๐๏ธMicrosoft is now providing FREE courses in the following cutting-edge areas:
Artificial Intelligence (AI)
Internet of Things (IoT)
Data Science
Machine Learning
๐ The best part? These courses are designed with a project-based approach, allowing you to learn while actively building and creating!
๐คAl for beginners: A 12-week, 24-lesson curriculum all about Artificial Intelligence.
Check this out: https://t.co/CMlsTfqfqk
๐พ๐ฝ๏ธ IOT: Learn about IOT by doing a project that covers the journey of food from farm to table. This includes farming, logistics, manufacturing, retail, and consumer - all popular industry areas for loT devices.
Check this out: https://t.co/zj4Eiu82ZK
๐Machine Learning: A great course on classical machine learning, using Scikit- learn! Check this out: https://lnkd.in/gjF5ypVY
๐Data Science: This course covers Deep Learning & Data Science in more detail! Check this out: https://t.co/QbivVp8hIC
Donโt miss out on this incredible opportunity to learn from the best. Enroll in these free certification courses and take your skills to the next level!๐
https://www.instagram.com/reel/C601ttUy6_p/?igsh=MW9jdXdiN3M1Z25hOA==
Artificial Intelligence (AI)
Internet of Things (IoT)
Data Science
Machine Learning
๐ The best part? These courses are designed with a project-based approach, allowing you to learn while actively building and creating!
๐คAl for beginners: A 12-week, 24-lesson curriculum all about Artificial Intelligence.
Check this out: https://t.co/CMlsTfqfqk
๐พ๐ฝ๏ธ IOT: Learn about IOT by doing a project that covers the journey of food from farm to table. This includes farming, logistics, manufacturing, retail, and consumer - all popular industry areas for loT devices.
Check this out: https://t.co/zj4Eiu82ZK
๐Machine Learning: A great course on classical machine learning, using Scikit- learn! Check this out: https://lnkd.in/gjF5ypVY
๐Data Science: This course covers Deep Learning & Data Science in more detail! Check this out: https://t.co/QbivVp8hIC
Donโt miss out on this incredible opportunity to learn from the best. Enroll in these free certification courses and take your skills to the next level!๐
https://www.instagram.com/reel/C601ttUy6_p/?igsh=MW9jdXdiN3M1Z25hOA==
microsoft.github.io
AI for Beginners
Description
๐6
Tips for using Data Analysis tools ๐ฅ
๐Understand Your Tools: Take the time to thoroughly understand the features and capabilities of the data analysis tools you're using. This knowledge will empower you to use them more effectively.
๐Data Cleaning is Key: Before diving into analysis, ensure your data is clean and well-organized. Address missing values, outliers, and inconsistencies to ensure accurate results.
๐Document Your Process: Keep detailed documentation of your analysis process, including the steps taken and the reasoning behind them. This makes it easier to revisit and share your work.
๐Utilize Keyboard Shortcuts: Learn and use keyboard shortcuts for your chosen tools to improve efficiency. This can save a significant amount of time in the long run.
๐Explore Visualization Options: Experiment with various visualization techniques to represent your data. Visualizations often make complex patterns easier to understand and communicate.
๐Master the Art of Querying: If you're using SQL or similar query languages, practice and become proficient in writing efficient and effective queries. This skill is fundamental for extracting valuable insights from databases.
๐Stay Organized: Keep your files, code, and datasets organized. Establish a clear folder structure and naming conventions to make it easy to locate and understand your work.
๐Validate Results: Double-check your results using different methods or tools. Verifying your findings helps ensure the accuracy and reliability of your analysis.
๐Collaborate and Seek Feedback: If you're working in a team, collaborate with others and seek feedback on your analysis. This can lead to valuable insights and improvements.
๐Continuous Learning: Data analysis tools are continually evolving. Stay curious and invest time in staying updated with the latest features, tools, and best practices in data analysis.
