capabilities.
- Week 1: Orientation, Course Introduction, and Assignments. Introduction to Data Structures.
- Week 2: Python Basics - Features and execution methods of the Python language.
- Week 3: Recursion and Inductive Reasoning - Data structures and recursion, recursion, and mathematical induction.
- Week 4: Algorithm Performance - Algorithm execution time and complexity.
- Week 5: Concepts of Lists, Arrays, and Linked Lists.
- Week 6: Concept of Stacks, Implementing Stacks using Lists and Linked Lists.
- Week 7: Concept of Queues, Implementing Queues using Lists and Linked Lists.
- Week 8: Midterm Exam.
- Week 9: Concept of Heaps, Heap Operations Algorithms, and Implementation.
- Week 10: Concept of Sorting, Sorting Algorithms.
- Week 11: Indexes and Binary Search Trees - Concepts and algorithms.
- Week 12: Balanced Search Trees - Concepts of AVL Trees, Red-Black Trees, and B-Trees.
- Week 13: Concept of Hash Tables, Hash Functions, and Collision Resolution.
- Week 14: Concepts and Representations of Graphs, Applications of Graphs.
- Week 15: Final Exam
Summaries of key concepts and personal insights gained during the course. This section is based on my interpretations and understanding of the topics covered.
- Lecture Notes: Personal notes taken during lectures, summarizing key points and concepts.
- Exercises and Examples: Practice exercises and examples covered in class, with detailed explanations of my solutions.
- Reference Materials: A list of additional resources and materials that I found helpful for understanding course topics.
This repository is a personal documentation of my learning journey. If you have suggestions, corrections, or additional insights related to the course material, feel free to open an issue or submit a pull request. All forms of contributions and feedback are welcome.
All content within this repository has been created solely based on my personal study and understanding of the material covered in the Data Structure at School, and is intended for educational purposes only. The content is based on my insights and interpretation of the course, intended for personal and non-commercial use. It does not reproduce or distribute any official course materials, direct quotations, or copyrighted content provided by the instructor. This content is not meant to replace or replicate the materials provided in the course and should not be considered an official representation of the course content.
Note: This repository is my personal note and interpretation of the learned course material. It is not an official course repository and does not directly reference or use any copyrighted materials provided by the instructor.