Steps to learn Data Structures and Algorithms (DSA) with Python
1. Learn Python: If you're not already familiar with Python, start by learning the basics of the language. There are many online resources and tutorials available for free.
2. Understand the Basics: Before diving into DSA, make sure you have a good grasp of Python's syntax, data types, and basic programming concepts. Use free resources from @dsabooks to help you in learning journey.
3. Pick Good Learning Resources: Choose a good book, online course, or tutorial series on DSA with Python. Most of the free stuff is already posted on the channel @crackingthecodinginterview
4. Data Structures: Begin with fundamental data structures like lists, arrays, stacks, queues, linked lists, trees, graphs, and hash tables. Understand their properties, operations, and when to use them.
5. Algorithms: Study common algorithms such as searching (binary search, linear search), sorting (quick sort, merge sort), and dynamic programming. Learn about their time and space complexity.
6. Practice: The key to mastering DSA is practice. Solve a wide variety of problems to apply your knowledge. Websites like LeetCode and HackerRank provide a vast collection of problems.
7. Analyze Complexity: Learn how to analyze the time and space complexity of algorithms. Big O notation is a crucial concept in DSA.
8. Implement Algorithms: Implement algorithms and data structures from scratch in Python. This hands-on experience will deepen your understanding.
9. Project Work: Apply DSA to real projects. This could be building a simple game, a small web app, or any software that requires efficient data handling. Check channel @programming_experts if you need project ideas.
10. Seek Help and Collaborate: Don't hesitate to ask for help when you're stuck. Engage in coding communities, forums, or collaborate with others to gain new insights.
11. Review and Revise: Periodically review what you've learned. Reinforce your understanding by revisiting data structures and algorithms you've studied.
12. Competitive Programming: Participate in competitive programming contests. They are a great way to test your skills and improve your problem-solving abilities.
13. Stay Updated: DSA is an ever-evolving field. Stay updated with the latest trends and algorithms.
14. Contribute to Open Source: Consider contributing to open source projects. It's a great way to apply your knowledge and work on real-world code.
15. Teach Others: Teaching what you've learned to others can deepen your understanding. You can create tutorials or mentor someone.
Join @free4unow_backup for more free courses
ENJOY LEARNING 👍👍
1. Learn Python: If you're not already familiar with Python, start by learning the basics of the language. There are many online resources and tutorials available for free.
2. Understand the Basics: Before diving into DSA, make sure you have a good grasp of Python's syntax, data types, and basic programming concepts. Use free resources from @dsabooks to help you in learning journey.
3. Pick Good Learning Resources: Choose a good book, online course, or tutorial series on DSA with Python. Most of the free stuff is already posted on the channel @crackingthecodinginterview
4. Data Structures: Begin with fundamental data structures like lists, arrays, stacks, queues, linked lists, trees, graphs, and hash tables. Understand their properties, operations, and when to use them.
5. Algorithms: Study common algorithms such as searching (binary search, linear search), sorting (quick sort, merge sort), and dynamic programming. Learn about their time and space complexity.
6. Practice: The key to mastering DSA is practice. Solve a wide variety of problems to apply your knowledge. Websites like LeetCode and HackerRank provide a vast collection of problems.
7. Analyze Complexity: Learn how to analyze the time and space complexity of algorithms. Big O notation is a crucial concept in DSA.
8. Implement Algorithms: Implement algorithms and data structures from scratch in Python. This hands-on experience will deepen your understanding.
9. Project Work: Apply DSA to real projects. This could be building a simple game, a small web app, or any software that requires efficient data handling. Check channel @programming_experts if you need project ideas.
10. Seek Help and Collaborate: Don't hesitate to ask for help when you're stuck. Engage in coding communities, forums, or collaborate with others to gain new insights.
11. Review and Revise: Periodically review what you've learned. Reinforce your understanding by revisiting data structures and algorithms you've studied.
12. Competitive Programming: Participate in competitive programming contests. They are a great way to test your skills and improve your problem-solving abilities.
13. Stay Updated: DSA is an ever-evolving field. Stay updated with the latest trends and algorithms.
14. Contribute to Open Source: Consider contributing to open source projects. It's a great way to apply your knowledge and work on real-world code.
15. Teach Others: Teaching what you've learned to others can deepen your understanding. You can create tutorials or mentor someone.
Join @free4unow_backup for more free courses
ENJOY LEARNING 👍👍
👍5❤1
A gentle reminder for software engineers (you'll thank me later):
• Learn SQL before ORM.
• Learn Git before Jenkins.
• Learn SQL before NoSQL.
• Learn CSS before Tailwind.
• Learn Linux before Docker.
• Learn Solidity before dApps.
• Learn English before Python.
• Learn REST before GraphQL.
• Learn JavaScript before React.
• Learn HTML before JavaScript.
• Learn React before Microfrontends.
