Master Python Data Structures: From Beginner to Advanced
What you will learn:
- Python Data Structures (Built-in and User-Defined)
- Object-Oriented Programming (OOP) in Python
- Data Structure Design and Implementation
- Algorithm Design and Analysis
- Time and Space Complexity Analysis
- Problem-Solving Techniques for Data Structures
- Practical Applications of Data Structures
- Hands-on Coding Exercises and Projects
- Complete Source Code Download
- Comprehensive Quizzes and Assessments
Description
Unlock the power of data structures and conquer algorithmic challenges with this comprehensive Python course. This isn't just another data structures tutorial; it's your all-access pass to mastering the fundamentals and advancing to complex algorithms. We start with the built-in Python data structures – lists, tuples, sets, dictionaries, and 2D arrays – laying a solid foundation through intuitive explanations and hands-on coding exercises. You'll then delve into the world of object-oriented programming (OOP) in Python, learning essential concepts to effectively implement user-defined data structures.
The curriculum progresses to cover crucial advanced data structures, including stacks, queues, deques, linked lists (singly, doubly, and circular), trees (binary, binary search, AVL), heaps, priority queues, hash maps, and graphs. For each, you'll gain a deep understanding of their underlying principles, practical applications, and time/space complexity analysis. We cover essential graph algorithms like Prim's and Kruskal's algorithms for finding minimum spanning trees (MSTs), as well as Trie (keyword tree) data structures.
Throughout the course, you'll actively solve numerous coding problems, solidifying your grasp of each concept. We emphasize a practical, hands-on approach, ensuring you develop not only theoretical understanding but also the ability to apply your knowledge to real-world programming challenges. Finally, download all the source code, a handy cheat sheet, and extra surprise materials to accelerate your learning journey.
Curriculum
Introduction to the course
This introductory section sets the stage, outlining the course's objectives and offering guidance on maximizing your learning experience. Lectures include 'What is this Course About?' and 'How to get most of it?' providing a clear understanding of the course structure and learning approach.
Essential Foundations
This crucial section covers 'IMPORTANT!!' topics setting the ground for further learning. It stresses the importance of understanding foundational concepts before proceeding to advanced topics.
Understanding Data Structures
Lectures in this section provide a clear definition of 'What is a Data Structure' and explore 'Types of Data Structures', laying the foundation for understanding the various data structure types covered later in the course.
Preliminary Skills Assessment
A coding test assesses prior knowledge to gauge your existing proficiency before delving deeper into the course material.
Built-in Python Data Structures
This section begins with an introduction to built-in structures and then systematically explores Lists, Tuples, Sets, and Dictionaries. Each data structure is explained with its introduction, followed by coding examples, methods, and quizzes to check for understanding.
2-D Arrays
This section provides a thorough overview of 2D arrays, covering their intuition, declaration, input, update, traversal, deletion, length and slicing. The concepts are reinforced with a concluding quiz.
Object-Oriented Programming (OOP) in Python
This section introduces the essential OOP concepts needed to understand and create user-defined data structures. Lectures cover terminology, syntax, constructors, the 'self' convention, and practical implementation, culminating in a quiz.
Time and Space Complexity Analysis
Understanding the efficiency of algorithms is paramount. This section provides an intuitive explanation of Time and Space Complexity, along with interactive quizzes to solidify your understanding.
Practice Test
A comprehensive test to check your understanding of the material covered so far.
Effective Problem Solving
This section teaches you how to approach and solve data structure problems effectively by showing example questions.
User-Defined Data Structures
The course introduces User Defined Data Structures and progressively covers Stacks, Queues, Deques, and various types of Linked Lists (singly, doubly, and circular). Each data structure includes explanations, coding examples, applications, solved problems and quizzes.
Trees
This section starts with an introduction to trees. It covers the introduction, terminology, types, applications and basic coding problems like counting nodes and finding the height of a tree, finishing off with quizzes.
Binary Trees and Binary Search Trees
The concepts of Binary Trees (proper, perfect, complete, and degenerate) are thoroughly explained. This section proceeds to cover Binary Search Trees, detailing search, insert, and delete operations with a focus on intuitive explanations and coding examples.
Tree Traversals
This section covers different tree traversal techniques (In-order, Pre-order, Post-order, and Level-order) with intuitive explanations and detailed code examples.
Advanced Tree Structures
This section delves into AVL trees, explaining rotations, insertion, and deletion operations with detailed code examples and ends with quizzes.
Heaps and Heap Sort
This section explains heaps (min and max), covering insertion, deletion, and heapify operations. The section proceeds to introduce Heap Sort with intuitive explanations, code examples, and quizzes.
Priority Queues
This section introduces Priority Queues and explains their operations, implementation (using the `heapq` module and max-heap), and time complexity with quizzes.
Hash Maps and Hash Tables
This section covers hash maps, hash functions, collision handling, operations, time complexity, and provides coding examples and quizzes.
Graphs and Graph Traversals
This section introduces graphs, their representations (adjacency matrix and linked lists), and covers Breadth-First Search (BFS) and Depth-First Search (DFS) with intuitive explanations and code.
Spanning Trees and Minimum Spanning Trees (MSTs)
The concepts of spanning trees and minimum spanning trees (MSTs) are explored, along with applications and quizzes.
Algorithms for Finding MSTs
This section covers Prim's and Kruskal's algorithms for finding MSTs, providing intuitive explanations, code, and time complexity analysis, with quizzes.
Tries (Keyword Trees)
This section introduces Tries (keyword trees), covering their intuition, insertion, search operations, and concludes with applications, time complexity, and quizzes.
Miscellaneous Topics
This section covers several important concepts like lambda functions, subarrays, substrings, subsequences, power sets, the collections library, default dictionaries, the heapq module, and solving a maze problem using a graph search algorithm.
Course Conclusion
The final section offers concluding remarks, provides a link to download the complete course code, a cheat sheet, and additional surprise materials.
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