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C++ Programming Roadmap | |-- Fundamentals | |-- Basics of Programming | | |-- Introduction to C++ | | |-- Setting Up Development Environment (IDE: Code::Blocks, Visual Studio, etc.) | | |-- Compiling and Running C++ Programs | | | |-- Syntax and Structure | | |-- Basic Syntax | | |-- Variables and Data Types | | |-- Operators (Arithmetic, Relational, Logical, Bitwise) | |-- Control Structures | |-- Conditional Statements | | |-- If-Else Statements | | |-- Switch Case | | | |-- Loops | | |-- For Loop | | |-- While Loop | | |-- Do-While Loop | | | |-- Jump Statements | | |-- Break, Continue | | |-- Goto Statement | |-- Functions and Scope | |-- Defining Functions | | |-- Function Syntax | | |-- Parameters and Arguments (Pass by Value, Pass by Reference) | | |-- Return Statement | | | |-- Function Overloading | | |-- Overloading Functions with Different Parameters | | | |-- Scope and Lifetime | | |-- Local and Global Scope | | |-- Static Variables | |-- Object-Oriented Programming (OOP) | |-- Basics of OOP | | |-- Classes and Objects | | |-- Member Functions and Data Members | | | |-- Constructors and Destructors | | |-- Constructor Types (Default, Parameterized, Copy) | | |-- Destructor Basics | | | |-- Inheritance | | |-- Single and Multiple Inheritance | | |-- Protected Access Specifier | | |-- Virtual Base Class | | | |-- Polymorphism | | |-- Function Overriding | | |-- Virtual Functions and Pure Virtual Functions | | |-- Abstract Classes | | | |-- Encapsulation and Abstraction | | |-- Access Specifiers (Public, Private, Protected) | | |-- Getters and Setters | | | |-- Operator Overloading | | |-- Overloading Operators (Arithmetic, Relational, etc.) | | |-- Friend Functions | |-- Advanced C++ | |-- Pointers and Dynamic Memory | | |-- Pointer Basics | | |-- Dynamic Memory Allocation (new, delete) | | |-- Pointer Arithmetic | | | |-- References | | |-- Reference Variables | | |-- Passing by Reference | | | |-- Templates | | |-- Function Templates | | |-- Class Templates | | | |-- Exception Handling | | |-- Try-Catch Blocks | | |-- Throwing Exceptions | | |-- Standard Exceptions | |-- Data Structures | |-- Arrays and Strings | | |-- One-Dimensional and Multi-Dimensional Arrays | | |-- String Handling | | | |-- Linked Lists | | |-- Singly and Doubly Linked Lists | | | |-- Stacks and Queues | | |-- Stack Operations (Push, Pop, Peek) | | |-- Queue Operations (Enqueue, Dequeue) | | | |-- Trees and Graphs | | |-- Binary Trees, Binary Search Trees | | |-- Graph Representation and Traversal (DFS, BFS) | |-- Standard Template Library (STL) | |-- Containers | | |-- Vectors, Lists, Deques | | |-- Stacks, Queues, Priority Queues | | |-- Sets, Maps, Unordered Maps | | | |-- Iterators | | |-- Input and Output Iterators | | |-- Forward, Bidirectional, and Random Access Iterators | | | |-- Algorithms | | |-- Sorting, Searching, and Manipulation | | |-- Numeric Algorithms | |-- File Handling | |-- Streams and File I/O | | |-- ifstream, ofstream, fstream | | |-- Reading and Writing Files | | |-- Binary File Handling | |-- Testing and Debugging | |-- Debugging Tools | | |-- gdb (GNU Debugger) | | |-- Valgrind for Memory Leak Detection | | | |-- Unit Testing | | |-- Google Test (gtest) | | |-- Writing and Running Tests | |-- Deployment and DevOps | |-- Version Control with Git | | |-- Integrating C++ Projects with GitHub | |-- Continuous Integration/Continuous Deployment (CI/CD) | | |-- Using Jenkins or GitHub | | |   |--Free courses | | |--imp.i115008.net/kjoq9V |   |   |--imp.i115008.net/5bmnKL |   |   |--Microsoft Documentation |   |   |--Udemy Course Join @free4unow_backup for more free resources ENJOY LEARNING 👍👍