• Learn Containers before Kubernetes.
• Learn Monolith before Microservices.
• Learn SQL before ORM.
• Learn Git before Jenkins.
• Learn SQL before NoSQL.
• Learn CSS before Tailwind.
• Learn Linux before Docker.
• Learn Solidity before dApps.
• Learn English before Python.
• Learn REST before GraphQL.
• Learn JavaScript before React.
• Learn HTML before JavaScript.
• Learn React before Microfrontends.
• Learn Containers before Kubernetes.
• Learn Monolith before Microservices.
👍10❤4
Here is the list of few projects (found on kaggle). They cover Basics of Python, Advanced Statistics, Supervised Learning (Regression and Classification problems) & Data Science
Please also check the discussions and notebook submissions for different approaches and solution after you tried yourself.
1. Basic python and statistics
Pima Indians :- https://www.kaggle.com/uciml/pima-indians-diabetes-database
Cardio Goodness fit :- https://www.kaggle.com/saurav9786/cardiogoodfitness
Automobile :- https://www.kaggle.com/toramky/automobile-dataset
2. Advanced Statistics
Game of Thrones:-https://www.kaggle.com/mylesoneill/game-of-thrones
World University Ranking:-https://www.kaggle.com/mylesoneill/world-university-rankings
IMDB Movie Dataset:- https://www.kaggle.com/carolzhangdc/imdb-5000-movie-dataset
3. Supervised Learning
a) Regression Problems
How much did it rain :- https://www.kaggle.com/c/how-much-did-it-rain-ii/overview
Inventory Demand:- https://www.kaggle.com/c/grupo-bimbo-inventory-demand
Property Inspection predictiion:- https://www.kaggle.com/c/liberty-mutual-group-property-inspection-prediction
Restaurant Revenue prediction:- https://www.kaggle.com/c/restaurant-revenue-prediction/data
IMDB Box office Prediction:-https://www.kaggle.com/c/tmdb-box-office-prediction/overview
b) Classification problems
Employee Access challenge :- https://www.kaggle.com/c/amazon-employee-access-challenge/overview
Titanic :- https://www.kaggle.com/c/titanic
San Francisco crime:- https://www.kaggle.com/c/sf-crime
Customer satisfcation:-https://www.kaggle.com/c/santander-customer-satisfaction
Trip type classification:- https://www.kaggle.com/c/walmart-recruiting-trip-type-classification
Categorize cusine:- https://www.kaggle.com/c/whats-cooking
4. Some helpful Data science projects for beginners
https://www.kaggle.com/c/house-prices-advanced-regression-techniques
https://www.kaggle.com/c/digit-recognizer
https://www.kaggle.com/c/titanic
5. Intermediate Level Data science Projects
Black Friday Data : https://www.kaggle.com/sdolezel/black-friday
Human Activity Recognition Data : https://www.kaggle.com/uciml/human-activity-recognition-with-smartphones
Trip History Data : https://www.kaggle.com/pronto/cycle-share-dataset
Million Song Data : https://www.kaggle.com/c/msdchallenge
Census Income Data : https://www.kaggle.com/c/census-income/data
Movie Lens Data : https://www.kaggle.com/grouplens/movielens-20m-dataset
Twitter Classification Data : https://www.kaggle.com/c/twitter-sentiment-analysis2
Share with credits: https://t.iss.one/sqlproject
ENJOY LEARNING 👍👍
Please also check the discussions and notebook submissions for different approaches and solution after you tried yourself.
1. Basic python and statistics
Pima Indians :- https://www.kaggle.com/uciml/pima-indians-diabetes-database
Cardio Goodness fit :- https://www.kaggle.com/saurav9786/cardiogoodfitness
Automobile :- https://www.kaggle.com/toramky/automobile-dataset
2. Advanced Statistics
Game of Thrones:-https://www.kaggle.com/mylesoneill/game-of-thrones
World University Ranking:-https://www.kaggle.com/mylesoneill/world-university-rankings
IMDB Movie Dataset:- https://www.kaggle.com/carolzhangdc/imdb-5000-movie-dataset
3. Supervised Learning
a) Regression Problems
How much did it rain :- https://www.kaggle.com/c/how-much-did-it-rain-ii/overview
Inventory Demand:- https://www.kaggle.com/c/grupo-bimbo-inventory-demand
Property Inspection predictiion:- https://www.kaggle.com/c/liberty-mutual-group-property-inspection-prediction
Restaurant Revenue prediction:- https://www.kaggle.com/c/restaurant-revenue-prediction/data
IMDB Box office Prediction:-https://www.kaggle.com/c/tmdb-box-office-prediction/overview
b) Classification problems
Employee Access challenge :- https://www.kaggle.com/c/amazon-employee-access-challenge/overview
Titanic :- https://www.kaggle.com/c/titanic
San Francisco crime:- https://www.kaggle.com/c/sf-crime
Customer satisfcation:-https://www.kaggle.com/c/santander-customer-satisfaction
Trip type classification:- https://www.kaggle.com/c/walmart-recruiting-trip-type-classification
Categorize cusine:- https://www.kaggle.com/c/whats-cooking
4. Some helpful Data science projects for beginners
https://www.kaggle.com/c/house-prices-advanced-regression-techniques
https://www.kaggle.com/c/digit-recognizer
https://www.kaggle.com/c/titanic
5. Intermediate Level Data science Projects
Black Friday Data : https://www.kaggle.com/sdolezel/black-friday
Human Activity Recognition Data : https://www.kaggle.com/uciml/human-activity-recognition-with-smartphones
Trip History Data : https://www.kaggle.com/pronto/cycle-share-dataset
Million Song Data : https://www.kaggle.com/c/msdchallenge
Census Income Data : https://www.kaggle.com/c/census-income/data
Movie Lens Data : https://www.kaggle.com/grouplens/movielens-20m-dataset
Twitter Classification Data : https://www.kaggle.com/c/twitter-sentiment-analysis2
Share with credits: https://t.iss.one/sqlproject
ENJOY LEARNING 👍👍
👍6
Here are 10 popular programming languages based on versatile, widely-used, and in-demand languages:
