Complete Roadmap to learn DSA in 30 days

Day 1-5: Introduction to Data Structures and Algorithms
- Understand the importance of DSA in programming
- Learn about different types of data structures (arrays, linked lists, stacks, queues, trees, graphs)
- Study basic algorithms like searching and sorting

Day 6-10: Arrays and Strings
- Dive deeper into arrays and strings
- Learn about common operations and algorithms on arrays and strings
- Practice solving problems related to arrays and strings

Day 11-15: Linked Lists
- Study linked lists and their variations (singly linked list, doubly linked list, circular linked list)
- Implement basic operations on linked lists
- Solve problems involving linked lists

Day 16-20: Stacks and Queues
- Learn about stacks and queues and their applications
- Implement stack and queue data structures
- Solve problems using stacks and queues

Day 21-25: Trees and Graphs
- Study binary trees, binary search trees, AVL trees, heaps, and graphs
- Understand traversal algorithms (inorder, preorder, postorder) for trees
- Implement basic graph algorithms (DFS, BFS)
- Solve problems related to trees and graphs

Day 26-30: Advanced Topics
- Study advanced data structures like hash tables, tries, segment trees
- Learn about dynamic programming, backtracking, and divide and conquer algorithms
- Practice solving complex problems that require a combination of data structures and algorithms

Throughout the 30 days, make sure to practice regularly by solving coding problems on platforms like LeetCode, HackerRank, or Codeforces. Additionally, review your concepts regularly and seek out resources like online tutorials, textbooks, and study groups to deepen your understanding of DSA.

5⃣ Free DSA resources to crack coding interview

👉 GeekforGeeks

👉 Leetcode

👉 Hackerrank

👉 DSA Resources

👉 FreeCodeCamp

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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://hottg.com/sqlanalyst

For Practice- https://stratascratch.com/?via=free

SQL Learning Series: https://hottg.com/sqlspecialist/567

Top 10 SQL Projects with Datasets: https://hottg.com/DataPortfolio/16

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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 ML & AI but mastering it is very difficult as AI keeps evolving every day.

Best Resources to learn ML & 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

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Complete Roadmap to become a data scientist in 5 months

Free Resources to learn Data Science: https://hottg.com/datasciencefun

Week 1-2: Fundamentals
- Day 1-3: Introduction to Data Science, its applications, and roles.
- Day 4-7: Brush up on Python programming.
- Day 8-10: Learn basic statistics and probability.

Week 3-4: Data Manipulation and Visualization
- Day 11-15: Pandas for data manipulation.
- Day 16-20: Data visualization with Matplotlib and Seaborn.

Week 5-6: Machine Learning Foundations
- Day 21-25: Introduction to scikit-learn.
- Day 26-30: Linear regression and logistic regression.

Work on Data Science Projects: https://hottg.com/pythonspecialist/29

Week 7-8: Advanced Machine Learning
- Day 31-35: Decision trees and random forests.
- Day 36-40: Clustering (K-Means, DBSCAN) and dimensionality reduction.

Week 9-10: Deep Learning
- Day 41-45: Basics of Neural Networks and TensorFlow/Keras.
- Day 46-50: Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).

Week 11-12: Data Engineering
- Day 51-55: Learn about SQL and databases.
- Day 56-60: Data preprocessing and cleaning.

Week 13-14: Model Evaluation and Optimization
- Day 61-65: Cross-validation, hyperparameter tuning.
- Day 66-70: Evaluation metrics (accuracy, precision, recall, F1-score).

Week 15-16: Big Data and Tools
- Day 71-75: Introduction to big data technologies (Hadoop, Spark).
- Day 76-80: Basics of cloud computing (AWS, GCP, Azure).

Week 17-18: Deployment and Production
- Day 81-85: Model deployment with Flask or FastAPI.
- Day 86-90: Containerization with Docker, cloud deployment (AWS, Heroku).

Week 19-20: Specialization
- Day 91-95: NLP or Computer Vision, based on your interests.

Week 21-22: Projects and Portfolios
- Day 96-100: Work on personal data science projects.

Week 23-24: Soft Skills and Networking
- Day 101-105: Improve communication and presentation skills.
- Day 106-110: Attend online data science meetups or forums.

Week 25-26: Interview Preparation
- Day 111-115: Practice coding interviews on platforms like LeetCode.
- Day 116-120: Review your projects and be ready to discuss them.

Week 27-28: Apply for Jobs
- Day 121-125: Start applying for entry-level data scientist positions.

Week 29-30: Interviews
- Day 126-130: Attend interviews, practice whiteboard problems.