Must know SQL basics !! ➡ Explain the order of execution of a SQL query. ➡ Provide a use case for RANK, DENSE_RANK, and ROW_NUMBER and explain when to use each. ➡ Write a query to find the cumulative sum (running total) of sales in a table. ➡ How would you write a query to find the most selling product by sales or the highest salary of employees? ➡ Write a SQL query to find the 2nd or Nth highest salary of employees. ➡ What is the difference between UNION and UNION ALL? Provide a use case for both. ➡ How do you write a query to identify duplicates in a table? ➡ Explain INNER, LEFT, and OUTER joins using a scenario and write a query for each. ➡ Write a query using LAG to find records where the transaction value is greater than the previous transaction value. ➡ What is the difference between RANK and DENSE_RANK, and how would you write a query to find the 2nd highest salary while handling ties? Here you can find essential SQL Interview Resources👇 https://topmate.io/analyst/864764 Like this post if you need more 👍❤️ Hope it helps :)

7⃣ SQL functions for data cleaning: 1. TRIM(): Usage: Removes leading and trailing whitespace from a string. Example: SELECT TRIM(column_name) FROM table_name; 2. UPPER() and LOWER(): Usage: Converts a string to uppercase or lowercase, respectively. This is useful for standardizing data. Example: SELECT UPPER(column_name) FROM table_name; SELECT LOWER(column_name) FROM table_name; 3. COALESCE(): Usage: Returns the first non-null value in a list of arguments. It helps to handle null values effectively. Example: SELECT COALESCE(column1, column2, 'default_value') FROM table_name; 4. REPLACE(): Usage: Replaces occurrences of a specified string within another string, which can help in cleaning up data formats. Example: SELECT REPLACE(column_name, 'old_value', 'new_value') FROM table_name; 5. SUBSTRING(): Usage: Extracts a substring from a string based on specified starting position and length, useful for cleaning or formatting data. Example: SELECT SUBSTRING(column_name, start_position, length) FROM table_name; 6. CAST() and CONVERT(): Usage: Converts one data type to another. This is useful for ensuring data consistency across your database. Example: SELECT CAST(column_name AS VARCHAR(255)) FROM table_name; SELECT CONVERT(VARCHAR(255), column_name) FROM table_name; 7. ISNULL(): Usage: Replaces NULL with a specified replacement value. This can help in making reports more readable. Example: SELECT ISNULL(column_name, 'default_value') FROM table_name; Here you can find SQL Interview Resources👇 https://topmate.io/analyst/864764 Hope it helps :)

Anyone looking to learn Pandas? Here’s your step-by-step guide to mastering data analysis.. 🎯 Pandas Checklist for Data Aspirants 🚀 🌱 Getting Started with Pandas 👉 Install Pandas and set up Jupyter Notebook 👉 Understand DataFrames and Series (your new best friends!) 🔍 Load & Explore Data 👉 Import data from files (CSV, Excel, etc.) 👉 Get a quick snapshot of data with head(), info(), and describe() 🧹 Data Cleaning Essentials 👉 Handle missing data with fillna() or dropna() 👉 Remove duplicates and filter data as needed 🔄 Transforming Data 👉 Sort and rank values easily 👉 Use apply() and map() for custom transformations 📊 Summarize with Grouping 👉 Group data by categories with groupby() 👉 Create quick pivot tables for summaries 📅 Master Date & Time Data 👉 Convert and extract date parts (year, month, etc.) 👉 Do time-based analysis easily 📈 Quick Exploratory Analysis 👉 Calculate statistics (mean, median, std dev) 👉 Spot correlations and outliers 📉 Basic Visualizations 👉 Plot data with line, bar, and scatter charts 👉 Customize charts with labels and colors 💪 Advanced Data Handling 👉 Work with MultiIndex for complex data 👉 Reshape data with pivot() and melt() 🚀 Optimize for Performance 👉 Reduce memory usage by adjusting data types 👉 Use vectorized operations for speed 📂 Practice Projects 👉 Apply your skills on real datasets 👉 Build a portfolio with case studies I have curated the best interview resources to crack Python Interviews 👇👇 https://topmate.io/analyst/907371 Hope you'll like it Like this post if you need more resources like this 👍❤️

