Useful Run Commands Every Windows User Should Know

Press Win + R on your ⌨️ to open the Run dialog box and enter any of 👇 commands to access the respective tool.

🔹 "." -  the user's folder.
🔹 ".." - user folder.
🔹 "control" - control panel.
🔹 "msconfig" - system configuration parameters.
🔹 "appwiz.cpl" - programs and components.
🔹 "cleanmgr" - a disk cleaning utility.
🔹 "resmon" - resource monitor.
🔹 "calc", "notepad", "mspaint" - calculator, notepad and paint.
🔹 "main.cpl" - mouse parameters.
🔹 "mstsc" - remote desktop.
🔹 "msinfo32" - system information.
🔹 wab  - Contacts.
🔹 dccw - Display Color Calibration.
🔹 desk.cpl - Display Settings.

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Here's a short roadmap to crack an IT job with a non-CS background 🚀

1. 📚 Learn basics of CS and programming.
2. 🎯 Choose a specialization (e.g., web dev, data analysis).
3. 🏆 Complete online courses and certifications.
4. 🛠️ Build a portfolio of projects.
5. 🤝 Network with professionals.
6. 💼 Seek internships for experience.
7. 📚 Keep learning and stay updated.
8. 🧠 Develop soft skills.
9. 📝 Prepare for interviews.
10. 💪 Stay persistent and positive! Good luck!


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Interview Coding Patterns

We'll cover multiple patterns asked in coding interviews. These patterns help you recognize how to approach different types of coding problems smartly — instead of solving each from scratch.

We’ll divide this into multiple parts — starting from basic and gradually going toward advanced.

Here's what we’ll cover in this section:

1. Stock Buy & Sell (Single Transaction)
2. Stock Buy & Sell (Multiple Transactions)
3. Kadane’s Algorithm (Max Subarray)
4. Sliding Window (Fixed + Variable Size)
5. Two Pointer Technique
6. Prefix Sum
7. HashMap-Based Pattern
8. Binary Search Variants
9. Backtracking Basics
10. Recursion to DP Conversion
11. Sorting-Based Tricks
12. Greedy Patterns
13. Frequency Maps and Counters
14. Stacks and Queues Based Patterns
15. Substring & Subarray Techniques

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Coding Resources 👆

30-days learning plan to master Data Structures and Algorithms (DSA) and prepare for coding interviews.

### Week 1: Foundations and Basic Data Structures

Day 1-3: Arrays and Strings
- Topics to Cover:
- Array basics, operations (insertion, deletion, searching)
- String manipulation
- Two-pointer technique, sliding window technique
- Practice Problems:
- Two Sum
- Maximum Subarray
- Reverse a String
- Longest Substring Without Repeating Characters

Day 4-5: Linked Lists
- Topics to Cover:
- Singly linked list, doubly linked list, circular linked list
- Common operations (insertion, deletion, reversal)
- Practice Problems:
- Reverse a Linked List
- Merge Two Sorted Lists
- Remove Nth Node From End of List

Day 6-7: Stacks and Queues
- Topics to Cover:
- Stack operations (push, pop, top)
- Queue operations (enqueue, dequeue)
- Applications (expression evaluation, backtracking, breadth-first search)
- Practice Problems:
- Valid Parentheses
- Implement Stack using Queues
- Implement Queue using Stacks

### Week 2: Advanced Data Structures

Day 8-10: Trees
- Topics to Cover:
- Binary Trees, Binary Search Trees (BST)
- Tree traversal (preorder, inorder, postorder, level order)
- Practice Problems:
- Invert Binary Tree
- Validate Binary Search Tree
- Serialize and Deserialize Binary Tree

Day 11-13: Heaps and Priority Queues
- Topics to Cover:
- Binary heap (min-heap, max-heap)
- Heap operations (insert, delete, extract-min/max)
- Applications (heap sort, priority queues)
- Practice Problems:
- Kth Largest Element in an Array
- Top K Frequent Elements
- Find Median from Data Stream

Day 14: Hash Tables
- Topics to Cover:
- Hashing concept, hash functions, collision resolution (chaining, open addressing)
- Applications (caching, counting frequencies)
- Practice Problems:
- Two Sum (using hash map)
- Group Anagrams
- Subarray Sum Equals K

### Week 3: Algorithms

Day 15-17: Sorting and Searching Algorithms
- Topics to Cover:
- Sorting algorithms (quick sort, merge sort, bubble sort, insertion sort)
- Searching algorithms (binary search, linear search)
- Practice Problems:
- Merge Intervals
- Search in Rotated Sorted Array
- Sort Colors
- Find Peak Element

Day 18-20: Recursion and Backtracking
- Topics to Cover:
- Basic recursion, tail recursion
- Backtracking (N-Queens, Sudoku solver)
- Practice Problems:
- Permutations
- Combination Sum
- Subsets
- Word Search

Day 21: Divide and Conquer
- Topics to Cover:
- Basic concept, merge sort, quick sort, binary search
- Practice Problems:
- Median of Two Sorted Arrays
- Pow(x, n)
- Kth Largest Element in an Array (using divide and conquer)
- Maximum Subarray (using divide and conquer)