๐๐Applying these tips can enhance your efficiency and the quality of your data analysis work.
๐Understand Your Tools: Take the time to thoroughly understand the features and capabilities of the data analysis tools you're using. This knowledge will empower you to use them more effectively.
๐Data Cleaning is Key: Before diving into analysis, ensure your data is clean and well-organized. Address missing values, outliers, and inconsistencies to ensure accurate results.
๐Document Your Process: Keep detailed documentation of your analysis process, including the steps taken and the reasoning behind them. This makes it easier to revisit and share your work.
๐Utilize Keyboard Shortcuts: Learn and use keyboard shortcuts for your chosen tools to improve efficiency. This can save a significant amount of time in the long run.
๐Explore Visualization Options: Experiment with various visualization techniques to represent your data. Visualizations often make complex patterns easier to understand and communicate.
๐Master the Art of Querying: If you're using SQL or similar query languages, practice and become proficient in writing efficient and effective queries. This skill is fundamental for extracting valuable insights from databases.
๐Stay Organized: Keep your files, code, and datasets organized. Establish a clear folder structure and naming conventions to make it easy to locate and understand your work.
๐Validate Results: Double-check your results using different methods or tools. Verifying your findings helps ensure the accuracy and reliability of your analysis.
๐Collaborate and Seek Feedback: If you're working in a team, collaborate with others and seek feedback on your analysis. This can lead to valuable insights and improvements.
๐Continuous Learning: Data analysis tools are continually evolving. Stay curious and invest time in staying updated with the latest features, tools, and best practices in data analysis.
๐๐Applying these tips can enhance your efficiency and the quality of your data analysis work.
Data Science Work From Home Internship Program 2024
Company Name :- Sony
Position:- Data Science Intern
Stipend :- Upto 50k/Month
Apply Link๐:-
https://bit.ly/4bhz732
Apply before the link expires
Company Name :- Sony
Position:- Data Science Intern
Stipend :- Upto 50k/Month
Apply Link๐:-
https://bit.ly/4bhz732
Apply before the link expires
๐4
๐๐จ๐ฉ ๐๐๐'๐ฌ ๐๐ข๐ซ๐ข๐ง๐ ๐
๐จ๐ซ ๐๐ฎ๐ฅ๐ญ๐ข๐ฉ๐ฅ๐ ๐๐จ๐ฅ๐๐ฌ ๐๐๐ซ๐จ๐ฌ๐ฌ ๐๐ง๐๐ข๐
Step 1:- ๐Upload Your Resume
https://bit.ly/3xiGdFq
Step 2:- Fill in your professional details like education & work experience (if any)
Step 3 :- Select your skills & preferred job role & location
Apply To The Jobs That Match To Your Profile.
๐๐บ๐ฝ ๐ก๐ผ๐๐ฒ:- Be aware of fake calls. Don't pay any money to recruiters.
Step 1:- ๐Upload Your Resume
https://bit.ly/3xiGdFq
Step 2:- Fill in your professional details like education & work experience (if any)
Step 3 :- Select your skills & preferred job role & location
Apply To The Jobs That Match To Your Profile.
๐๐บ๐ฝ ๐ก๐ผ๐๐ฒ:- Be aware of fake calls. Don't pay any money to recruiters.
๐1
Here is the list of Games learn css , html and javascript with fun.
1. CodeCombat
https://codecombat.com/
2. CSS Diner
https://flukeout.github.io/
3. Flexbox Froggy
https://flexboxfroggy.com/
4. CheckIO and Empire of Code
https://checkio.org/
5. Flexbox Defense
http://www.flexboxdefense.com/
6. Untrusted
https://alexnisnevich.github.io/untrusted/
7. CodeMonkey
https://www.codemonkey.com/
8. CodinGame
https://www.codingame.com/start/
1. CodeCombat
https://codecombat.com/
2. CSS Diner
https://flukeout.github.io/
3. Flexbox Froggy
https://flexboxfroggy.com/
4. CheckIO and Empire of Code
https://checkio.org/
5. Flexbox Defense
http://www.flexboxdefense.com/
6. Untrusted
https://alexnisnevich.github.io/untrusted/
7. CodeMonkey
https://www.codemonkey.com/
8. CodinGame
https://www.codingame.com/start/
CodeCombat
CodeCombat: Learn to Code by Playing a Game
Learn programming with a multiplayer live coding strategy game for beginners. Learn Python or JavaScript as you defeat ogres, solve mazes, and level up. Open source HTML5 game!