Iterating over Pandas DataFrames can cost you much performance. Comparing iterrows() and itertuples() can help in some cases: 1. 𝗶𝘁𝗲𝗿𝗿𝗼𝘄𝘀(): Generates index and Series pairs for each row. 𝗣𝗿𝗼𝘀: Easy to use and intuitive. Suitable for small datasets. 𝗖𝗼𝗻𝘀: Slow for large datasets. Series conversion incurs additional overhead. 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲: Quick data inspection and small-scale transformations. 2. 𝗶𝘁𝗲𝗿𝘁𝘂𝗽𝗹𝗲𝘀(): Returns namedtuples of the DataFrame rows. 𝗣𝗿𝗼𝘀: Much faster than iterrows(). More efficient for large datasets. 𝗖𝗼𝗻𝘀: Slightly less intuitive syntax. Avoid using when mutating DataFrames. 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲: Large-scale data processing and read-only operations. For optimal performance, use vectorized operations whenever possible! Iteration methods should be your last resort! I have curated the best interview resources to crack Data Science Interviews 👇👇 https://topmate.io/analyst/1024129 Like if you need similar content 😄👍

Many data scientists don't know how to push ML models to production. Here's the recipe 👇 𝗞𝗲𝘆 𝗜𝗻𝗴𝗿𝗲𝗱𝗶𝗲𝗻𝘁𝘀 🔹 𝗧𝗿𝗮𝗶𝗻 / 𝗧𝗲𝘀𝘁 𝗗𝗮𝘁𝗮𝘀𝗲𝘁 - Ensure Test is representative of Online data 🔹 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 - Generate features in real-time 🔹 𝗠𝗼𝗱𝗲𝗹 𝗢𝗯𝗷𝗲𝗰𝘁 - Trained SkLearn or Tensorflow Model 🔹 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗖𝗼𝗱𝗲 𝗥𝗲𝗽𝗼 - Save model project code to Github 🔹 𝗔𝗣𝗜 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 - Use FastAPI or Flask to build a model API 🔹 𝗗𝗼𝗰𝗸𝗲𝗿 - Containerize the ML model API 🔹 𝗥𝗲𝗺𝗼𝘁𝗲 𝗦𝗲𝗿𝘃𝗲𝗿 - Choose a cloud service; e.g. AWS sagemaker 🔹 𝗨𝗻𝗶𝘁 𝗧𝗲𝘀𝘁𝘀 - Test inputs & outputs of functions and APIs 🔹 𝗠𝗼𝗱𝗲𝗹 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 - Evidently AI, a simple, open-source for ML monitoring 𝗣𝗿𝗼𝗰𝗲𝗱𝘂𝗿𝗲 𝗦𝘁𝗲𝗽 𝟭 - 𝗗𝗮𝘁𝗮 𝗣𝗿𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻 & 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 Don't push a model with 90% accuracy on train set. Do it based on the test set - if and only if, the test set is representative of the online data. Use SkLearn pipeline to chain a series of model preprocessing functions like null handling. 𝗦𝘁𝗲𝗽 𝟮 - 𝗠𝗼𝗱𝗲𝗹 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 Train your model with frameworks like Sklearn or Tensorflow. Push the model code including preprocessing, training and validation scripts to Github for reproducibility. 𝗦𝘁𝗲𝗽 𝟯 - 𝗔𝗣𝗜 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 & 𝗖𝗼𝗻𝘁𝗮𝗶𝗻𝗲𝗿𝗶𝘇𝗮𝘁𝗶𝗼𝗻 Your model needs a "/predict" endpoint, which receives a JSON object in the request input and generates a JSON object with the model score in the response output. You can use frameworks like FastAPI or Flask. Containzerize this API so that it's agnostic to server environment 𝗦𝘁𝗲𝗽 𝟰 - 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 & 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 Write tests to validate inputs & outputs of API functions to prevent errors. Push the code to remote services like AWS Sagemaker. 𝗦𝘁𝗲𝗽 𝟱 - 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 Set up monitoring tools like Evidently AI, or use a built-in one within AWS Sagemaker. I use such tools to track performance metrics and data drifts on online data. I have curated the best interview resources to crack Data Science Interviews 👇👇 https://topmate.io/analyst/1024129 Like if you need similar content 😄👍

