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Complete Roadmap to learn Excel in 2024 👇👇 1. Basic Excel Skills:    - Familiarize yourself with Excel's interface and navigation.    - Learn basic formulas (SUM, AVERAGE, COUNT, etc.).    - Understand cell referencing (absolute vs. relative). 2. Data Entry and Formatting:    - Practice entering and formatting data efficiently.    - Explore cell formatting options for a clean and organized dataset. 3. Advanced Formulas:    - Master more advanced formulas like VLOOKUP, HLOOKUP, INDEX-MATCH.    - Learn logical functions (IF, AND, OR).    - Understand array formulas for complex calculations. 4. Pivot Tables:    - Gain proficiency in creating Pivot Tables for data summarization.    - Learn to customize and format Pivot Tables effectively. 5. Data Cleaning:    - Acquire skills in cleaning and transforming data.    - Explore text-to-columns, remove duplicates, and data validation. 6. Charts and Graphs:    - Learn to create various charts (bar, line, pie) for data visualization.    - Understand chart formatting and customization. 7. Dashboard Creation:    - Combine charts and tables to build basic dashboards.    - Explore dynamic dashboards using Excel features. 8. Macros and VBA:    - Dive into basic automation using Excel macros.    - Learn Visual Basic for Applications (VBA) for more advanced automation. 9. Power Query:    - Introduce yourself to Power Query for enhanced data manipulation.    - Learn to import, transform, and load data efficiently. 10. Advanced Excel Techniques:    - Explore advanced features like Goal Seek, Solver, and Scenario Manager.    - Master the use of data tables for sensitivity analysis. 11. Real-world Projects:    - Apply your skills to real-world projects or datasets.    - Practice solving analytical problems using Excel. Remember to practice consistently, as hands-on experience is crucial for mastering Excel. This roadmap will provide a solid foundation for your journey into data analysis using Excel. 5️⃣ Free resources to practice Excel https://www.w3schools.com/EXCEL/index.php https://bit.ly/3PSorPT http://learn.microsoft.com/en-gb/training/paths/modern-analytics/ https://t.me/excel_analyst/52 https://excel-practice-online.com/ Join for more: https://t.me/free4unow_backup ENJOY LEARNING 👍👍

Here is a complete roadmap to learn Data Structures and Algorithms (DSA): 👇👇 1. Basics of Programming: Start by learning the basics of a programming language like Python, Java, or C++. Understand concepts like variables, loops, functions, and arrays. 2. Data Structures: Study fundamental data structures like arrays, linked lists, stacks, queues, trees, graphs, and hash tables. Understand the operations that can be performed on these data structures and their time complexities. 3. Algorithms: Learn common algorithms like searching, sorting, recursion, dynamic programming, greedy algorithms, and divide and conquer. Understand how these algorithms work and their time complexities. 4. Problem Solving: Practice solving coding problems on platforms like LeetCode, HackerRank, or Codeforces. Start with easy problems and gradually move to medium and hard problems. 5. Complexity Analysis: Learn how to analyze the time and space complexity of algorithms. Understand Big O notation and how to calculate the complexity of different algorithms. 6. Advanced Data Structures: Study advanced data structures like AVL trees, B-trees, tries, segment trees, and fenwick trees. Understand when and how to use these data structures in problem-solving. 7. Graph Algorithms: Learn graph traversal algorithms like BFS and DFS. Study algorithms like Dijkstra's algorithm, Bellman-Ford algorithm, and Floyd-Warshall algorithm for shortest path problems. 8. Dynamic Programming: Master dynamic programming techniques for solving complex problems efficiently. Practice solving dynamic programming problems to build your skills. 9. Practice and Review: Regularly practice coding problems and review your solutions. Analyze your mistakes and learn from them to improve your problem-solving skills. 10. Mock Interviews: Prepare for technical interviews by participating in mock interviews and solving interview-style coding problems. Practice explaining your thought process and reasoning behind your solutions. Top DSA resources to crack coding interview 👉 GeekforGeeks 👉 Leetcode 👉 Hackerrank 👉 DSA Steps 👉 FreeCodeCamp 👉 Best DSA Resources Share with Credits: https://t.me/free4unow_backup ENJOY LEARNING 👍👍

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30 Days Roadmap to learn Ethical Hacking 👇👇 Day 1-3: Introduction to Ethical Hacking - Understand the basics of ethical hacking and its importance - Learn about different types of hackers and their motivations - Explore the legal and ethical considerations of ethical hacking Day 4-7: Networking Fundamentals - Learn about networking protocols, IP addresses, and subnets - Understand how data is transmitted over networks - Explore common network vulnerabilities and how to secure them Day 8-10: Information Gathering and Footprinting - Learn how to gather information about a target system or network - Explore techniques such as passive information gathering and footprinting - Understand the importance of reconnaissance in ethical hacking Day 11-14: Scanning and Enumeration - Learn how to scan for open ports and services on a target system - Understand the concept of enumeration and its role in ethical hacking - Explore tools such as Nmap for scanning and enumeration Day 15-17: Vulnerability Assessment and Exploitation - Learn how to identify and assess vulnerabilities in a target system - Understand common exploitation techniques and tools used in ethical hacking - Explore how to exploit vulnerabilities responsibly and ethically Day 18-21: Web Application Security - Learn about common web application vulnerabilities (e.g., SQL injection, XSS) - Understand how to secure web applications against attacks - Explore tools such as Burp Suite for web application testing Day 22-24: Wireless Network Security - Learn about common wireless network vulnerabilities and attacks - Understand how to secure wireless networks against intruders - Explore tools such as Aircrack-ng for wireless network penetration testing Day 25-27: Social Engineering and Physical Security - Learn about social engineering techniques used in ethical hacking - Understand the importance of physical security in cybersecurity - Explore ways to protect against social engineering attacks Day 28-30: Penetration Testing and Reporting - Learn how to conduct penetration tests on systems and networks - Understand the methodology of penetration testing (e.g., reconnaissance, scanning, exploitation, reporting) - Practice conducting penetration tests on virtual environments and create detailed reports on findings Remember to practice your skills in a controlled environment and always seek permission before performing any ethical hacking activities. Additionally, consider obtaining relevant certifications such as Certified Ethical Hacker (CEH) to validate your skills in ethical hacking. Some good resources to learn Ethical Hacking 1. Tutorials & Courses    - Informarion Security Free Course    - Ethical Hacking Bootcamp    - Network Hacking Course 2. Telegram Channels    - Cyber Security and Ethical Hacking    - Ethical Hacking Books 3. Books    - Ultimate Linux Free Book    - Python for Ethical Hacking 4. Ethical Hacking Forums Join @free4unow_backup for more free resources ENJOY LEARNING 👨‍💻🔒