1. Python – Ideal for beginners and professionals; used in web development, data analysis, AI, and more.
2. Java – A classic language for building enterprise applications, Android apps, and large-scale systems.
3. C – The foundation for many other languages; great for understanding low-level programming concepts.
4. C++ – Popular for game development, competitive programming, and performance-critical applications.
5. C# – Widely used for Windows applications, game development (Unity), and enterprise software.
6. Go (Golang) – A modern language designed for performance and scalability, popular in cloud services.
7. Rust – Known for its safety and performance, ideal for system-level programming.
8. Kotlin – The preferred language for Android development with modern features.
9. Swift – Used for developing iOS and macOS applications with simplicity and power.
10. PHP – A staple for web development, powering many websites and applications
1. Python – Ideal for beginners and professionals; used in web development, data analysis, AI, and more.
2. Java – A classic language for building enterprise applications, Android apps, and large-scale systems.
3. C – The foundation for many other languages; great for understanding low-level programming concepts.
4. C++ – Popular for game development, competitive programming, and performance-critical applications.
5. C# – Widely used for Windows applications, game development (Unity), and enterprise software.
6. Go (Golang) – A modern language designed for performance and scalability, popular in cloud services.
7. Rust – Known for its safety and performance, ideal for system-level programming.
8. Kotlin – The preferred language for Android development with modern features.
9. Swift – Used for developing iOS and macOS applications with simplicity and power.
10. PHP – A staple for web development, powering many websites and applications
👍7❤1
Check out the list of top 10 Python projects on GitHub given below.
1. Magenta: Explore the artist inside you with this python project. A Google Brain’s brainchild, it leverages deep learning and reinforcement learning algorithms to create drawings, music, and other similar artistic products.
2. Photon: Designing web crawlers can be fun with the Photon project. It is a fast crawler designed for open-source intelligence tools. Photon project helps you perform data crawling functions, which include extracting data from URLs, e-mails, social media accounts, XML and pdf files, and Amazon buckets.
3. Mail Pile: Want to learn some encrypting tricks? This project on GitHub can help you learn to send and receive PGP encrypted electronic mails. Powered by Bayesian classifiers, it is capable of automatic tagging and handling huge volumes of email data, all organized in a clean web interface.
4. XS Strike: XS Strike helps you design a vulnerability to check your network’s security. It is a security suite developed to detect vulnerability attacks. XSS attacks inject malicious scripts into web pages. XSS’s features include four handwritten parsers, a payload generator, a fuzzing engine, and a fast crawler.
5. Google Images Download: It is a script that looks for keywords and phrases to optionally download the image files. All you need to do is, replicate the source code of this project to get a sense of how it works in practice.
6. Pandas Project: Pandas library is a collection of data structures that can be used for flexible data analysis and data manipulation. Compared to other libraries, its flexibility, intuitiveness, and automated data manipulation processes make it a better choice for data manipulation.
7. Xonsh: Used for designing interactive applications without the need for command-line interpreters like Unix. It is a Python-powered Shell language that commands promptly. An easily scriptable application that comes with a standard library, and various types of variables and has its own virtual environment management system.
8. Manim: The Mathematical Animation Engine, Manim, can create video explainers. Using Python 3.7, it produces animated videos, with added illustrations and display graphs. Its source code is freely available on GitHub and for tutorials and installation guides, you can refer to their 3Blue1Brown YouTube channel.
9. AI Basketball Analysis: It is an artificial intelligence application that analyses basketball shots using an object detection concept. All you need to do is upload the files or submit them as a post requests to the API. Then the OpenPose library carries out the calculations to generate the results.