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. Data Science Interview Resources 👇👇 https://topmate.io/analyst/1024129 Like for more 😄

Top three most required tech stack for the following roles: 1. Data Analyst: SQL, Excel, Tableau/Power BI 2. Data Scientist: Python, R, SQL 3. Quantitative Analyst: Python, R, MATLAB 4. Business Analyst: SQL, Business Requirements Gathering, Agile Methodologies, Power BI/Tableau 5. Data Engineer: Python/Scala, SQL, Cloud, Apache Spark 6. Machine Learning Engineer: Python, TensorFlow/PyTorch, Docker/Kubernetes.

Essential Python Libraries to build your career in Data Science 📊👇 1. NumPy: - Efficient numerical operations and array manipulation. 2. Pandas: - Data manipulation and analysis with powerful data structures (DataFrame, Series). 3. Matplotlib: - 2D plotting library for creating visualizations. 4. Seaborn: - Statistical data visualization built on top of Matplotlib. 5. Scikit-learn: - Machine learning toolkit for classification, regression, clustering, etc. 6. TensorFlow: - Open-source machine learning framework for building and deploying ML models. 7. PyTorch: - Deep learning library, particularly popular for neural network research. 8. SciPy: - Library for scientific and technical computing. 9. Statsmodels: - Statistical modeling and econometrics in Python. 10. NLTK (Natural Language Toolkit): - Tools for working with human language data (text). 11. Gensim: - Topic modeling and document similarity analysis. 12. Keras: - High-level neural networks API, running on top of TensorFlow. 13. Plotly: - Interactive graphing library for making interactive plots. 14. Beautiful Soup: - Web scraping library for pulling data out of HTML and XML files. 15. OpenCV: - Library for computer vision tasks. As a beginner, you can start with Pandas and NumPy for data manipulation and analysis. For data visualization, Matplotlib and Seaborn are great starting points. As you progress, you can explore machine learning with Scikit-learn, TensorFlow, and PyTorch. Free Notes & Books to learn Data Science: https://t.me/datasciencefree Python Project Ideas: https://t.me/dsabooks/85 Best Resources to learn Python & Data Science 👇👇 Python Tutorial Data Science Course by Kaggle Machine Learning Course by Google Best Data Science & Machine Learning Resources Interview Process for Data Science Role at Amazon Python Interview Resources Join @free4unow_backup for more free courses Like for more ❤️ ENJOY LEARNING👍👍

