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Data Structures and Algorithms

The Data Structures and Algorithms course establishes a strong basis for mastering efficient data organization and algorithm design.

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Certificate :

After Completion

Start Date :

10-Jan-2025

Duration :

30 Days

Course fee :

$150

COURSE DESCRIPTION:

  1. The Data Structures and Algorithms course establishes a strong basis for mastering efficient data organization and algorithm design.

  2. It is designed for students and professionals aiming to excel in technical interviews, competitive programming, or improving software development problem-solving abilities.

  3. Key topics include arrays, linked lists, trees, graphs, sorting, searching, dynamic programming, and advanced algorithmic strategies.

  4. Participants will gain practical skills applicable in real-world scenarios.

  5. The course emphasizes both theoretical understanding and hands-on implementation.

CERTIFICATION:

  1. Participants are awarded a Certificate of Completion by the training provider.
  2. Equips learners for technical interviews with top technology firms.

LEARNING OUTCOMES:

By the conclusion of the course, participants will possess the skills to:

  1. Grasp and apply essential data structures including arrays, stacks, queues, and linked lists.

  2. Create effective algorithms for sorting, searching, and data traversal.

  3. Address practical challenges utilizing dynamic programming and greedy techniques.
    – Evaluate algorithm efficiency through Big-O notation analysis.

  4. Investigate complex topics such as graphs, trees, and hashmaps.

  5. Equip yourself for coding interviews with assuredness and structured problem-solving methods.

  6. Master the implementation of basic data structures.

  7. Design algorithms that optimize data handling processes.

  8. Tackle real-life scenarios with advanced algorithmic strategies.

  9. Strengthen your analytical skills for algorithm performance assessment.

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Course Curriculum

Introduction to Data Structures and Algorithms
  1. Importance of data structures and algorithms
  2. Big-O notation and complexity analysis
  3. Overview of algorithmic problem-solving
Linear Data Structures
  1. Arrays and their operations
  2. Linked Lists: singly, doubly, and circular
  3. Stacks and Queues: implementation and use cases
Non-Linear Data Structures
  1. Trees: binary trees, binary search trees, AVL trees
  2. Graphs: adjacency matrix, adjacency list, and graph traversals (BFS, DFS)
  3. Heaps and priority queues
Searching and Sorting Algorithms
  1. Linear and binary search
  2. Sorting techniques: bubble, selection, insertion, merge, quick, and heap sort
  3. Algorithm complexity comparison
Advanced Algorithms
  1. Dynamic programming: concepts and examples (e.g., knapsack, LCS)
  2. Greedy algorithms: principles and applications (e.g., Kruskal’s and Prim’s)
  3. Backtracking and recursion
Hashing and String Algorithms
  1. Hash tables and hashmaps
  2. String manipulation and pattern matching (e.g., KMP, Rabin-Karp)
  3. Anagrams and palindromes
Advanced Topics
  1. Graph algorithms: shortest path (Dijkstra’s, Bellman-Ford), minimum spanning tree
  2. Divide and conquer techniques
  3. Advanced tree algorithms: segment and Fenwick trees

Training Features

Mock Technical Interviews

Simulate real-world coding interviews with expert feedback.

Interactive Tutorials

Step-by-step explanations with visual aids for better understanding.

Comprehensive Learning Resources

Access to cheat sheets, algorithm templates, and code libraries.

Community and Peer Learning

Collaborate with peers and engage in coding competitions.

Expert Mentorship

Guidance from industry professionals and problem-solving tips.

Real-World Projects and Case Studies

Apply data structures and algorithms to solve practical problems like route optimization, recommendation systems, and data analysis.

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