### Week 4: Graphs and Dynamic Programming

Day 22-24: Graphs
- Topics to Cover:
- Graph representations (adjacency list, adjacency matrix)
- Traversal algorithms (DFS, BFS)
- Shortest path algorithms (Dijkstra's, Bellman-Ford)
- Practice Problems:
- Number of Islands

Day 25-27: Dynamic Programming
- Topics to Cover:
- Basic concept, memoization, tabulation
- Common problems (knapsack, longest common subsequence)
- Practice Problems:
- Longest Increasing Subsequence
- Maximum Product Subarray

Day 28: Advanced Topics and Miscellaneous
- Topics to Cover:
- Bit manipulation
- Greedy algorithms
- Miscellaneous problems (trie, segment tree, disjoint set)
- Practice Problems:
- Single Number
- Decode Ways
- Minimum Spanning Tree

### Week 5: Review and Mock Interviews

Day 29: Review and Weakness Analysis
- Activities:
  - Review topics you found difficult
  - Revisit problems you struggled with

Day 30: Mock Interviews and Practice
- Activities:
  - Conduct mock interviews with a friend or use online platforms
  - Focus on communication and explaining your thought process

Top DSA resources to crack coding interview

👉 GeekforGeeks

👉 Leetcode

👉 DSA Steps

👉 FreeCodeCamp

👉 Coding Interviews

👉 Best DSA Resources

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Top 4 Python Projects for Beginners

1. To-Do List App: Create a simple to-do list application where users can add, edit, and delete tasks. This project will help you learn about basic data handling and user interface design.

2. Weather App: Build a weather application that allows users to enter a location and see the current weather conditions. This project will introduce you to working with APIs and handling JSON data.

3. Web Scraper: Develop a web scraper that extracts information from a website and saves it to a file or database. This project will teach you about web scraping techniques and data manipulation.

4. Quiz Game: Create a quiz game where users can answer multiple-choice questions and receive a score at the end. This project will help you practice working with functions, loops, and conditional statements in Python.

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

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ENJOY LEARNING 👍👍

Guys, Big Announcement!

We’ve officially hit 5 Lakh followers on WhatsApp and it’s time to level up together! ❤️

I've launched a Python Learning Series — designed for beginners to those preparing for technical interviews or building real-world projects.

This will be a step-by-step journey — from basics to advanced — with real examples and short quizzes after each topic to help you lock in the concepts.

Here’s what we’ll cover in the coming days:

Week 1: Python Fundamentals

- Variables & Data Types

- Operators & Expressions

- Conditional Statements (if, elif, else)

- Loops (for, while)

- Functions & Parameters

- Input/Output & Basic Formatting


Week 2: Core Python Skills

- Lists, Tuples, Sets, Dictionaries

- String Manipulation

- List Comprehensions

- File Handling

- Exception Handling


Week 3: Intermediate Python

- Lambda Functions

- Map, Filter, Reduce

- Modules & Packages

- Scope & Global Variables

- Working with Dates & Time


Week 4: OOP & Pythonic Concepts

- Classes & Objects

- Inheritance & Polymorphism

- Decorators (Intro level)

- Generators & Iterators

- Writing Clean & Readable Code


Week 5: Real-World & Interview Prep

- Web Scraping (BeautifulSoup)

- Working with APIs (Requests)

- Automating Tasks

- Data Analysis Basics (Pandas)

- Interview Coding Patterns

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⌨️ Built-in Functions in JavaScript!!

JavaScript includes a variety of built-in functions that allow developers to perform common operations on data, manipulate strings and arrays, and work with dates and times. Here are some examples of built-in functions in JavaScript.

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#coding

A-Z of essential data science concepts

A: Algorithm - A set of rules or instructions for solving a problem or completing a task.
B: Big Data - Large and complex datasets that traditional data processing applications are unable to handle efficiently.
C: Classification - A type of machine learning task that involves assigning labels to instances based on their characteristics.
D: Data Mining - The process of discovering patterns and extracting useful information from large datasets.
E: Ensemble Learning - A machine learning technique that combines multiple models to improve predictive performance.
F: Feature Engineering - The process of selecting, extracting, and transforming features from raw data to improve model performance.
G: Gradient Descent - An optimization algorithm used to minimize the error of a model by adjusting its parameters iteratively.
H: Hypothesis Testing - A statistical method used to make inferences about a population based on sample data.
I: Imputation - The process of replacing missing values in a dataset with estimated values.
J: Joint Probability - The probability of the intersection of two or more events occurring simultaneously.
K: K-Means Clustering - A popular unsupervised machine learning algorithm used for clustering data points into groups.
L: Logistic Regression - A statistical model used for binary classification tasks.
M: Machine Learning - A subset of artificial intelligence that enables systems to learn from data and improve performance over time.
N: Neural Network - A computer system inspired by the structure of the human brain, used for various machine learning tasks.
O: Outlier Detection - The process of identifying observations in a dataset that significantly deviate from the rest of the data points.
P: Precision and Recall - Evaluation metrics used to assess the performance of classification models.
Q: Quantitative Analysis - The process of using mathematical and statistical methods to analyze and interpret data.
R: Regression Analysis - A statistical technique used to model the relationship between a dependent variable and one or more independent variables.
S: Support Vector Machine - A supervised machine learning algorithm used for classification and regression tasks.
T: Time Series Analysis - The study of data collected over time to detect patterns, trends, and seasonal variations.
U: Unsupervised Learning - Machine learning techniques used to identify patterns and relationships in data without labeled outcomes.
V: Validation - The process of assessing the performance and generalization of a machine learning model using independent datasets.
W: Weka - A popular open-source software tool used for data mining and machine learning tasks.
X: XGBoost - An optimized implementation of gradient boosting that is widely used for classification and regression tasks.
Y: Yarn - A resource manager used in Apache Hadoop for managing resources across distributed clusters.
Z: Zero-Inflated Model - A statistical model used to analyze data with excess zeros, commonly found in count data.