Week 31-32: Continuous Learning
- Day 131-135: Stay updated with the latest trends in data science.

Week 33-34: Accepting Offers
- Day 136-140: Evaluate job offers and negotiate if necessary.

Week 35-36: Settling In
- Day 141-150: Start your new data science job, adapt to the team, and continue learning on the job.

Best Resources to learn Data Science

Intro to Data Analytics by Udacity

Machine Learning course by Google

Machine Learning with Python

Data Science Interview Questions

Data Science Project ideas

Data Science: Linear Regression Course by Harvard

Machine Learning Interview Questions

Free Datasets for Projects

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If I were to start Computer Science in 2023,

- Harvard - Stanford
- MIT - IBM - Telegram
- Microsoft - Google

❯ CS50 from Harvard
http://cs50.harvard.edu/x/2023/certificate/

❯ C/C++
http://ocw.mit.edu/courses/6-s096-effective-programming-in-c-and-c-january-iap-2014/

❯ Python
http://cs50.harvard.edu/python/2022/

https://hottg.com/dsabooks

❯ SQL
http://online.stanford.edu/courses/soe-ydatabases0005-databases-relational-databases-and-sql

https://hottg.com/sqlanalyst

❯ DSA
http://techdevguide.withgoogle.com/paths/data-structures-and-algorithms/

https://hottg.com/crackingthecodinginterview/290

❯ Java
http://learn.microsoft.com/shows/java-for-beginners/

https://hottg.com/Java_Programming_Notes

❯ JavaScript
http://learn.microsoft.com/training/paths/web-development-101/

https://hottg.com/javascript_courses

❯ TypeScript
http://learn.microsoft.com/training/paths/build-javascript-applications-typescript/

❯ C#
http://learn.microsoft.com/users/dotnet/collections/yz26f8y64n7k07

❯ Mathematics (incl. Statistics)
ocw.mit.edu/search/?d=Mathematics&s=department_course_numbers.sort_coursenum

❯ Data Science
cognitiveclass.ai/courses/data-science-101

https://hottg.com/datasciencefun/1141

❯ Machine Learning
http://developers.google.com/machine-learning/crash-course

❯ Deep Learning
introtodeeplearning.com

hottg.com/machinelearning_deeplearning/

❯ Full Stack Web (HTML/CSS)
pll.harvard.edu/course/cs50s-web-programming-python-and-javascript/2023-05

hottg.com/webdevcoursefree/594

❯ OS, Networking
ocw.mit.edu/courses/6-033-computer-system-engineering-spring-2018/

❯ Compiler Design
online.stanford.edu/courses/soe-ycscs1-compilers

Please give us credits while sharing: -> https://hottg.com/free4unow_backup

ENJOY LEARNING 👍👍

30-day roadmap to learn HTML, CSS, and JavaScript:

Day 1-5: HTML Basics
- Day 1-2: Introduction to HTML, tags, elements, and structure
- Day 3-4: Working with text, links, images, lists, and tables
- Day 5: Forms and input elements

Day 6-15: CSS Fundamentals
- Day 6-8: Introduction to CSS, selectors, properties, and values
- Day 9-11: Box model, margins, padding, borders, and positioning
- Day 12-15: CSS layout techniques, responsive design, and media queries

Day 16-25: JavaScript Essentials
- Day 16-18: Introduction to JavaScript, variables, data types, operators
- Day 19-21: Functions, control flow (if statements, loops), and arrays
- Day 22-25: DOM manipulation, events, and forms validation

Day 26-30: Project-Based Learning
- Day 26-27: Build a simple website using HTML and CSS
- Day 28-29: Enhance the website with interactivity using JavaScript
- Day 30: Finalize your project, test it thoroughly, and showcase it to others

Throughout the 30 days:
- Practice regularly by working on small projects and challenges
- Review your progress and reinforce your learning by revisiting key concepts
- Seek help from online resources, forums, and communities when you encounter difficulties
- Stay motivated and track your progress to see how far you've come

Here are some free resources to help you in the journey 👇

Into to HTML & CSS

Learn JavaScript

Learn HTML

Learn CSS

HTML Book

Full Stack Web Development Course

Object Oriented Javascript

Javascript Courses

CSS Roadmap

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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

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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

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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://hottg.com/excel_analyst/52

https://excel-practice-online.com/

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Best way to prepare for Python interviews 👇👇

1. Fundamentals: Strengthen your understanding of Python basics, including data types, control structures, functions, and object-oriented programming concepts.

2. Data Structures and Algorithms: Familiarize yourself with common data structures (lists, dictionaries, sets, etc.) and algorithms. Practice solving coding problems on platforms like LeetCode or HackerRank.