๐6๐ฅฐ2
Here's the website that have 10,000+ Tech internships with the government of India and top MNC's.
Link- https://internship.aicte-india.org/
https://www.meity.gov.in/applying-internship
Link- https://internship.aicte-india.org/
https://www.meity.gov.in/applying-internship
internship.aicte-india.org
AICTE Internship Portal - National Internship Portal for Students & Verified Internships India
Explore the All India Council for Technical Education AICTE Internship Portal, the National Internship Portal connecting students with verified companies for internships, PPOs, MSMEs, and skill-based opportunities. The AICTE portal for internship programsโฆ
๐1
HCL Tech Walk-In Drive On 18th May 2024
Role:- Software Engineer/Software Developer
Qualification: Any Graduate
CTC Offered โ 4.5 LPA to 6 LPA
Location: Noida, Chennai
Apply Link ๐:-
https://bit.ly/3wxosSN
Apply before the link expires.
Role:- Software Engineer/Software Developer
Qualification: Any Graduate
CTC Offered โ 4.5 LPA to 6 LPA
Location: Noida, Chennai
Apply Link ๐:-
https://bit.ly/3wxosSN
Apply before the link expires.
๐3๐1
๐ฅ๐These SQL interview questions typically asked in a Data Analyst interview?
1.What distinguishes a Primary key from a Unique key?
Primary key uniquely identifies each record in a table and cannot contain null values, whereas a Unique key also uniquely identifies records but can contain null values and multiple unique keys can exist in a table.
2. Define Candidate key.
Candidate key is a key or set of keys that uniquely identifies each record in a table. It can be a combination of Primary and Alternate keys.
3.Explain the concept of Constraint in SQL.
A Constraint is a specific rule or limit defined in a table to enforce data integrity. Examples include NOT NULL and AUTO INCREMENT.
4. Differentiate between TRUNCATE and DELETE commands.
TRUNCATE is a DDL command that removes all data from a table while preserving the table's structure, and it is faster than DELETE. DELETE is a DML command that removes specific rows based on conditions and operates slower than TRUNCATE as it deletes data row by row.
5.Compare and contrast a 'View' and a 'Stored Procedure'.
A View is a virtual table derived from one or more base tables, often used to simplify complex queries, while a Stored Procedure is a precompiled collection of SQL statements stored on the database server, used to perform specific tasks or operations.
6.What sets apart a Common Table Expression from a temporary table?
A Common Table Expression (CTE) is a temporary result set defined within the execution scope of a single SELECT, DELETE, or UPDATE statement, while a temporary table is stored in TempDB and persists until the session ends.
7.Contrast a clustered index with a non-clustered index.
A clustered index determines the physical ordering of data in a table and there can be only one clustered index per table. In contrast, a non-clustered index is similar to an index in a book where data is stored separately from the index, and multiple non-clustered indexes can exist for a table.
8.Define triggers in SQL and their purpose.
Triggers are SQL codes that automatically execute in response to certain events on a table, such as INSERT, UPDATE, or DELETE operations. They are used to maintain data integrity and perform actions based on specific conditions.
React ๐โค๏ธ to this it helps you in your Data Analyst interview..
1.What distinguishes a Primary key from a Unique key?
Primary key uniquely identifies each record in a table and cannot contain null values, whereas a Unique key also uniquely identifies records but can contain null values and multiple unique keys can exist in a table.