🆒Complete Roadmap to Become Bug Hunter ⁉️ 📈 1. Learn the Basics of Web Security - Study OWASP Top 10: These are the most critical security risks for web applications. Learn about vulnerabilities like XSS, SQLi, Broken Access Control, etc. - Understand HTTP and APIs: Know how HTTP works (requests, responses, status codes, etc.) and get familiar with how APIs are built and secured. - Learn How Web Apps Work: Understand the basic architecture of web applications (frontend, backend, databases). 📈 2. Start Bug Bounty Hunting - Join Platforms: Sign up on platforms like [HackerOne], [Bugcrowd], or [Open Bug Bounty]. - Participate in Programs: Look for beginner-friendly programs or public bug bounty programs with clear scope and guidelines. - Read Reports: Study public write-ups from experienced hunters to see how they approach finding and exploiting vulnerabilities. 📈 3. Hone Your Vulnerability HuTo become a successful bug bounty hunter, focus on a structured learning path and skill-building process. Here’s a simplified roadmap to get you started: - Learn Burp Suite: This is the most common tool for web vulnerability hunting. Start with the free version and learn to use it effectively for manual testing. - Automate with Scripts and Tools: Learn to automate some tasks using tools like ffuf, sqlmap, and nmap to help with enumeration and scanning. - Master Exploitation Techniques : Focus on exploiting bugs like IDOR (Insecure Direct Object Reference), CSRF (Cross-Site Request Forgery), and RCE (Remote Code Execution). 📈4. Practice Continuously - Try Labs and CTFs : Practice on platforms like [TryHackMe] and [Hack The Box]. These provide hands-on challenges to sharpen your skills. - Bug Bounty Platforms: Actively participate in bug bounty platforms and hunt for bugs in real-world applications. 📈 5. Document Your Findings - Write Clear Reports: When you find a bug, ensure your report is clear, concise, and provides steps to reproduce the vulnerability - Share Write-ups: Writing about your findings on platforms like Medium or starting a blog will help you build a reputation in the community. 📈6. Stay Updated - Follow Researchers: Keep up with the latest techniques and tools by following well-known bug bounty hunters on Twitter or subscribing to security blogs. - Experiment with New Tools: New tools and techniques emerge regularly, so stay up-to-date by experimenting with the latest tools in your hunts. 📈 7. Keep Patience and Perseverance - Bug bounty hunting can be competitive and time-consuming. The key to success is persistence and constantly learning from both your failures and successes. This was the Full Guide to Become Bug Hunter 🎉 ☄️ Give 💯+ Reactions

🖥 100 Web Vulnerabilities, categorized into various types : 😀 ⚡️ Injection Vulnerabilities: 1. SQL Injection (SQLi) 2. Cross-Site Scripting (XSS) 3. Cross-Site Request Forgery (CSRF) 4. Remote Code Execution (RCE) 5. Command Injection 6. XML Injection 7. LDAP Injection 8. XPath Injection 9. HTML Injection 10. Server-Side Includes (SSI) Injection 11. OS Command Injection 12. Blind SQL Injection 13. Server-Side Template Injection (SSTI) ⚡️ Broken Authentication and Session Management: 14. Session Fixation 15. Brute Force Attack 16. Session Hijacking 17. Password Cracking 18. Weak Password Storage 19. Insecure Authentication 20. Cookie Theft 21. Credential Reuse ⚡️ Sensitive Data Exposure: 22. Inadequate Encryption 23. Insecure Direct Object References (IDOR) 24. Data Leakage 25. Unencrypted Data Storage 26. Missing Security Headers 27. Insecure File Handling ⚡️ Security Misconfiguration: 28. Default Passwords 29. Directory Listing 30. Unprotected API Endpoints 31. Open Ports and Services 32. Improper Access Controls 33. Information Disclosure 34. Unpatched Software 35. Misconfigured CORS 36. HTTP Security Headers Misconfiguration ⚡️ XML-Related Vulnerabilities: 37. XML External Entity (XXE) Injection 38. XML Entity Expansion (XEE) 39. XML Bomb ⚡️ Broken Access Control: 40. Inadequate Authorization 41. Privilege Escalation 42. Insecure Direct Object References 43. Forceful Browsing 44. Missing Function-Level Access Control ⚡️ Insecure Deserialization: 45. Remote Code Execution via Deserialization 46. Data Tampering 47. Object Injection ⚡️ API Security Issues: 48. Insecure API Endpoints 49. API Key Exposure 50. Lack of Rate Limiting 51. Inadequate Input Validation ⚡️ Insecure Communication: 52. Man-in-the-Middle (MITM) Attack 53. Insufficient Transport Layer Security 54. Insecure SSL/TLS Configuration 55. Insecure Communication Protocols ⚡️ Client-Side Vulnerabilities: 56. DOM-based XSS 57. Insecure Cross-Origin Communication 58. Browser Cache Poisoning 59. Clickjacking 60. HTML5 Security Issues ⚡️ Denial of Service (DoS): 61. Distributed Denial of Service (DDoS) 62. Application Layer DoS 63. Resource Exhaustion 64. Slowloris Attack 65. XML Denial of Service ⚡️ Other Web Vulnerabilities: 66. Server-Side Request Forgery (SSRF) 67. HTTP Parameter Pollution (HPP) 68. Insecure Redirects and Forwards 69. File Inclusion Vulnerabilities 70. Security Header Bypass 71. Clickjacking 72. Inadequate Session Timeout 73. Insufficient Logging and Monitoring 74. Business Logic Vulnerabilities 75. API Abuse ⚡️ Mobile Web Vulnerabilities: 76. Insecure Data Storage on Mobile Devices 77. Insecure Data Transmission on Mobile Devices 78. Insecure Mobile API Endpoints 79. Mobile App Reverse Engineering ⚡️ IoT Web Vulnerabilities: 80. Insecure IoT Device Management 81. Weak Authentication on IoT Devices 82. IoT Device Vulnerabilities ⚡️ Web of Things (WoT) Vulnerabilities: 83. Unauthorized Access to Smart Homes 84. IoT Data Privacy Issues ⚡️ Authentication Bypass: 85. Insecure "Remember Me" Functionality 86. CAPTCHA Bypass ⚡️ Server-Side Request Forgery (SSRF): 87. Blind SSR 88. Time-Based Blind SSRF ⚡️ Content Spoofing: 89. MIME Sniffing 90. X-Content-Type-Options Bypass 91. Content Security Policy (CSP) Bypass ⚡️ Business Logic Flaws: 92. Inconsistent Validation 93. Race Conditions 94. Order Processing Vulnerabilities 95. Price Manipulation 96. Account Enumeration 97. User-Based Flaws ⚡️ Zero-Day Vulnerabilities: 98. Unknown Vulnerabilities 99. Unpatched Vulnerabilities 100. Day-Zero Exploits ➡️ Give 100+ Reactions 😎