3. Problem Solving: Develop problem-solving skills by working on real-world scenarios. Understand how to approach and solve problems efficiently using Python.

4. Libraries and Frameworks: Be well-versed in popular Python libraries and frameworks relevant to the job, such as NumPy, Pandas, Flask, or Django. Demonstrate your ability to apply these tools in practical situations.

5. Web Development (if applicable): If the position involves web development, understand web frameworks like Flask or Django. Be ready to discuss your experience in building web applications using Python.

6. Database Knowledge: Have a solid understanding of working with databases in Python. Know how to interact with databases using SQLAlchemy or Django ORM.

7. Testing and Debugging: Showcase your proficiency in writing unit tests and debugging code. Understand testing frameworks like pytest and debugging tools available in Python.

8. Version Control: Familiarize yourself with version control systems, particularly Git, and demonstrate your ability to collaborate on projects using Git.

9. Projects: Showcase relevant projects in your portfolio. Discuss the challenges you faced, solutions you implemented, and the impact of your work.

10. Soft Skills: Highlight your communication and collaboration skills. Be ready to explain your thought process and decision-making during technical discussions.

Best Resource to learn Python

Python Interview Questions with Answers

Freecodecamp Python Course with FREE Certificate

Python for Data Analysis and Visualization

Python course for beginners by Microsoft

Python course by Google

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ENJOY LEARNING 👍👍

Complete Roadmap to become a web developer in two months:

Week 1-2: Basics of Web Development
1. HTML & CSS: Learn the fundamentals of building web pages with HTML for structure and CSS for styling.
2. Responsive Design: Understand how to make your websites responsive to different screen sizes using media queries.
3. Basic JavaScript: Start with basic JavaScript concepts like variables, data types, and operators.

Week 3-4: Intermediate Web Development
1. DOM Manipulation: Learn how to manipulate the Document Object Model (DOM) with JavaScript to dynamically change website content.
2. Intermediate JavaScript: Dive deeper into JavaScript with concepts like functions, arrays, objects, and control flow.
3. Version Control: Learn Git and GitHub for version control and collaboration.

Week 5-6: Frontend Development
1. Frontend Frameworks: Learn a frontend framework like React, Vue.js, or Angular. Focus on one and understand its fundamentals.
2. Package Managers: Learn how to use npm or yarn to manage dependencies for your projects.
3. CSS Preprocessors: Explore tools like Sass or Less to enhance your CSS workflow.

Week 7-8: Backend Development
1. Server-side Programming: Learn a backend language like Node.js with Express, Python with Django or Flask, or Ruby on Rails.
2. Databases: Understand basics of database management systems like MongoDB, MySQL, or PostgreSQL.
3. APIs: Learn how to build and consume APIs to connect your frontend and backend.

Additional Tips:
- Practice regularly by building projects. Start with simple ones and gradually increase complexity.
- Utilize online resources like tutorials, documentation, and forums like Stack Overflow and GitHub.
- Network with other developers through online communities and attend webinars or meetups.
- Stay updated with industry trends and best practices by following blogs and podcasts.

5 Free Web Development Courses by Udacity 👇👇

Intro to HTML and CSS

Intro to Backend

Networking for Web Developers

Intro to JavaScript

Object-Oriented JavaScript

Free Web Development Resources: 👇 https://hottg.com/webdevcoursefree

Join @free4unow_backup for more free resources.

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Free Programming and Data Analytics Resources 👇👇

✅ Data science and Data Analytics Free Courses by Google

https://developers.google.com/edu/python/introduction

https://grow.google/intl/en_in/data-analytics-course/?tab=get-started-in-the-field

https://cloud.google.com/data-science?hl=en

https://developers.google.com/machine-learning/crash-course

https://hottg.com/datasciencefun/1371

🔍 Free Data Analytics Courses by Microsoft

1. Get started with microsoft dataanalytics
https://learn.microsoft.com/en-us/training/paths/data-analytics-microsoft/

2. Introduction to version control with git
https://learn.microsoft.com/en-us/training/paths/intro-to-vc-git/

3. Microsoft azure ai fundamentals
https://learn.microsoft.com/en-us/training/paths/get-started-with-artificial-intelligence-on-azure/

🤖 Free AI Courses by Microsoft

1. Fundamentals of AI by Microsoft

https://learn.microsoft.com/en-us/training/paths/get-started-with-artificial-intelligence-on-azure/

2. Introduction to AI with python by Harvard.

https://pll.harvard.edu/course/cs50s-introduction-artificial-intelligence-python

📚 Useful Resources for the Programmers

Data Analyst Roadmap
https://hottg.com/sqlspecialist/94

Free C course from Microsoft
https://docs.microsoft.com/en-us/cpp/c-language/?view=msvc-170&viewFallbackFrom=vs-2019