2. Define Candidate key.
Candidate key is a key or set of keys that uniquely identifies each record in a table. It can be a combination of Primary and Alternate keys.
3.Explain the concept of Constraint in SQL.
A Constraint is a specific rule or limit defined in a table to enforce data integrity. Examples include NOT NULL and AUTO INCREMENT.
4. Differentiate between TRUNCATE and DELETE commands.
TRUNCATE is a DDL command that removes all data from a table while preserving the table's structure, and it is faster than DELETE. DELETE is a DML command that removes specific rows based on conditions and operates slower than TRUNCATE as it deletes data row by row.
5.Compare and contrast a 'View' and a 'Stored Procedure'.
A View is a virtual table derived from one or more base tables, often used to simplify complex queries, while a Stored Procedure is a precompiled collection of SQL statements stored on the database server, used to perform specific tasks or operations.
6.What sets apart a Common Table Expression from a temporary table?
A Common Table Expression (CTE) is a temporary result set defined within the execution scope of a single SELECT, DELETE, or UPDATE statement, while a temporary table is stored in TempDB and persists until the session ends.
7.Contrast a clustered index with a non-clustered index.
A clustered index determines the physical ordering of data in a table and there can be only one clustered index per table. In contrast, a non-clustered index is similar to an index in a book where data is stored separately from the index, and multiple non-clustered indexes can exist for a table.
8.Define triggers in SQL and their purpose.
Triggers are SQL codes that automatically execute in response to certain events on a table, such as INSERT, UPDATE, or DELETE operations. They are used to maintain data integrity and perform actions based on specific conditions.
React ๐โค๏ธ to this it helps you in your Data Analyst interview..
๐6โค4
Here's free courses provided by Microsoft.
1. Microsoft azure AI foundation
Link- https://learn.microsoft.com/en-us/credentials/certifications/azure-ai-fundamentals/?practice-assessment-type=certification
2. Microsoft Certified: Azure Fundamentals
Link- https://learn.microsoft.com/en-us/credentials/certifications/azure-fundamentals/?practice-assessment-type=certification
3. Azure SQL fundamentals
Link- https://learn.microsoft.com/en-us/training/paths/azure-sql-fundamentals/
1. Microsoft azure AI foundation
Link- https://learn.microsoft.com/en-us/credentials/certifications/azure-ai-fundamentals/?practice-assessment-type=certification
2. Microsoft Certified: Azure Fundamentals
Link- https://learn.microsoft.com/en-us/credentials/certifications/azure-fundamentals/?practice-assessment-type=certification
3. Azure SQL fundamentals
Link- https://learn.microsoft.com/en-us/training/paths/azure-sql-fundamentals/
Docs
Microsoft Certified: Azure AI Fundamentals - Certifications
Demonstrate fundamental AI concepts related to the development of software and services of Microsoft Azure to create AI solutions.
๐3
๐๐จ๐ซ๐ค ๐
๐ซ๐จ๐ฆ ๐๐จ๐ฆ๐ ๐๐จ๐๐ฌ
Companies Hiring:- Amazon, Nielsen, Fresh Prints, Barclays & Many More
Qualification:- Graduate
Salary:- Rs.30,000 To Rs.60,000/Month
๐๐ฉ๐ฉ๐ฅ๐ฒ ๐๐ข๐ง๐ค ๐:-
https://bit.ly/3wOPIfv
Apply before the link expires
Companies Hiring:- Amazon, Nielsen, Fresh Prints, Barclays & Many More
Qualification:- Graduate
Salary:- Rs.30,000 To Rs.60,000/Month
๐๐ฉ๐ฉ๐ฅ๐ฒ ๐๐ข๐ง๐ค ๐:-
https://bit.ly/3wOPIfv
Apply before the link expires
โค3
Work From Home Internships
Stipend :- 20k To 40k/Month
Location:- Work From Home
Experience:- Students/Freshers
Apply Link ๐:-
https://bit.ly/3V57Qv1
Apply before the link expires.