10. Rebound: A great project to put Python to use in building Stackoverflow content, this tool is built on the Urwid console user interface, and solves compiler errors. Using this tool, you can learn how the Beautiful Soup package scrapes StackOverflow and how subprocesses work to find compiler errors.
1. Magenta: Explore the artist inside you with this python project. A Google Brain’s brainchild, it leverages deep learning and reinforcement learning algorithms to create drawings, music, and other similar artistic products.
2. Photon: Designing web crawlers can be fun with the Photon project. It is a fast crawler designed for open-source intelligence tools. Photon project helps you perform data crawling functions, which include extracting data from URLs, e-mails, social media accounts, XML and pdf files, and Amazon buckets.
3. Mail Pile: Want to learn some encrypting tricks? This project on GitHub can help you learn to send and receive PGP encrypted electronic mails. Powered by Bayesian classifiers, it is capable of automatic tagging and handling huge volumes of email data, all organized in a clean web interface.
4. XS Strike: XS Strike helps you design a vulnerability to check your network’s security. It is a security suite developed to detect vulnerability attacks. XSS attacks inject malicious scripts into web pages. XSS’s features include four handwritten parsers, a payload generator, a fuzzing engine, and a fast crawler.
5. Google Images Download: It is a script that looks for keywords and phrases to optionally download the image files. All you need to do is, replicate the source code of this project to get a sense of how it works in practice.
6. Pandas Project: Pandas library is a collection of data structures that can be used for flexible data analysis and data manipulation. Compared to other libraries, its flexibility, intuitiveness, and automated data manipulation processes make it a better choice for data manipulation.
7. Xonsh: Used for designing interactive applications without the need for command-line interpreters like Unix. It is a Python-powered Shell language that commands promptly. An easily scriptable application that comes with a standard library, and various types of variables and has its own virtual environment management system.
8. Manim: The Mathematical Animation Engine, Manim, can create video explainers. Using Python 3.7, it produces animated videos, with added illustrations and display graphs. Its source code is freely available on GitHub and for tutorials and installation guides, you can refer to their 3Blue1Brown YouTube channel.
9. AI Basketball Analysis: It is an artificial intelligence application that analyses basketball shots using an object detection concept. All you need to do is upload the files or submit them as a post requests to the API. Then the OpenPose library carries out the calculations to generate the results.
10. Rebound: A great project to put Python to use in building Stackoverflow content, this tool is built on the Urwid console user interface, and solves compiler errors. Using this tool, you can learn how the Beautiful Soup package scrapes StackOverflow and how subprocesses work to find compiler errors.
👍1
--- Git Commands ---
🏗️ git init | Initialize a new Git repository
🔄 git clone <repo> | Clone a repository
📊 git status | Check the status of your repository
➕ git add <file> | Add a file to the staging area
📝 git commit -m "message" | Commit changes with a message
🚀 git push | Push changes to a remote repository
⬇️ git pull | Fetch and merge changes from a remote repository
--- Branching ---
🌿 git branch | List branches
🌱 git branch <name> | Create a new branch
🔀 git checkout <branch> | Switch to a branch
🔧 git merge <branch> | Merge a branch into the current branch
🔄 git rebase <branch> | Reapply commits on top of another base branch
--- Undo & Fix Mistakes ---
🔙 git reset --soft HEAD~1 | Undo last commit but keep changes
🚫 git reset --hard HEAD-1 | Undo last commit and discard changes
⏪ git revert <commit> | Create a new commit that undoes changes from a specific commit
--- Logs & History ---
📜 git log | Show commit history
🌐 git log --oneline --graph --all | Pretty graph of commit history
--- Stashing ---
🎒 git stash | Save changes without committing
🎭 git stash pop | Apply stashed changes and remove them from stash
--- Remote & Collaboration ---
🌍 git remote -v | View remote repositories
📡 git fetch | Fetch changes without merging
🕵️ git diff | Compare changes
🏗️ git init | Initialize a new Git repository
🔄 git clone <repo> | Clone a repository
📊 git status | Check the status of your repository
➕ git add <file> | Add a file to the staging area
📝 git commit -m "message" | Commit changes with a message
🚀 git push | Push changes to a remote repository
