Interview questions asked by top product-based companies.
A friend of mine recently shared their interview journey, and I'd like to pass on what I learned about the data structures and algorithms (DSA) rounds.
๐จ๐พโ๐ป Data Structures: He encountered questions on topics like arrays, strings, matrices, stacks, queues, and different types of linked lists (singly, doubly, and circular).
โถ๏ธ Algorithms: He was also interviewed on a wide array of algorithms like linear search, binary search, and sorting algorithms (bubble, quick, merge).
And faced questions on more challenging subjects like Greedy algorithms, Dynamic programming, and Graph algorithms.
๐ Specifics: The devil lies in the details! His interview also delved into advanced topics such as Advanced Data Structures, Pattern Searching, Recursion, Backtracking, and Divide and Conquer strategies.
However, your ability to apply these concepts to real-world situations will undoubtedly set you apart from others.
On top, If youโre stuck at any of the above questions and need the right guidance in cracking top product-based company interviews,
As a community of tech enthusiasts, let's share our own interview experiences in the comments below. Together, we can learn from each other's experiences.
A friend of mine recently shared their interview journey, and I'd like to pass on what I learned about the data structures and algorithms (DSA) rounds.
๐จ๐พโ๐ป Data Structures: He encountered questions on topics like arrays, strings, matrices, stacks, queues, and different types of linked lists (singly, doubly, and circular).
โถ๏ธ Algorithms: He was also interviewed on a wide array of algorithms like linear search, binary search, and sorting algorithms (bubble, quick, merge).
And faced questions on more challenging subjects like Greedy algorithms, Dynamic programming, and Graph algorithms.
๐ Specifics: The devil lies in the details! His interview also delved into advanced topics such as Advanced Data Structures, Pattern Searching, Recursion, Backtracking, and Divide and Conquer strategies.
However, your ability to apply these concepts to real-world situations will undoubtedly set you apart from others.
On top, If youโre stuck at any of the above questions and need the right guidance in cracking top product-based company interviews,
As a community of tech enthusiasts, let's share our own interview experiences in the comments below. Together, we can learn from each other's experiences.
๐3
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In collaboration with Accenture and with GeeksforGeeks as the Community Partner, this event offers a unique opportunity to collaborate, learn, and innovate.
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Top 10 important data science concepts
1. Data Cleaning: Data cleaning is the process of identifying and correcting or removing errors, inconsistencies, and inaccuracies in a dataset. It is a crucial step in the data science pipeline as it ensures the quality and reliability of the data.
2. Exploratory Data Analysis (EDA): EDA is the process of analyzing and visualizing data to gain insights and understand the underlying patterns and relationships. It involves techniques such as summary statistics, data visualization, and correlation analysis.
3. Feature Engineering: Feature engineering is the process of creating new features or transforming existing features in a dataset to improve the performance of machine learning models. It involves techniques such as encoding categorical variables, scaling numerical variables, and creating interaction terms.
4. Machine Learning Algorithms: Machine learning algorithms are mathematical models that learn patterns and relationships from data to make predictions or decisions. Some important machine learning algorithms include linear regression, logistic regression, decision trees, random forests, support vector machines, and neural networks.
5. Model Evaluation and Validation: Model evaluation and validation involve assessing the performance of machine learning models on unseen data. It includes techniques such as cross-validation, confusion matrix, precision, recall, F1 score, and ROC curve analysis.
6. Feature Selection: Feature selection is the process of selecting the most relevant features from a dataset to improve model performance and reduce overfitting. It involves techniques such as correlation analysis, backward elimination, forward selection, and regularization methods.
7. Dimensionality Reduction: Dimensionality reduction techniques are used to reduce the number of features in a dataset while preserving the most important information. Principal Component Analysis (PCA) and t-SNE (t-Distributed Stochastic Neighbor Embedding) are common dimensionality reduction techniques.
8. Model Optimization: Model optimization involves fine-tuning the parameters and hyperparameters of machine learning models to achieve the best performance. Techniques such as grid search, random search, and Bayesian optimization are used for model optimization.
9. Data Visualization: Data visualization is the graphical representation of data to communicate insights and patterns effectively. It involves using charts, graphs, and plots to present data in a visually appealing and understandable manner.
10. Big Data Analytics: Big data analytics refers to the process of analyzing large and complex datasets that cannot be processed using traditional data processing techniques. It involves technologies such as Hadoop, Spark, and distributed computing to extract insights from massive amounts of data.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
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Like if you need similar content ๐๐
Hope this helps you ๐
1. Data Cleaning: Data cleaning is the process of identifying and correcting or removing errors, inconsistencies, and inaccuracies in a dataset. It is a crucial step in the data science pipeline as it ensures the quality and reliability of the data.
2. Exploratory Data Analysis (EDA): EDA is the process of analyzing and visualizing data to gain insights and understand the underlying patterns and relationships. It involves techniques such as summary statistics, data visualization, and correlation analysis.