Stipend :- 20k To 40k/Month
Location:- Work From Home
Experience:- Students/Freshers
Apply Link ๐:-
https://bit.ly/3V57Qv1
Apply before the link expires.
๐3
Start your career in data analysis for freshers ๐๐
1. Learn the Basics: Begin with understanding the fundamental concepts of statistics, mathematics, and programming languages like Python or R.
2. Acquire Technical Skills: Develop proficiency in data analysis tools such as Excel, SQL, and data visualization tools like Tableau or Power BI.
3. Gain Knowledge in Statistics: A solid foundation in statistical concepts is crucial for data analysis. Learn about probability, hypothesis testing, and regression analysis.
Free course by Khan Academy will help you to enhance these skills.
4. Programming Proficiency: Enhance your programming skills, especially in languages commonly used in data analysis like Python or R. Familiarity with libraries such as Pandas and NumPy in Python is beneficial. Kaggle has amazing content to learn these skills.
5. Data Cleaning and Preprocessing: Understand the importance of cleaning and preprocessing data. Learn techniques to handle missing values, outliers, and transform data for analysis.
6. Database Knowledge: Acquire knowledge about databases and SQL for efficient data retrieval and manipulation.
7. Data Visualization: Master the art of presenting insights through visualizations. Learn tools like Matplotlib, Seaborn, or ggplot2 for creating meaningful charts and graphs. If you are from non-technical background, learn Tableau or Power BI.
8. Machine Learning Basics: Familiarize yourself with basic machine learning concepts. This knowledge can be beneficial for advanced analytics tasks.
9. Build a Portfolio: Work on projects that showcase your skills. This could be personal projects, contributions to open-source projects, or challenges from platforms like Kaggle.
10. Networking and Continuous Learning: Engage with the data science community, attend meetups, webinars, and conferences. Build your strong Linkedin profile and enhance your network.
11. Apply for Internships or Entry-Level Positions: Gain practical experience by applying for internships or entry-level positions in data analysis. Real-world projects contribute significantly to your learning.
12. Effective Communication: Develop strong communication skills. Being able to convey your findings and insights in a clear and understandable manner is crucial.
1. Learn the Basics: Begin with understanding the fundamental concepts of statistics, mathematics, and programming languages like Python or R.
2. Acquire Technical Skills: Develop proficiency in data analysis tools such as Excel, SQL, and data visualization tools like Tableau or Power BI.
3. Gain Knowledge in Statistics: A solid foundation in statistical concepts is crucial for data analysis. Learn about probability, hypothesis testing, and regression analysis.
Free course by Khan Academy will help you to enhance these skills.
4. Programming Proficiency: Enhance your programming skills, especially in languages commonly used in data analysis like Python or R. Familiarity with libraries such as Pandas and NumPy in Python is beneficial. Kaggle has amazing content to learn these skills.
5. Data Cleaning and Preprocessing: Understand the importance of cleaning and preprocessing data. Learn techniques to handle missing values, outliers, and transform data for analysis.
6. Database Knowledge: Acquire knowledge about databases and SQL for efficient data retrieval and manipulation.
7. Data Visualization: Master the art of presenting insights through visualizations. Learn tools like Matplotlib, Seaborn, or ggplot2 for creating meaningful charts and graphs. If you are from non-technical background, learn Tableau or Power BI.
8. Machine Learning Basics: Familiarize yourself with basic machine learning concepts. This knowledge can be beneficial for advanced analytics tasks.
9. Build a Portfolio: Work on projects that showcase your skills. This could be personal projects, contributions to open-source projects, or challenges from platforms like Kaggle.
10. Networking and Continuous Learning: Engage with the data science community, attend meetups, webinars, and conferences. Build your strong Linkedin profile and enhance your network.
