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Data Eminence is hiring Full Stack Engineer
For 2024, 2025 grads
Location: Bangalore

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Accenture is hiring Web Developer Associate
For 2023, 2024, 2025 grads
Location: Mumbai

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Infosys hiring .Net Dev

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Siemens Energy
Position: AI/Machine Learning Engineer
Qualifications: Bachelor’s Degree
Experience: Freshers
Location: Gurgaon, India

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MakeMyTrip is hiring for Project Based Internship (Marketing)
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JB Poindexter (India) Private Limited is hiring for Jr Pricing Analyst
Experience: 0-1 years
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American Express is hiring for Software Engineer I
Experience: 0-2 years
Salary: 15-25 LPA

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Gainwell Technologies
Position: C Unix Developer
Qualification: Bachelor’s Degree
Experience: Experienced
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𝐆𝐄 𝐇𝐞𝐚𝐥𝐭𝐡𝐂𝐚𝐫𝐞
Position: Graduate Engineer Trainee
Qualifications: Bachelor’s Degree
Experience: Freshers
Location: Pune, India

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Platform Science is hiring for Data Engineer
Batch: 2023, 2024, 2025

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Priceline is hiring for Associate Software Engineer
Batch: 2024, 2025

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Artpark is hiring for Data quality check executive (Freelance)
Batch: 2023, 2024, 2025, 2026

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Octave is hiring for Software Engineer
Batch: 2021, 2022, 2023, 2024

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American Express is hiring for Software Engineer I
Batch: 2024, 2025, 2026

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ACI Worldwide is hiring for Software Engineer
Batch: 2023, 2024, 2025

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fmg is hiring Software Engineer Intern (PHP & Java)
For 2026, 2027 grads
Location: Bangalore

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carelon care solutions is hiring Associate Software Engineer
For 2024, 2025 grads
Location: Multiple

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BJAK is hiring Software Engineer (Backend)
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💰 Greedy Algorithm Mindset

📖 Core Idea: Greedy makes the locally best choice at every step, hoping these choices lead to a global optimum. It works well when the problem has optimal substructure and a greedy choice property that you can prove or justify.

🗯 Real Interview Scenario: “Jump Game”, “Minimum Number of Arrows to Burst Balloons”, or interval scheduling.

✅ How to shine: Share your greedy intuition first, then briefly prove why it works (e.g., “Sorting by end time guarantees we fit maximum activities”). Compare with DP when asked. This shows deeper problem-solving maturity.



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Complete roadmap to learn Python and Data Structures & Algorithms (DSA) in 2 months

### Week 1: Introduction to Python

Day 1-2: Basics of Python
- Python setup (installation and IDE setup)
- Basic syntax, variables, and data types
- Operators and expressions

Day 3-4: Control Structures
- Conditional statements (if, elif, else)
- Loops (for, while)

Day 5-6: Functions and Modules
- Function definitions, parameters, and return values
- Built-in functions and importing modules

Day 7: Practice Day
- Solve basic problems on platforms like HackerRank or LeetCode

### Week 2: Advanced Python Concepts

Day 8-9: Data Structures in Python
- Lists, tuples, sets, and dictionaries
- List comprehensions and generator expressions

Day 10-11: Strings and File I/O
- String manipulation and methods
- Reading from and writing to files

Day 12-13: Object-Oriented Programming (OOP)
- Classes and objects
- Inheritance, polymorphism, encapsulation

Day 14: Practice Day
- Solve intermediate problems on coding platforms

### Week 3: Introduction to Data Structures

Day 15-16: Arrays and Linked Lists
- Understanding arrays and their operations
- Singly and doubly linked lists

Day 17-18: Stacks and Queues
- Implementation and applications of stacks
- Implementation and applications of queues

Day 19-20: Recursion
- Basics of recursion and solving problems using recursion
- Recursive vs iterative solutions

Day 21: Practice Day
- Solve problems related to arrays, linked lists, stacks, and queues

### Week 4: Fundamental Algorithms

Day 22-23: Sorting Algorithms
- Bubble sort, selection sort, insertion sort
- Merge sort and quicksort

Day 24-25: Searching Algorithms
- Linear search and binary search
- Applications and complexity analysis

Day 26-27: Hashing
- Hash tables and hash functions
- Collision resolution techniques

Day 28: Practice Day
- Solve problems on sorting, searching, and hashing

### Week 5: Advanced Data Structures

Day 29-30: Trees
- Binary trees, binary search trees (BST)
- Tree traversals (in-order, pre-order, post-order)

Day 31-32: Heaps and Priority Queues
- Understanding heaps (min-heap, max-heap)
- Implementing priority queues using heaps

Day 33-34: Graphs
- Representation of graphs (adjacency matrix, adjacency list)
- Depth-first search (DFS) and breadth-first search (BFS)

Day 35: Practice Day
- Solve problems on trees, heaps, and graphs

### Week 6: Advanced Algorithms

Day 36-37: Dynamic Programming
- Introduction to dynamic programming
- Solving common DP problems (e.g., Fibonacci, knapsack)

Day 38-39: Greedy Algorithms
- Understanding greedy strategy
- Solving problems using greedy algorithms

Day 40-41: Graph Algorithms
- Dijkstra’s algorithm for shortest path
- Kruskal’s and Prim’s algorithms for minimum spanning tree

Day 42: Practice Day
- Solve problems on dynamic programming, greedy algorithms, and advanced graph algorithms

### Week 7: Problem Solving and Optimization

Day 43-44: Problem-Solving Techniques
- Backtracking, bit manipulation, and combinatorial problems

Day 45-46: Practice Competitive Programming
- Participate in contests on platforms like Codeforces or CodeChef

Day 47-48: Mock Interviews and Coding Challenges
- Simulate technical interviews
- Focus on time management and optimization

Day 49: Review and Revise
- Go through notes and previously solved problems
- Identify weak areas and work on them

### Week 8: Final Stretch and Project

Day 50-52: Build a Project
- Use your knowledge to build a substantial project in Python involving DSA concepts

Day 53-54: Code Review and Testing
- Refactor your project code
- Write tests for your project

Day 55-56: Final Practice
- Solve problems from previous contests or new challenging problems



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