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
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- 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.
Why do you need to master coding ?
Curriculum snapshot
Salary Hike
+ Live Sessions
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- 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.
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- 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.