List of Top 30 Wi-Fi Hacking Tools: 1. Aircrack-ng  2. Kismet  3. Wireshark  4. Reaver  5. Fern WiFi Cracker  6. Wifite  7. Airgeddon  8. Fluxion  9. Bully  10. InSSIDer  11. NetStumbler  12. WiFi Pineapple  13. Ghost Phisher  14. CoWPAtty  15. Bettercap  16. Bluepot  17. THC-Hydra  18. Pixiewps  19. Pyrit  20. WPA3 WiFi Hacking Suite  21. MDK4  22. AirSnort  23. WiFiPhisher  24. FreeRADIUS-WPE  25. Yersinia  26. WiFi Pumpkin  27. Evil Twin Attack Framework  28. Macchanger  29. WiFi Password Recovery  30. WiFi Password Decryptor ➡️ Give 100+ Reactions on this one 🙌

Step guide to prepare for your software engineering interview👇 1. Maximize your chances of being shortlisted 2. Find out the interview format 3. Pick a programming language 4. Sharpen your Computer Science fundamentals for interviews 5. Practice for the coding interview 6. Prepare for the system design interview (for mid/senior levels) 7. Prepare for the behavioral interview 8. Negotiating the offer package React ❤️ if you want more such valuable knowledge

⬇️ This is how you need to tackle Leetcode problem - 1. Read about the topic. Eg - Arrays. 2. Solve the specfic problem on the topic. 3. You can refer to neetcode website also. 4. Put your phone on DND and use stopwatch. 👉Decide the time to solve question 👉Use pen and paper to build Logic and then code 👉 Don't see solution in starting 👉 Give your best and after 1hr ,refer solution. 5. Code by yourself after you see solution. 6. Restart the same process. Pro tip- Give time to think before closing the laptop about what all you have done today. Rethink about questions you solved❤️