⬇️ git pull | Fetch and merge changes from a remote repository
--- Branching ---
🌿 git branch | List branches
🌱 git branch <name> | Create a new branch
🔀 git checkout <branch> | Switch to a branch
🔧 git merge <branch> | Merge a branch into the current branch
🔄 git rebase <branch> | Reapply commits on top of another base branch
--- Undo & Fix Mistakes ---
🔙 git reset --soft HEAD~1 | Undo last commit but keep changes
🚫 git reset --hard HEAD-1 | Undo last commit and discard changes
⏪ git revert <commit> | Create a new commit that undoes changes from a specific commit
--- Logs & History ---
📜 git log | Show commit history
🌐 git log --oneline --graph --all | Pretty graph of commit history
--- Stashing ---
🎒 git stash | Save changes without committing
🎭 git stash pop | Apply stashed changes and remove them from stash
--- Remote & Collaboration ---
🌍 git remote -v | View remote repositories
📡 git fetch | Fetch changes without merging
🕵️ git diff | Compare changes
👍7😁2
Where Each Programming Language Shines 🚀👨🏻💻
❯ C ➟ OS Development, Embedded Systems, Game Engines
❯ C++ ➟ Game Development, High-Performance Applications, Financial Systems
❯ Java ➟ Enterprise Software, Android Development, Backend Systems
❯ C# ➟ Game Development (Unity), Windows Applications, Enterprise Software
❯ Python ➟ AI/ML, Data Science, Web Development, Automation
❯ JavaScript ➟ Frontend Web Development, Full-Stack Apps, Game Development
❯ Golang ➟ Cloud Services, Networking, High-Performance APIs
❯ Swift ➟ iOS/macOS App Development
❯ Kotlin ➟ Android Development, Backend Services
❯ PHP ➟ Web Development (WordPress, Laravel)
❯ Ruby ➟ Web Development (Ruby on Rails), Prototyping
❯ Rust ➟ Systems Programming, High-Performance Computing, Blockchain
❯ Lua ➟ Game Scripting (Roblox, WoW), Embedded Systems
❯ R ➟ Data Science, Statistics, Bioinformatics
❯ SQL ➟ Database Management, Data Analytics
❯ TypeScript ➟ Scalable Web Applications, Large JavaScript Projects
❯ Node.js ➟ Backend Development, Real-Time Applications
❯ React ➟ Modern Web Applications, Interactive UIs
❯ Vue ➟ Lightweight Frontend Development, SPAs
❯ Django ➟ Scalable Web Applications, AI/ML Backend
❯ Laravel ➟ Full-Stack PHP Development
❯ Blazor ➟ Web Apps with .NET
❯ Spring Boot ➟ Enterprise Java Applications, Microservices
❯ Ruby on Rails ➟ Startup Web Apps, MVP Development
❯ HTML/CSS ➟ Web Design, UI Development
❯ GIT ➟ Version Control, Collaboration
❯ Linux ➟ Server Management, Security, DevOps
❯ DevOps ➟ Infrastructure Automation, CI/CD
❯ CI/CD ➟ Continuous Deployment & Testing
❯ Docker ➟ Containerization, Cloud Deployments
❯ Kubernetes ➟ Scalable Cloud Orchestration
❯ Microservices ➟ Distributed Systems, Scalable Backends
❯ Selenium ➟ Web Automation Testing
❯ Playwright ➟ Modern Browser Automation
React ❤️ for more
❯ C ➟ OS Development, Embedded Systems, Game Engines
❯ C++ ➟ Game Development, High-Performance Applications, Financial Systems
❯ Java ➟ Enterprise Software, Android Development, Backend Systems
❯ C# ➟ Game Development (Unity), Windows Applications, Enterprise Software
❯ Python ➟ AI/ML, Data Science, Web Development, Automation
❯ JavaScript ➟ Frontend Web Development, Full-Stack Apps, Game Development
❯ Golang ➟ Cloud Services, Networking, High-Performance APIs
❯ Swift ➟ iOS/macOS App Development
❯ Kotlin ➟ Android Development, Backend Services
❯ PHP ➟ Web Development (WordPress, Laravel)
❯ Ruby ➟ Web Development (Ruby on Rails), Prototyping
❯ Rust ➟ Systems Programming, High-Performance Computing, Blockchain
❯ Lua ➟ Game Scripting (Roblox, WoW), Embedded Systems
❯ R ➟ Data Science, Statistics, Bioinformatics
❯ SQL ➟ Database Management, Data Analytics
❯ TypeScript ➟ Scalable Web Applications, Large JavaScript Projects
❯ Node.js ➟ Backend Development, Real-Time Applications
❯ React ➟ Modern Web Applications, Interactive UIs
❯ Vue ➟ Lightweight Frontend Development, SPAs
❯ Django ➟ Scalable Web Applications, AI/ML Backend
❯ Laravel ➟ Full-Stack PHP Development
❯ Blazor ➟ Web Apps with .NET
❯ Spring Boot ➟ Enterprise Java Applications, Microservices
❯ Ruby on Rails ➟ Startup Web Apps, MVP Development
❯ HTML/CSS ➟ Web Design, UI Development
❯ GIT ➟ Version Control, Collaboration
❯ Linux ➟ Server Management, Security, DevOps
❯ DevOps ➟ Infrastructure Automation, CI/CD
❯ CI/CD ➟ Continuous Deployment & Testing
❯ Docker ➟ Containerization, Cloud Deployments
❯ Kubernetes ➟ Scalable Cloud Orchestration
❯ Microservices ➟ Distributed Systems, Scalable Backends
❯ Selenium ➟ Web Automation Testing
❯ Playwright ➟ Modern Browser Automation
React ❤️ for more
👍15❤5🥰1
Essential Programming Languages to Learn Data Science 👇👇
1. Python: Python is one of the most popular programming languages for data science due to its simplicity, versatility, and extensive library support (such as NumPy, Pandas, and Scikit-learn).