11. Apply for Internships or Entry-Level Positions: Gain practical experience by applying for internships or entry-level positions in data analysis. Real-world projects contribute significantly to your learning.
12. Effective Communication: Develop strong communication skills. Being able to convey your findings and insights in a clear and understandable manner is crucial.
๐4โค1๐1
Here are your links for free AI COURSES provided by Nvidia ๐
Generative AI Explained -
https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-FX-07+V1
Augment your LLM Using Retrieval Augmented Generation - https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-FX-16+V1
Building RAG Agents with LLMs - https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-FX-15+V1
Getting Started with AI on Jetson Nano - https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-RX-02+V2
Building Video AI Applications at the Edge on Jetson Nano - https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-IV-02+V2
Essentials of Developing Omniverse Kit Applications - https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-OV-11+V1
Generative AI Explained -
https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-FX-07+V1
Augment your LLM Using Retrieval Augmented Generation - https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-FX-16+V1
Building RAG Agents with LLMs - https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-FX-15+V1
Getting Started with AI on Jetson Nano - https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-RX-02+V2
Building Video AI Applications at the Edge on Jetson Nano - https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-IV-02+V2
Essentials of Developing Omniverse Kit Applications - https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-OV-11+V1
๐5
Here's the website link which have cheet sheets for CODERS GAMERS and STUDENTS.
Link- https://cheatography.com/
Link- https://cheatography.com/
Cheatography
Download Free Cheat Sheets or Create Your Own! - Cheatography.com: Cheat Sheets For Every Occasion
Find thousands of incredible, original programming cheat sheets, all free to download.
๐1
Hey Guys,
Applying For Jobs , But Not Getting Interview Calls?
We had Interaction with Recruiters from Top Companies.
Here are the 5 Best Tips to get Interview Calls ๐
https://bit.ly/4ayA3ia
All The Best ๐ฅ
Applying For Jobs , But Not Getting Interview Calls?
We had Interaction with Recruiters from Top Companies.
Here are the 5 Best Tips to get Interview Calls ๐
https://bit.ly/4ayA3ia
All The Best ๐ฅ
๐2
ROADMAP TO LEARN FRONTEND WEB DEVELOPMENT
By following this roadmap, you'll gain a solid understanding of frontend web development, equipping you with the skills needed to build modern, responsive, and dynamic web applications:
1. Foundational Skills
I. HTML (HyperText Markup Language)
โ Structure of a webpage
โ Semantic HTML elements
โ Forms and input elements
โ Accessibility basics
II. CSS (Cascading Style Sheets)
โ Styling elements
โ Layout techniques (Box Model, Flexbox, Grid)
โ Responsive design (Media Queries)
โ CSS preprocessors (Sass, LESS)
III. JavaScript
โ Syntax and basics (variables, data types, loops, functions)
โ DOM manipulation
โ Event handling
โ ES6+ features (let, const, arrow functions, template literals)
2. Version Control Systems
I. Git
โ Basic commands (init, clone, add, commit, push, pull)
โ Branching and merging
โ Using platforms like GitHub, GitLab, or Bitbucket
3. Development Tools
I. Text Editors/IDEs
โ VSCode, Sublime Text, Atom
II. Browser Developer Tools
โ Inspecting and debugging HTML, CSS, and JavaScript
III. Command Line Basics