Interactive React Native Resources
https://fullstackopen.com/en/part10

Python for Data Science and ML
https://hottg.com/datasciencefree/68

Ethical Hacking Bootcamp
https://hottg.com/ethicalhackingtoday/3

Unity Documentation
https://docs.unity3d.com/Manual/index.html

Advanced Javascript concepts
https://hottg.com/Programming_experts/72

Oops in Java
https://nptel.ac.in/courses/106105224

Intro to Version control with Git
https://docs.microsoft.com/en-us/learn/modules/intro-to-git/0-introduction

Python Data Structure and Algorithms
https://hottg.com/programming_guide/76

Free PowerBI course by Microsoft
https://docs.microsoft.com/en-us/users/microsoftpowerplatform-5978/collections/k8xidwwnzk1em

Data Structures Interview Preparation
https://hottg.com/crackingthecodinginterview/309

🍻 Free Programming Courses by Microsoft

❯ JavaScript
http://learn.microsoft.com/training/paths/web-development-101/

❯ TypeScript
http://learn.microsoft.com/training/paths/build-javascript-applications-typescript/

❯ C#
http://learn.microsoft.com/users/dotnet/collections/yz26f8y64n7k07

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🎯 𝐅𝐫𝐨𝐧𝐭𝐞𝐧𝐝 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 𝐒𝐤𝐢𝐥𝐥𝐬 𝐟𝐨𝐫 𝐏𝐫𝐨𝐝𝐮𝐜𝐭 𝐁𝐚𝐬𝐞𝐝 𝐂𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬 🔥

✅ A roadmap is the best way to kick-start your attempt to become a front-end developer.

📌 𝗕𝗮𝘀𝗶𝗰 𝗝𝗮𝘃𝗮𝗦𝗰𝗿𝗶𝗽𝘁 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀:
1. Data types
2. Functions
3. Scope in JavaScript
4. Closure
5. Event loop
6. Prototype and prototype chain
7. Class and inheritance
8. DOM
9. bind/call/apply
10. Promise
11. WebAPI
12. Task queue
13. Call stack
14. Async/await
15. Generators
16. Typescript

📌 𝗕𝗮𝘀𝗶𝗰 𝗛𝗧𝗠𝗟 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀:
1. block element
2. import
3. etc - infinite questions

📌 𝗕𝗮𝘀𝗶𝗰 𝗖𝗦𝗦 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀:
1. Class and other selectors
2. Pseudo Classes
3. Box Model
4. Pseudo Elements
5. CSS type - flex, grid, normal
6. How to center
7. pseudo classes and elements
8. All element states - active, hover
9. Media queries
10. Pre-processors - SCSS or LESS
1. mixins
11. CSS constants
12. BEM
13. Import

📌 𝗕𝗮𝘀𝗶𝗰 𝗪𝗲𝗯 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀:
1. Page rendering cycle
2. HTTP / HTTPS / https2
3. CORS
4. Local storage/Session storage
5. Cookie
6. JWT
7. XHR
8. Micro Frontend
9. REST/GraphQL/Socket connection
10. Browser Concepts
11. Debugging Application
12. Chrome Dev Tool Features

📌 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗝𝗮𝘃𝗮𝗦𝗰𝗿𝗶𝗽𝘁 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀:
1. OOPs concept
2. Design Patterns
a. Singleton
b. Provider
c. Prototype
d. Observer
e. Module
f. HOC

3. Understanding V8 in-depth
a. JIT
b. Interpreter
c. Execution
d. Compiler
4. Currying

📌 𝗕𝗮𝘀𝗶𝗰 𝗥𝗲𝗮𝗰𝘁𝗝𝗦 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀 (bonus): -
1. Introduction JSX
2. React Component
3. Component State and Props
4. Adding Style (CSS)
5. Functional and Class components
6. React Lifecycle Methods
7. Virtual DOM
8. React Hooks
9. Custom Hooks
10. Context API
11. Synthetic Events
12. Routing
13. Data Flow (Redux/Flux)
14. Server-Side Rendering
15. Unit Testing
16. Jest & React Testing Library
17. Mocking Data
18. Understanding Webpack (Bundler)
19. Babel, env, prettier, linter

Free Books and Courses to learn Frontend Development
👇👇

Frontend Development Free Course with Project

Frontend Development Roadmap

Frontend Developer Free Book

Frontend Interview preparation handbook

Foundations of Frontend Development Free Udemy course

Javascript Resources

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