2. R: R is another popular language for data science, particularly in academia and research settings. It has powerful statistical analysis capabilities and a wide range of packages for data manipulation and visualization.
3. SQL: SQL (Structured Query Language) is essential for working with databases, which are a critical component of data science projects. Knowledge of SQL is necessary for querying and manipulating data stored in relational databases.
4. Java: Java is a versatile language that is widely used in enterprise applications and big data processing frameworks like Apache Hadoop and Apache Spark. Knowledge of Java can be beneficial for working with large-scale data processing systems.
5. Scala: Scala is a functional programming language that is often used in conjunction with Apache Spark for distributed data processing. Knowledge of Scala can be valuable for building high-performance data processing applications.
6. Julia: Julia is a high-performance language specifically designed for scientific computing and data analysis. It is gaining popularity in the data science community due to its speed and ease of use for numerical computations.
7. MATLAB: MATLAB is a proprietary programming language commonly used in engineering and scientific research for data analysis, visualization, and modeling. It is particularly useful for signal processing and image analysis tasks.
Free Resources to master data analytics concepts 👇👇
Data Analysis with R
Intro to Data Science
Practical Python Programming
SQL for Data Analysis
Java Essential Concepts
Machine Learning with Python
Data Science Project Ideas
Learning SQL FREE Book
Join @free4unow_backup for more free resources.
ENJOY LEARNING👍👍
1. Python: Python is one of the most popular programming languages for data science due to its simplicity, versatility, and extensive library support (such as NumPy, Pandas, and Scikit-learn).
2. R: R is another popular language for data science, particularly in academia and research settings. It has powerful statistical analysis capabilities and a wide range of packages for data manipulation and visualization.
3. SQL: SQL (Structured Query Language) is essential for working with databases, which are a critical component of data science projects. Knowledge of SQL is necessary for querying and manipulating data stored in relational databases.
4. Java: Java is a versatile language that is widely used in enterprise applications and big data processing frameworks like Apache Hadoop and Apache Spark. Knowledge of Java can be beneficial for working with large-scale data processing systems.
5. Scala: Scala is a functional programming language that is often used in conjunction with Apache Spark for distributed data processing. Knowledge of Scala can be valuable for building high-performance data processing applications.
6. Julia: Julia is a high-performance language specifically designed for scientific computing and data analysis. It is gaining popularity in the data science community due to its speed and ease of use for numerical computations.
7. MATLAB: MATLAB is a proprietary programming language commonly used in engineering and scientific research for data analysis, visualization, and modeling. It is particularly useful for signal processing and image analysis tasks.
Free Resources to master data analytics concepts 👇👇
Data Analysis with R
Intro to Data Science
Practical Python Programming
SQL for Data Analysis
Java Essential Concepts
Machine Learning with Python
Data Science Project Ideas
Learning SQL FREE Book
Join @free4unow_backup for more free resources.
ENJOY LEARNING👍👍
👍7❤1
Your Roadmap to be a Full Stack Developer in 1 Year
↓ HTML/CSS → 45 Days
↓ JavaScript + DOM → 45 Days
↓ React → 20 Days
↓ Next.js → 30 Days
↓ Java/Golang/Python/Node.js → 45 Days
↓ Spring/Django/Express → 30 Days
↓ GraphQL → 30 Days
↓ PostgreSQL/MySQL/MongoDB → 30 Days
↓ [Any of] Docker/K8S/Kafka/Redis → 30 Days
↓ Cloud Computing → 20 Days
↓ Build an End-to-End Project → 40 Days
Tip: • Start with projects and enhance it step by step.
📂 Web Development Resources
ENJOY LEARNING 👍👍
↓ HTML/CSS → 45 Days
↓ JavaScript + DOM → 45 Days
↓ React → 20 Days
↓ Next.js → 30 Days
↓ Java/Golang/Python/Node.js → 45 Days
↓ Spring/Django/Express → 30 Days
↓ GraphQL → 30 Days
↓ PostgreSQL/MySQL/MongoDB → 30 Days
↓ [Any of] Docker/K8S/Kafka/Redis → 30 Days
↓ Cloud Computing → 20 Days
↓ Build an End-to-End Project → 40 Days
Tip: • Start with projects and enhance it step by step.
📂 Web Development Resources
ENJOY LEARNING 👍👍
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Advanced SQL Optimization Tips for Data Analysts
Use Proper Indexing: Create indexes for frequently queried columns.