โ Navigating the filesystem
โ Running scripts
4. Advanced JavaScript
I. Asynchronous JavaScript
โ Callbacks, Promises, Async/Await
II. JavaScript Modules
โ Import/export syntax
โ Module bundlers (Webpack, Parcel)
III. APIs and AJAX
โ Fetch API, XMLHttpRequest
โ Working with JSON
โ RESTful services
5. Frontend Frameworks and Libraries
I. React
โ Components, JSX
โ State and Props
โ Hooks and Lifecycle methods
โ React Router for navigation
II. Vue.js
โ Vue instance, directives
โ Components, Vue Router, Vuex for state management
III. Angular
โ TypeScript, Components, Modules
โ Services and Dependency Injection
โ Angular Router
6. State Management
I. Redux (for React)
โ Store, Actions, Reducers
โ Middleware (Redux Thunk, Redux Saga)
II. Vuex (for Vue.js)
III. NgRx (for Angular)
7. Styling Frameworks and Libraries
I. CSS Frameworks
โ Bootstrap, Bulma, Tailwind CSS
II. CSS-in-JS
โ Styled-components, Emotion
8. Build Tools and Automation
I. Task Runners
โ npm scripts, Gulp
II. Module Bundlers
โ Webpack, Parcel, Rollup
III. Code Quality Tools
โ Linters (ESLint, Stylelint)
โ Formatters (Prettier)
9. Testing
I. Unit Testing
โ Jest, Mocha, Jasmine
II. Integration Testing
โ React Testing Library, Enzyme
III. End-to-End Testing
โ Cypress, Selenium
10. Progressive Web Apps (PWAs)
I. Service Workers
โ Caching strategies
โ Offline functionality
II. Web App Manifest
โ Metadata for the app
11. Performance Optimization
I. Code Splitting
II. Lazy Loading
III. Image Optimization
IV. Minification and Compression
12. Deployment and Hosting
I. Static Site Generators
โ Next.js, Nuxt.js, Gatsby
II. Hosting Platforms
โ Netlify, Vercel, GitHub Pages
III. CI/CD
โ Setting up continuous integration and deployment pipelines
13. Soft Skills and Collaboration
I. Agile/Scrum Methodologies
II. Communication Skills
III. Problem-Solving and Debugging
Additional Resources
I. Online Courses and Tutorials
โ FreeCodeCamp, Codecademy, Coursera, Udemy
II. Documentation and Books
โ MDN Web Docs, W3Schools, "You Donโt Know JS" series
III. Community and Networking
โ Join developer communities (Reddit, Stack Overflow, Dev.to)
โ Attend webinars and conferences
By following this roadmap, you'll gain a solid understanding of frontend web development, equipping you with the skills needed to build modern, responsive, and dynamic web applications:
1. Foundational Skills
I. HTML (HyperText Markup Language)
โ Structure of a webpage
โ Semantic HTML elements
โ Forms and input elements
โ Accessibility basics
II. CSS (Cascading Style Sheets)
โ Styling elements
โ Layout techniques (Box Model, Flexbox, Grid)
โ Responsive design (Media Queries)
โ CSS preprocessors (Sass, LESS)
III. JavaScript
โ Syntax and basics (variables, data types, loops, functions)
โ DOM manipulation
โ Event handling
โ ES6+ features (let, const, arrow functions, template literals)
2. Version Control Systems
I. Git
โ Basic commands (init, clone, add, commit, push, pull)
โ Branching and merging
โ Using platforms like GitHub, GitLab, or Bitbucket
3. Development Tools
I. Text Editors/IDEs
โ VSCode, Sublime Text, Atom
II. Browser Developer Tools
โ Inspecting and debugging HTML, CSS, and JavaScript
III. Command Line Basics
โ Navigating the filesystem
โ Running scripts
4. Advanced JavaScript
I. Asynchronous JavaScript
โ Callbacks, Promises, Async/Await
II. JavaScript Modules
โ Import/export syntax
โ Module bundlers (Webpack, Parcel)
III. APIs and AJAX
โ Fetch API, XMLHttpRequest
โ Working with JSON
โ RESTful services
5. Frontend Frameworks and Libraries
I. React
โ Components, JSX
โ State and Props
โ Hooks and Lifecycle methods
โ React Router for navigation
