Data Structures with Problem Solving
- Description
- Curriculum
- Reviews
Through this course, you will understand the core concepts of various data structures, learn how to implement them, and apply them to solve real-world problems. The blend of theoretical knowledge and practical problem-solving ensures that you can effectively use data structures in your coding projects and algorithms.
Main Features:
- Comprehensive Curriculum: Detailed coverage of fundamental data structures, including arrays, linked lists, stacks, queues, trees, graphs, heaps, hashing, tries, and segment trees.
- Hands-On Activities: Practical exercises and projects to reinforce each topic and develop real-world problem-solving skills.
- Step-by-Step Guidance: Easy-to-follow lessons from basic definitions to advanced applications and problem-solving techniques.
- Focus on Problem Solving: Emphasis on applying data structures to solve a variety of coding challenges.
- Interactive Learning: Use of modern tools and platforms for an engaging learning experience.
- Real-Time Support: Access to mentors and community support for assistance and deeper understanding.
- Flexible Learning Pace: Complete the course in 20 hours at your own pace, accommodating your schedule and learning style.
Target Audience:
- Beginners in Programming: Individuals with basic programming knowledge who want to understand and use data structures effectively.
- Students: High school or college students who need to learn data structures for their academic courses or projects.
- Career Changers: Professionals from non-technical fields looking to transition into software development or computer science.
- Aspiring Developers: Individuals aiming to build a solid foundation in data structures for careers in software development, data analysis, or competitive programming.
- Hobbyists: Enthusiasts interested in learning data structures for personal projects or to enhance their technical knowledge.
- Tech Professionals: IT professionals seeking to add data structure skills to their repertoire for career growth or job requirements.
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1
Introduction to Data Structures and Problem Solving-
Introduction to Data Structures
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Definition and importance.
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Overview of different types: linear and non-linear.
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Problem-Solving Techniques
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Understanding problem statements.
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Steps in problem-solving.
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Choosing the right data structure.
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2
Arrays and Strings
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Arrays
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Definition and properties.
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Operations: insertion, deletion, traversal, and searching.
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Example problems: finding the maximum/minimum, reverse an array, etc.
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Strings
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Definition and operations.
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Common problems: palindrome check, anagram check, string reversal, etc.
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3
Linked Lists
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Singly Linked List
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Definition and properties.
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Operations: insertion, deletion, traversal.
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Example problems: reversing a linked list, detecting cycles.
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Doubly Linked List
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Definition and operations.
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Example problems: finding the middle element, deletion of nodes.
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4
Stacks
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Definition and Properties
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LIFO (Last In, First Out) principle.
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Operations: push, pop, peek.
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Applications and Problems
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Balancing parentheses.
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Infix to postfix conversion.
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Evaluating postfix expressions.
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5
Queues
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Definition and Properties
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FIFO (First In, First Out) principle.
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Operations: enqueue, dequeue, front.
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Types of Queues
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Circular queue, priority queue.
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Example problems: implementing a queue using stacks, scheduling algorithms.
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6
Trees
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Binary Trees
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Definition and properties.
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Traversals: inorder, preorder, postorder.
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Example problems: height of a tree, level order traversal.
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Binary Search Trees (BST)
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Definition and properties.
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Operations: insertion, deletion, searching.
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Example problems: finding the kth smallest element, validating a BST.
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Heaps
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Definition and types: min-heap, max-heap.
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Operations: insertion, deletion.
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Example problems: heap sort, finding the k largest elements.
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7
Graphs
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Definition and Properties
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Graph representations: adjacency matrix, adjacency list.
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Types: directed, undirected, weighted, unweighted.
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Traversal Algorithms
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Breadth-First Search (BFS).
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Depth-First Search (DFS).
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Example problems: connected components, shortest path (Dijkstra’s algorithm).
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8
Hashing
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Definition and Applications
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Hash functions and hash tables.
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Collision resolution techniques: chaining, open addressing.
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Example Problems
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Implementing a hash map.
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Solving problems like finding duplicates, implementing a set.
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9
Advanced Data Structures
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Tries
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Definition and applications.
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Example problems: autocomplete feature, prefix matching.
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Segment Trees
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Definition and applications.
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Example problems: range queries, interval updates.
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10
Problem-Solving Sessions and Project
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Problem-Solving Practice
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Hands-on coding problems using various data structures.
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Platforms: LeetCode, HackerRank, CodeSignal.
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Project Work
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Implement a complex problem using multiple data structures.
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Presentation and discussion of the project.
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