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Περιγραφή
Description:
This comprehensive course offers an in-depth exploration of fundamental concepts in data structures and algorithms, equipping learners with the knowledge and skills necessary to tackle complex programming challenges with confidence. Whether you're a beginner seeking to establish a strong foundation in computer science or an experienced programmer aiming to enhance your problem-solving abilities, this course has something to offer for everyone.
Course Content:
Introduction to Data Structures and Algorithms: Understand the importance of data structures and algorithms in computer science. Learn about basic terminology, key concepts, and common applications.
Arrays and Linked Lists: Dive into the world of linear data structures. Explore arrays and linked lists, their implementation, operations, advantages, and limitations.
Stacks and Queues: Learn about stack and queue data structures, their applications, and implementation using arrays and linked lists. Understand concepts like LIFO and FIFO.
Trees and Binary Trees: Explore hierarchical data structures. Study binary trees, their properties, traversal algorithms (inorder, preorder, postorder), and common applications.
Graphs: Delve into graph theory. Understand graph representations (adjacency matrix, adjacency list), traversal algorithms (DFS, BFS), and applications like shortest path algorithms.
Sorting and Searching Algorithms: Master sorting algorithms (bubble sort, insertion sort, selection sort, merge sort, quick sort) and searching algorithms (linear search, binary search). Analyze their time and space complexity.
Hashing: Learn about hash functions, collision resolution techniques (chaining, open addressing), and applications of hashing in data storage and retrieval.
Advanced Data Structures: Explore advanced data structures such as heaps, hash maps, AVL trees, red-black trees, and tries. Understand their implementation and usage scenarios.
Dynamic Programming: Grasp the concept of dynamic programming and learn how to apply it to solve optimization problems efficiently.
Greedy Algorithms: Understand greedy algorithms and their application in solving optimization problems by making locally optimal choices.
Algorithmic Problem Solving: Practice solving a variety of algorithmic problems, ranging from simple to complex, to sharpen your problem-solving skills.
Complexity Analysis: Master the techniques for analyzing the time and space complexity of algorithms. Understand Big O notation and its significance.
Course Format:
Detailed written content covering each topic comprehensively.
Interactive coding exercises and quizzes to reinforce learning.
Real-world examples and case studies to demonstrate the practical relevance of concepts.
Hands-on programming assignments to apply theoretical knowledge in practical scenarios.
Optional peer-to-peer discussion forums for collaborative learning and problem-solving.
Prerequisites:
Basic understanding of programming concepts (variables, loops, conditionals).
Familiarity with a programming language such as Python, Java, or C++.
Outcome:
By the end of this course, you will have:
A solid understanding of essential data structures and algorithms.
Proficiency in analyzing and solving algorithmic problems efficiently.
Enhanced problem-solving skills and critical thinking abilities.
Confidence to tackle coding interviews and competitive programming challenges.
A strong foundation for pursuing advanced topics in computer science.
This version focuses solely on the content and does not mention any video lectures.
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