II. Vue.js
โ Vue instance, directives
โ Components, Vue Router, Vuex for state management
III. Angular
โ TypeScript, Components, Modules
โ Services and Dependency Injection
โ Angular Router
6. State Management
I. Redux (for React)
โ Store, Actions, Reducers
โ Middleware (Redux Thunk, Redux Saga)
II. Vuex (for Vue.js)
III. NgRx (for Angular)
7. Styling Frameworks and Libraries
I. CSS Frameworks
โ Bootstrap, Bulma, Tailwind CSS
II. CSS-in-JS
โ Styled-components, Emotion
8. Build Tools and Automation
I. Task Runners
โ npm scripts, Gulp
II. Module Bundlers
โ Webpack, Parcel, Rollup
III. Code Quality Tools
โ Linters (ESLint, Stylelint)
โ Formatters (Prettier)
9. Testing
I. Unit Testing
โ Jest, Mocha, Jasmine
II. Integration Testing
โ React Testing Library, Enzyme
III. End-to-End Testing
โ Cypress, Selenium
10. Progressive Web Apps (PWAs)
I. Service Workers
โ Caching strategies
โ Offline functionality
II. Web App Manifest
โ Metadata for the app
11. Performance Optimization
I. Code Splitting
II. Lazy Loading
III. Image Optimization
IV. Minification and Compression
12. Deployment and Hosting
I. Static Site Generators
โ Next.js, Nuxt.js, Gatsby
II. Hosting Platforms
โ Netlify, Vercel, GitHub Pages
III. CI/CD
โ Setting up continuous integration and deployment pipelines
13. Soft Skills and Collaboration
I. Agile/Scrum Methodologies
II. Communication Skills
III. Problem-Solving and Debugging
Additional Resources
I. Online Courses and Tutorials
โ FreeCodeCamp, Codecademy, Coursera, Udemy
II. Documentation and Books
โ MDN Web Docs, W3Schools, "You Donโt Know JS" series
III. Community and Networking
โ Join developer communities (Reddit, Stack Overflow, Dev.to)
โ Attend webinars and conferences
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TechSchoool pinned ยซROADMAP TO LEARN FRONTEND WEB DEVELOPMENT By following this roadmap, you'll gain a solid understanding of frontend web development, equipping you with the skills needed to build modern, responsive, and dynamic web applications: 1. Foundational Skills I.โฆยป
Coding Projects That Will Actually Get You Hired
1. 3D Engine
David Rousset: write a 3D soft engine from scratch
Link- https://www.davrous.com/2013/06/13/tutorial-series-learning-how-to-write-a-3d-soft-engine-from-scratch-in-c-typescript-or-javascript/
2. Neural Networks
Victor Zhou: Introduction to neural networks
Link- https://victorzhou.com/blog/intro-to-neural-networks/
3. Build A Web Browser
Chris Harrelson: Web browser engineering
Link- https://browser.engineering/
4. Build A Framework
Paul Shannessy: Building React from Scratch
Link- https://youtu.be/_MAD4Oly9yg?si=Hj2tmWWQl9u5Hu2X
1. 3D Engine
David Rousset: write a 3D soft engine from scratch
Link- https://www.davrous.com/2013/06/13/tutorial-series-learning-how-to-write-a-3d-soft-engine-from-scratch-in-c-typescript-or-javascript/
2. Neural Networks
Victor Zhou: Introduction to neural networks
Link- https://victorzhou.com/blog/intro-to-neural-networks/
3. Build A Web Browser
Chris Harrelson: Web browser engineering
Link- https://browser.engineering/
4. Build A Framework
Paul Shannessy: Building React from Scratch
Link- https://youtu.be/_MAD4Oly9yg?si=Hj2tmWWQl9u5Hu2X
David Rousset
Tutorial series: learning how to write a 3D soft engine from scratch in C#, TypeScript or JavaScript
Iโd to like to share with you how Iโve learned to build whatโs known as a โ3D soft engineโ through a series of tutorials. โSoftware engineโ means that we will use only the CPU to build a 3D engine in an old school way (remember Doom on your 80386 ?). Iโllโฆ
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