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Complete Roadmap to learn Machine Learning and Artificial Intelligence 👇👇 Week 1-2: Introduction to Machine Learning - Learn the basics of Python programming language (if you are not already familiar with it) - Understand the fundamentals of Machine Learning concepts such as supervised learning, unsupervised learning, and reinforcement learning - Study linear algebra and calculus basics - Complete online courses like Andrew Ng's Machine Learning course on Coursera Week 3-4: Deep Learning Fundamentals - Dive into neural networks and deep learning - Learn about different types of neural networks like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) - Implement deep learning models using frameworks like TensorFlow or PyTorch - Complete online courses like Deep Learning Specialization on Coursera Week 5-6: Natural Language Processing (NLP) and Computer Vision - Explore NLP techniques such as tokenization, word embeddings, and sentiment analysis - Dive into computer vision concepts like image classification, object detection, and image segmentation - Work on projects involving NLP and Computer Vision applications Week 7-8: Reinforcement Learning and AI Applications - Learn about Reinforcement Learning algorithms like Q-learning and Deep Q Networks - Explore AI applications in fields like healthcare, finance, and autonomous vehicles - Work on a final project that combines different aspects of Machine Learning and AI Additional Tips: - Practice coding regularly to strengthen your programming skills - Join online communities like Kaggle or GitHub to collaborate with other learners - Read research papers and articles to stay updated on the latest advancements in the field Pro Tip: Roadmap won't help unless you start working on it consistently. Start working on projects as early as possible. 2 months are good as a starting point to get grasp the basics of Generative AI but mastering it is very difficult as AI keeps evolving every day. Best Resources to learn Generative AI 👇👇 Learn Python for Free Prompt Engineering Course Prompt Engineering Guide Data Science Course Google Cloud Generative AI Path Unlock the power of Generative AI Models Machine Learning with Python Free Course Machine Learning Free Book Deep Learning Nanodegree Program with Real-world Projects AI, Machine Learning and Deep Learning Join @free4unow_backup for more free courses ENJOY LEARNING👍👍

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Master SQL step-by-step! From basics to advanced, here are the key topics you need for a solid SQL foundation. 🚀 1. Foundations: - Learn basic SQL syntax, including SELECT, FROM, WHERE clauses. - Understand data types, constraints, and the basic structure of a database. 2. Database Design: - Study database normalization to ensure efficient data organization. - Learn about primary keys, foreign keys, and relationships between tables. 3. Queries and Joins: - Practice writing simple to complex SELECT queries. - Master different types of joins (INNER, LEFT, RIGHT, FULL) to combine data from multiple tables. 4. Aggregation and Grouping: - Explore aggregate functions like COUNT, SUM, AVG, MAX, and MIN. - Understand GROUP BY clause for summarizing data based on specific criteria. 5. Subqueries and Nested Queries: - Learn how to use subqueries to perform operations within another query. - Understand the concept of nested queries and their practical applications. 6. Indexing and Optimization: - Study indexing for enhancing query performance. - Learn optimization techniques, such as avoiding SELECT * and using appropriate indexes. 7. Transactions and ACID Properties: - Understand the basics of transactions and their role in maintaining data integrity. - Explore ACID properties (Atomicity, Consistency, Isolation, Durability) in database management. 8. Views and Stored Procedures: - Create and use views to simplify complex queries. - Learn about stored procedures for reusable and efficient query execution. 9. Security and Permissions: - Understand SQL injection risks and how to prevent them. - Learn how to manage user permissions and access control. 10. Advanced Topics: - Explore advanced SQL concepts like window functions, CTEs (Common Table Expressions), and recursive queries. - Familiarize yourself with database-specific features (e.g., PostgreSQL's JSON functions, MySQL's spatial data types). 11. Real-world Projects: - Apply your knowledge to real-world scenarios by working on projects. - Practice with sample databases or create your own to reinforce your skills. 12. Continuous Learning: - Stay updated on SQL advancements and industry best practices. - Engage with online communities, forums, and resources for ongoing learning and problem-solving. Here are some free resources to learn & practice SQL 👇👇 Udacity free course- https://imp.i115008.net/AoAg7K SQL For Data Analysis: https://t.me/sqlanalyst For Practice- https://stratascratch.com/?via=free SQL Learning Series: https://t.me/sqlspecialist/567 Top 10 SQL Projects with Datasets: https://t.me/DataPortfolio/16 ENJOY LEARNING 👍👍

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