Avoid SELECT *: Specify only required columns to improve performance.
Use WHERE Instead of HAVING: Filter data early in the query.
Limit Joins: Avoid excessive joins to reduce query complexity.
Apply LIMIT or TOP: Retrieve only the required rows.
Optimize Joins: Use INNER JOIN over OUTER JOIN where applicable.
Use Temporary Tables: Break complex queries into smaller parts.
Avoid Functions on Indexed Columns: It prevents index usage.
Use CTEs for Readability: Simplify nested queries using Common Table Expressions.
Analyze Execution Plans: Identify bottlenecks and optimize queries.
Here you can find SQL Interview Resources👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Like this post if you need more 👍❤️
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
Use Proper Indexing: Create indexes for frequently queried columns.
Avoid SELECT *: Specify only required columns to improve performance.
Use WHERE Instead of HAVING: Filter data early in the query.
Limit Joins: Avoid excessive joins to reduce query complexity.
Apply LIMIT or TOP: Retrieve only the required rows.
Optimize Joins: Use INNER JOIN over OUTER JOIN where applicable.
Use Temporary Tables: Break complex queries into smaller parts.
Avoid Functions on Indexed Columns: It prevents index usage.
Use CTEs for Readability: Simplify nested queries using Common Table Expressions.
Analyze Execution Plans: Identify bottlenecks and optimize queries.
Here you can find SQL Interview Resources👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Like this post if you need more 👍❤️
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
👍2
Common Programming Interview Questions
How do you reverse a string?
How do you determine if a string is a palindrome?
How do you calculate the number of numerical digits in a string?
How do you find the count for the occurrence of a particular character in a string?
How do you find the non-matching characters in a string?
How do you find out if the two given strings are anagrams?
How do you calculate the number of vowels and consonants in a string?
How do you total all of the matching integer elements in an array?
How do you reverse an array?
How do you find the maximum element in an array?
How do you sort an array of integers in ascending order?
How do you print a Fibonacci sequence using recursion?
How do you calculate the sum of two integers?
How do you find the average of numbers in a list?
How do you check if an integer is even or odd?
How do you find the middle element of a linked list?
How do you remove a loop in a linked list?
How do you merge two sorted linked lists?
How do you implement binary search to find an element in a sorted array?
How do you print a binary tree in vertical order?
Conceptual Coding Interview Questions
What is a data structure?
What is an array?
What is a linked list?
What is the difference between an array and a linked list?
What is LIFO?
What is FIFO?
What is a stack?
What are binary trees?
What are binary search trees?
What is object-oriented programming?
What is the purpose of a loop in programming?
What is a conditional statement?
What is debugging?
What is recursion?
What are the differences between linear and non-linear data structures?
General Coding Interview Questions
What programming languages do you have experience working with?
Describe a time you faced a challenge in a project you were working on and how you overcame it.
Walk me through a project you’re currently or have recently worked on.
Give an example of a project you worked on where you had to learn a new programming language or technology. How did you go about learning it?
How do you ensure your code is readable by other developers?
What are your interests outside of programming?
How do you keep your skills sharp and up to date?
How do you collaborate on projects with non-technical team members?
Tell me about a time when you had to explain a complex technical concept to a non-technical team member.
How do you get started on a new coding project?
Best Programming Resources: https://topmate.io/coding/886839
Join for more: https://t.iss.one/programming_guide
ENJOY LEARNING 👍👍
How do you reverse a string?
How do you determine if a string is a palindrome?
How do you calculate the number of numerical digits in a string?
How do you find the count for the occurrence of a particular character in a string?
How do you find the non-matching characters in a string?
How do you find out if the two given strings are anagrams?
How do you calculate the number of vowels and consonants in a string?
How do you total all of the matching integer elements in an array?
How do you reverse an array?
How do you find the maximum element in an array?
How do you sort an array of integers in ascending order?
How do you print a Fibonacci sequence using recursion?
How do you calculate the sum of two integers?
How do you find the average of numbers in a list?
How do you check if an integer is even or odd?
How do you find the middle element of a linked list?
How do you remove a loop in a linked list?
How do you merge two sorted linked lists?
How do you implement binary search to find an element in a sorted array?
How do you print a binary tree in vertical order?
Conceptual Coding Interview Questions
What is a data structure?
What is an array?
What is a linked list?
What is the difference between an array and a linked list?
What is LIFO?
What is FIFO?
What is a stack?
What are binary trees?
What are binary search trees?
What is object-oriented programming?
What is the purpose of a loop in programming?
What is a conditional statement?
What is debugging?
What is recursion?
What are the differences between linear and non-linear data structures?
General Coding Interview Questions
What programming languages do you have experience working with?
Describe a time you faced a challenge in a project you were working on and how you overcame it.
Walk me through a project you’re currently or have recently worked on.
