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Codepro 2898

Land your dream tech job in 6 weeks! Our intensive programming bootcamp equips you with in-demand skills and job-search strategies.

Type:Live Class

Duration:6 Weeks

Start:10 August

Why do you need to master coding ?

Jobs Available
% of final year students realize they could have started studying coding early.
Billion $ Year on year growth of the industry
% of students fail in the first level of interviews

Why do you need to master coding ?

Course Snapshot

Salary Hike
+ Live Sessions
% Offline
  • Dedicate just 98 minutes each day to elevate your coding proficiency.
  • Follow a structured curriculum designed to maximize learning in a short period.

 

  • Gain hands-on experience by tackling over 100 problems on LeetCode.
  • Learn to approach problems methodically and develop efficient solutions.

 

  • Deep dive into fundamental concepts essential for any coder.
  • Understand how to implement and apply various data structures and algorithms effectively.

 

  • Whether you code in C++, Java, Python, or any other language, this program is designed for you.
  • Focus on problem-solving and algorithmic thinking, applicable across all programming languages.

 

  • Prepare to excel on leading coding platforms like HackerRank, LeetCode, and HackerEarth.
  • Build a solid foundation to solve problems across different competitive coding sites.

 

  • Equip yourself with the skills needed to ace coding interviews and exams.
  • Gain the confidence to tackle challenges posed by top tech companies.

 

CODEPRO 2898

Master coding and ace all coding exams.
Try 1 Week Free!

Why do you need to master coding ?

Curriculum snapshot

Salary Hike
+ Live Sessions
% Offline
  • Mathematical Concepts:
  • Fundamental arithmetic and number theory.
  • Mathematical reasoning and problem-solving techniques.
  • Prime numbers, GCD, and LCM.
  • Arrays:
  • Introduction to arrays and basic operations.
  • Array traversal, searching, and sorting.
  • Multi-dimensional arrays and matrix operations.
  • Strings:
  • Basic string operations and manipulations.
  • Pattern matching algorithms.
  • String sorting and transformation.

 

    • Linked Lists:
    • Singly and doubly linked lists.
    • Operations: insertion, deletion, and traversal.
    • Circular linked lists and their applications.
    • Stacks:
    • Stack implementation using arrays and linked lists.
    • Common stack operations: push, pop, peek.
    • Applications of stacks in problem-solving.
    • Queues:
    • Queue implementation using arrays and linked lists.
    • Types of queues: simple, circular, and priority queues.
    • Common queue operations: enqueue, dequeue, front, rear.
  • Binary Trees:
  • Tree traversal techniques: in-order, pre-order, post-order.
  • Binary search trees (BST) and their properties.
  • AVL trees and balancing techniques.
  • Advanced Trees:
  • Heap data structures: min-heap and max-heap.
  • Trie and its applications in string processing.
  • Segment trees and interval trees for range queries.

 

  • Graph Fundamentals:
  • Graph representations: adjacency matrix and list.
  • Types of graphs: directed, undirected, weighted, and unweighted.
  • Graph traversal algorithms: BFS and DFS.
  • Advanced Graph Algorithms:
  • Shortest path algorithms: Dijkstra’s and Floyd-Warshall.
  • Minimum spanning tree algorithms: Kruskal’s and Prim’s.
  • Network flow and bipartite matching.

 

  • Recursion:
  • Understanding recursion and its applications.
  • Solving problems using recursive algorithms.
  • Tail recursion and its optimization.
  • Greedy Algorithms:
  • Greedy method principles and problem-solving.
  • Classic greedy problems: activity selection, knapsack, and coin change.
  • Comparing greedy algorithms with other approaches.

 

  • Dynamic Programming Basics:
  • Understanding the concept of dynamic programming.
  • Memoization vs. tabulation techniques.
  • Solving problems using dynamic programming.
  • Advanced Dynamic Programming:
  • Classic DP problems: longest common subsequence, knapsack problem, and matrix chain multiplication.
  • State optimization and space complexity reduction.
  • DP on trees and graphs.

 

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