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Data Structure and Algorithms
IntermediateFree LearningSoftware Engineering

Data Structure and Algorithms

by UptoSkills Team

Missions

6

Quests

37

Games

12

XP

100

Coins

50

Free Learning

Certificate optional — ₹499, or ₹199 on Pro

Self-paced
Certificate

Key Benefits

Earn XP & Level Up

Gain 100 XP to unlock badges and achievements

Collect Coins

Earn 50 coins to redeem rewards

Professional Certificate

QR-verified and recruiter-checkable — ₹499, or ₹199 on Pro

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About This League

Efficient problem-solving and optimized code are crucial for high-impact software development roles. This comprehensive league meticulously details fundamental and advanced data structures, alongside essential algorithm design techniques. Mastering these concepts will significantly boost your coding proficiency and problem-solving capabilities.

What you will learn

Upon completion, you will be able to:

  • Implement and analyze arrays, linked lists, stacks, and queues for dynamic data management.
  • Design and traverse binary trees, BSTs, AVL trees, and heaps for efficient data organization.
  • Apply hashing techniques and collision resolution strategies for rapid lookups in hash tables.
  • Represent and traverse graphs using adjacency matrices and lists, including BFS and DFS.
  • Analyze algorithm complexity with Big O notation and apply divide and conquer, greedy, and dynamic programming.
  • Utilize advanced tree structures like Red-Black trees and Tries, and implement graph algorithms like Dijkstra's.

What this league covers

ModuleWhat it covers
Foundation of Data StructuresLearn about arrays, linked lists, stacks, and queues, building foundational data management skills.
Trees and HierarchiesExplore binary trees, BSTs, AVL trees, B-trees, and heaps for efficient hierarchical data representation.
Hashing and Efficient LookupsUnderstand hashing, hash functions, and collision resolution techniques for fast data retrieval.
Graph FundamentalsGrasp graph representations, BFS, DFS, and topological sorting for network and relationship analysis.
Algorithm Analysis and DesignMaster Big O notation, divide and conquer, greedy algorithms, and dynamic programming for optimal solutions.
Advanced Topics and ApplicationsDelve into Red-Black trees, Tries, Union-Find, Dijkstra's, Kruskal's, Prim's, and string matching algorithms.

Further your expertise with Debugging and Code Optimization.

A look inside

Data Structure and Algorithms screen 1

Prerequisites

  • Proficient in at least one language — Python, Java, C++ or similar.
  • Understand variables, loops, conditionals and functions in some language.
  • Comfortable with recursion and basic algebra.

Learning Objectives

  • Implement common data structures such as linked lists, stacks, queues, trees (binary search trees, AVL trees), and hash tables in a chosen programming language, demonstrating proficiency in their core operations.
  • Analyze the time and space complexity of various sorting algorithms (e.g., bubble sort, insertion sort, merge sort, quicksort) and searching algorithms (e.g., linear search, binary search) using Big O notation, justifying the efficiency of chosen approaches.
  • Design and apply appropriate data structures and algorithms to solve practical software engineering problems, such as efficient data retrieval, graph traversal for navigation systems, or resource management in operating systems.
  • Evaluate the trade-offs between different data structure and algorithm choices for a given problem, considering factors like performance, memory usage, and ease of implementation.
  • Utilize graph traversal algorithms (e.g., Breadth-First Search, Depth-First Search) to solve real-world problems such as finding shortest paths in maps or detecting cycles in dependencies.
  • Develop and test algorithms for common problem-solving paradigms, including greedy algorithms, dynamic programming, and divide and conquer, and explain their application in scenarios like optimization or pattern recognition.