Give an example of a project you worked on where you had to learn a new programming language or technology. How did you go about learning it?
How do you ensure your code is readable by other developers?
What are your interests outside of programming?
How do you keep your skills sharp and up to date?
How do you collaborate on projects with non-technical team members?
Tell me about a time when you had to explain a complex technical concept to a non-technical team member.
How do you get started on a new coding project?
Best Programming Resources: https://topmate.io/coding/886839
Join for more: https://t.iss.one/programming_guide
ENJOY LEARNING 👍👍
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Prepare for placement season in 6 months
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Getting job offers as a developer involves several steps:👨💻🚀
1. Build a Strong Portfolio: Create a portfolio of projects that showcase your skills. Include personal projects, open-source contributions, or freelance work. This demonstrates your abilities to potential employers.👨💻
2. Enhance Your Skills: Stay updated with the latest technologies and trends in your field. Consider taking online courses, attending workshops, or earning certifications to bolster your skills.🚀
3. Network: Attend industry events, conferences, and meetups to connect with professionals in your field. Utilize social media platforms like LinkedIn to build a professional network.🔥
4. Resume and Cover Letter: Craft a tailored resume and cover letter for each job application. Highlight relevant skills and experiences that match the job requirements.📇
5. Job Search Platforms: Utilize job search websites like LinkedIn, Indeed, Glassdoor, and specialized platforms like Stack Overflow Jobs, GitHub Jobs, or AngelList for tech-related positions. 🔍
6. Company Research: Research companies you're interested in working for. Customize your application to show your genuine interest in their mission and values.🕵️♂️
7. Prepare for Interviews: Be ready for technical interviews. Practice coding challenges, algorithms, and data structures. Also, be prepared to discuss your past projects and problem-solving skills.📝
8. Soft Skills: Develop your soft skills like communication, teamwork, and problem-solving. Employers often look for candidates who can work well in a team and communicate effectively.💻
9. Internships and Freelancing: Consider internships or freelancing opportunities to gain practical experience and build your resume. 🏠
10. Personal Branding: Maintain an online presence by sharing your work, insights, and thoughts on platforms like GitHub, personal blogs, or social media. This can help you get noticed by potential employers.👦
11. Referrals: Reach out to your network and ask for referrals from people you know in the industry. Employee referrals are often highly valued by companies.🌈
12. Persistence: The job search process can be challenging. Don't get discouraged by rejections. Keep applying, learning, and improving your skills.💯
13. Negotiate Offers: When you receive job offers, negotiate your salary and benefits. Research industry standards and be prepared to discuss your expectations.📉
Remember that the job search process can take time, so patience is key. By focusing on these steps and continuously improving your skills and network, you can increase your chances of receiving job offers as a developer.
1. Build a Strong Portfolio: Create a portfolio of projects that showcase your skills. Include personal projects, open-source contributions, or freelance work. This demonstrates your abilities to potential employers.👨💻
2. Enhance Your Skills: Stay updated with the latest technologies and trends in your field. Consider taking online courses, attending workshops, or earning certifications to bolster your skills.🚀
3. Network: Attend industry events, conferences, and meetups to connect with professionals in your field. Utilize social media platforms like LinkedIn to build a professional network.🔥
4. Resume and Cover Letter: Craft a tailored resume and cover letter for each job application. Highlight relevant skills and experiences that match the job requirements.📇
5. Job Search Platforms: Utilize job search websites like LinkedIn, Indeed, Glassdoor, and specialized platforms like Stack Overflow Jobs, GitHub Jobs, or AngelList for tech-related positions. 🔍
6. Company Research: Research companies you're interested in working for. Customize your application to show your genuine interest in their mission and values.🕵️♂️
7. Prepare for Interviews: Be ready for technical interviews. Practice coding challenges, algorithms, and data structures. Also, be prepared to discuss your past projects and problem-solving skills.📝
8. Soft Skills: Develop your soft skills like communication, teamwork, and problem-solving. Employers often look for candidates who can work well in a team and communicate effectively.💻
9. Internships and Freelancing: Consider internships or freelancing opportunities to gain practical experience and build your resume. 🏠
10. Personal Branding: Maintain an online presence by sharing your work, insights, and thoughts on platforms like GitHub, personal blogs, or social media. This can help you get noticed by potential employers.👦
11. Referrals: Reach out to your network and ask for referrals from people you know in the industry. Employee referrals are often highly valued by companies.🌈
12. Persistence: The job search process can be challenging. Don't get discouraged by rejections. Keep applying, learning, and improving your skills.💯
13. Negotiate Offers: When you receive job offers, negotiate your salary and benefits. Research industry standards and be prepared to discuss your expectations.📉
Remember that the job search process can take time, so patience is key. By focusing on these steps and continuously improving your skills and network, you can increase your chances of receiving job offers as a developer.
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