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Lists 🆚 Tuples 🆚 Dictionaries

What's the difference?

Lists are mutable.
Tuples are immutable.
Dictionaries are associative.

When should you use each?

Lists:
⟶ When you want to add or remove elements
⟶ When you want to sort elements
⟶ When you want to slice elements

Tuples:
⟶ When you want a constant object
⟶ When you want to send multiple in a function
⟶ When you want to return multiple from a function

Dictionaries:
⟶ When you want to map keys to values
⟶ When you want to loop over the keys
⟶ When you want to validate if key exists

Now, pick your weapon of mass data analysis and become a Python pro!

Python Interview Q&A: https://topmate.io/coding/898340

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ENJOY LEARNING 👍👍

Here's the A–Z list of essential Python programming concepts

A - Arguments
B - Built-in Functions
C - Comprehensions
D - Dictionaries
E - Exceptions
F - Functions
G - Generators
H - Higher-Order Functions
I - Iterators
J - Join Method
K - Keyword Arguments
L - Lambda Functions
M - Modules
N - NoneType
O - Object-Oriented Programming
P - PEP8
Q - Queue
R - Range Function
S - Sets
T - Tuples
U - Unpacking
V - Variables
W - While Loop
X - XOR Operation
Y - Yield Keyword
Z - Zip Function

These concepts are foundational to mastering Python and writing clean, efficient, and Pythonic code.

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5 Easy Projects to Build as a Beginner

(No AI degree needed. Just curiosity & coffee.)

❯ 1. Calculator App
 • Learn logic building
 • Try it in Python, JavaScript or C++
 • Bonus: Add GUI using Tkinter or HTML/CSS

❯ 2. Quiz App (with Score Tracker)
 • Build a fun MCQ quiz
 • Use basic conditions, loops, and arrays
 • Add a timer for extra challenge!

❯ 3. Rock, Paper, Scissors Game
 • Classic game using random choice
 • Great to practice conditions and user input
 • Optional: Add a scoreboard

❯ 4. Currency Converter
 • Convert from USD to INR, EUR, etc.
 • Use basic math or try fetching live rates via API
 • Build a mini web app for it!

❯ 5. To-Do List App
 • Create, read, update, delete tasks
 • Perfect for learning arrays and functions
 • Bonus: Add local storage (in JS) or file saving (in Python)


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Here is an A-Z list of essential programming terms:

1. Array: A data structure that stores a collection of elements of the same type in contiguous memory locations.

2. Boolean: A data type that represents true or false values.

3. Conditional Statement: A statement that executes different code based on a condition.

4. Debugging: The process of identifying and fixing errors or bugs in a program.

5. Exception: An event that occurs during the execution of a program that disrupts the normal flow of instructions.

6. Function: A block of code that performs a specific task and can be called multiple times in a program.

7. GUI (Graphical User Interface): A visual way for users to interact with a computer program using graphical elements like windows, buttons, and menus.

8. HTML (Hypertext Markup Language): The standard markup language used to create web pages.

9. Integer: A data type that represents whole numbers without any fractional part.

10. JSON (JavaScript Object Notation): A lightweight data interchange format commonly used for transmitting data between a server and a web application.

11. Loop: A programming construct that allows repeating a block of code multiple times.

12. Method: A function that is associated with an object in object-oriented programming.

13. Null: A special value that represents the absence of a value.

14. Object-Oriented Programming (OOP): A programming paradigm based on the concept of "objects" that encapsulate data and behavior.

15. Pointer: A variable that stores the memory address of another variable.

16. Queue: A data structure that follows the First-In-First-Out (FIFO) principle.

17. Recursion: A programming technique where a function calls itself to solve a problem.

18. String: A data type that represents a sequence of characters.

19. Tuple: An ordered collection of elements, similar to an array but immutable.

20. Variable: A named storage location in memory that holds a value.

21. While Loop: A loop that repeatedly executes a block of code as long as a specified condition is true.

Best Programming Resources: https://topmate.io/coding/898340

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