𝗙𝗥𝗘𝗘 𝗖𝗮𝗿𝗲𝗲𝗿 𝗖𝗮𝗿𝗻𝗶𝘃𝗮𝗹 𝗯𝘆 𝗛𝗖𝗟 𝗚𝗨𝗩𝗜😍

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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

Day 57-58: Documentation and Presentation
- Document your project and prepare a presentation or a detailed report

Day 59-60: Reflection and Future Plan
- Reflect on what you've learned
- Plan your next steps (advanced topics, more projects, etc.)

Best DSA RESOURCES: https://topmate.io/coding/886874

Credits: https://t.iss.one/free4unow_backup

ENJOY LEARNING 👍👍

This repository collects everything you need to use AI and LLM in your projects.

120+ libraries, organized by development stages:

→ Model training, fine-tuning, and evaluation
→ Deploying applications with LLM and RAG
→ Fast and scalable model launch
→ Data extraction, crawlers, and scrapers
→ Creating autonomous LLM agents
→ Prompt optimization and security

Repo: https://github.com/KalyanKS-NLP/llm-engineer-toolkit

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Java vs Python Programming: Quick Comparison ✍

📌 Java Programming
• Strongly typed language
• Object-oriented
• Compiled, runs on JVM

Best fields:
• Backend development
• Enterprise systems
• Android development
• Large-scale applications

Job titles:
• Java Developer
• Backend Engineer
• Software Engineer
• Android Developer

Hiring reality:
• Popular in MNCs and legacy systems
• Used in banking and enterprise apps

India salary range:
• Fresher: 4–7 LPA
• Mid-level: 8–18 LPA

Real tasks:
• Build REST APIs
• Backend services
• Android apps
• Large transaction systems

📌 Python Programming
• Dynamically typed
• Simple syntax
• Interpreted language

Best fields:
• Data Analytics
• Data Science
• Machine Learning
• Automation
• Backend development

Job titles:
• Python Developer
• Data Analyst
• Data Scientist
• ML Engineer

Hiring reality:
• High demand in startups and AI teams
• Preferred for rapid development

India salary range:
• Fresher: 6–10 LPA
• Mid-level: 12–25 LPA

Real tasks:
• Data analysis scripts
• ML models
• Automation tools
• APIs with Django or FastAPI

⚔️ Quick comparison
• Data handling: Java focuses on structured systems, Python handles data and files easily
• Speed: Java runs faster in production, Python runs slower but builds faster
• Learning: Java has steep learning curve, Python is beginner-friendly

🎯 Role-based choice
• Backend Developer: Java for scalability, Python for quick APIs
• Data Analyst: Python preferred, Java rarely used
• Data Scientist: Python mandatory, Java optional
• Android Developer: Java required, Python not used

✅ Best career move
• Start with Python for quick entry
• Add Java for strong backend roles
• Pick based on your target job

Which one do you prefer?
Java 👍
Python ❤️
Both 🙏
None 😮

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Few common problems with lot of resumes:

1. 𝐈𝐫𝐫𝐞𝐥𝐞𝐯𝐚𝐧𝐭 𝐢𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧.
I understand that there are a lot of achievements that we are personally proud of (things like represented school/clg in XYZ competition or school head/class head etc), but not all of them are relevant to technical roles. As a fresher, try to focus more on technical achievements rather than managerial ones.

2. 𝐋𝐚𝐜𝐤 𝐨𝐟 𝐪𝐮𝐚𝐥𝐢𝐭𝐲 𝐩𝐫𝐨𝐣𝐞𝐜𝐭𝐬.
Many resumes have the same common projects, such as:
Creating just the front-end using HTML and CSS and redirecting all the work to an open-source API (e.g., weather prediction and recipe suggestion apps).

Most common projects are: -
Tic-tac-toe game.
Sorting algorithms visualizers.
To-do application.
Movie listing.

The codes for these projects are often copied and pasted from GitHub repositories.

Projects are like a bounty. If you are prepared well and have quality projects in your resume, you can set the tempo of the interview. It is one of the few questions that you will almost certainly be asked in the interview.

I don't understand why we can spend 2 years preparing for data structures and algorithms (DSA) and competitive programming (CP), but not even 2 weeks to create quality projects.
Even if your resume passes the applicant tracking system (ATS) and recruiter's screening, weak projects can still lead to your rejection in interviews. And this is completely in your hands.

I feel that this topic needs a lot more discussion about the type and quality of projects that one needs. Let me know if you want a dedicated post on this.

3. 𝐋𝐚𝐜𝐤 𝐨𝐟 𝐪𝐮𝐚𝐧𝐭𝐢𝐭𝐚𝐭𝐢𝐯𝐞 𝐝𝐚𝐭𝐚.
For technical roles, adding quantitative data has a big impact.
For example, instead of saying "I wrote unit tests for service X and reduced the latency of service Y by caching," you can say "I wrote unit tests and increased the code coverage from 80% to 95% of service X and reduced latency from 100 milliseconds to 50 milliseconds of service Y."

🧩 Core Computer Science Concepts

🧠 Big-O Notation
🗂️ Data Structures
🔁 Recursion
🧵 Concurrency vs Parallelism
📦 Memory Management
🔒 Race Conditions
🌐 Networking Basics
⚙️ Operating Systems
🧪 Testing Strategies
📐 System Design

React ❤️ for more like this