3. Feature Engineering: Feature engineering is the process of creating new features or transforming existing features in a dataset to improve the performance of machine learning models. It involves techniques such as encoding categorical variables, scaling numerical variables, and creating interaction terms.
4. Machine Learning Algorithms: Machine learning algorithms are mathematical models that learn patterns and relationships from data to make predictions or decisions. Some important machine learning algorithms include linear regression, logistic regression, decision trees, random forests, support vector machines, and neural networks.
5. Model Evaluation and Validation: Model evaluation and validation involve assessing the performance of machine learning models on unseen data. It includes techniques such as cross-validation, confusion matrix, precision, recall, F1 score, and ROC curve analysis.
6. Feature Selection: Feature selection is the process of selecting the most relevant features from a dataset to improve model performance and reduce overfitting. It involves techniques such as correlation analysis, backward elimination, forward selection, and regularization methods.
7. Dimensionality Reduction: Dimensionality reduction techniques are used to reduce the number of features in a dataset while preserving the most important information. Principal Component Analysis (PCA) and t-SNE (t-Distributed Stochastic Neighbor Embedding) are common dimensionality reduction techniques.
8. Model Optimization: Model optimization involves fine-tuning the parameters and hyperparameters of machine learning models to achieve the best performance. Techniques such as grid search, random search, and Bayesian optimization are used for model optimization.
9. Data Visualization: Data visualization is the graphical representation of data to communicate insights and patterns effectively. It involves using charts, graphs, and plots to present data in a visually appealing and understandable manner.
10. Big Data Analytics: Big data analytics refers to the process of analyzing large and complex datasets that cannot be processed using traditional data processing techniques. It involves technologies such as Hadoop, Spark, and distributed computing to extract insights from massive amounts of data.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
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Like if you need similar content ๐๐
Hope this helps you ๐
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Complete Data Science Roadmap
๐๐
1. Introduction to Data Science
- Overview and Importance
- Data Science Lifecycle
- Key Roles (Data Scientist, Analyst, Engineer)
2. Mathematics and Statistics
- Probability and Distributions
- Descriptive/Inferential Statistics
- Hypothesis Testing
- Linear Algebra and Calculus Basics
3. Programming Languages
- Python: NumPy, Pandas, Matplotlib
- R: dplyr, ggplot2
- SQL: Joins, Aggregations, CRUD
4. Data Collection & Preprocessing
- Data Cleaning and Wrangling
- Handling Missing Data
- Feature Engineering
5. Exploratory Data Analysis (EDA)
- Summary Statistics
- Data Visualization (Histograms, Box Plots, Correlation)
6. Machine Learning
- Supervised (Linear/Logistic Regression, Decision Trees)
- Unsupervised (K-Means, PCA)
- Model Selection and Cross-Validation
7. Advanced Machine Learning
- SVM, Random Forests, Boosting
- Neural Networks Basics
8. Deep Learning
- Neural Networks Architecture
- CNNs for Image Data
- RNNs for Sequential Data
9. Natural Language Processing (NLP)
- Text Preprocessing
- Sentiment Analysis
- Word Embeddings (Word2Vec)
10. Data Visualization & Storytelling
- Dashboards (Tableau, Power BI)
- Telling Stories with Data
11. Model Deployment
- Deploy with Flask or Django
- Monitoring and Retraining Models
12. Big Data & Cloud
- Introduction to Hadoop, Spark
- Cloud Tools (AWS, Google Cloud)
13. Data Engineering Basics
- ETL Pipelines
- Data Warehousing (Redshift, BigQuery)
14. Ethics in Data Science
- Ethical Data Usage
- Bias in AI Models
15. Tools for Data Science
- Jupyter, Git, Docker
16. Career Path & Certifications
- Building a Data Science Portfolio
Like if you need similar content ๐๐
๐๐
1. Introduction to Data Science
- Overview and Importance
- Data Science Lifecycle
- Key Roles (Data Scientist, Analyst, Engineer)
2. Mathematics and Statistics
- Probability and Distributions
- Descriptive/Inferential Statistics
- Hypothesis Testing
- Linear Algebra and Calculus Basics
3. Programming Languages
- Python: NumPy, Pandas, Matplotlib
- R: dplyr, ggplot2
- SQL: Joins, Aggregations, CRUD
4. Data Collection & Preprocessing
- Data Cleaning and Wrangling
- Handling Missing Data
- Feature Engineering
5. Exploratory Data Analysis (EDA)
- Summary Statistics
- Data Visualization (Histograms, Box Plots, Correlation)
6. Machine Learning
- Supervised (Linear/Logistic Regression, Decision Trees)
- Unsupervised (K-Means, PCA)
- Model Selection and Cross-Validation
7. Advanced Machine Learning
- SVM, Random Forests, Boosting
- Neural Networks Basics
8. Deep Learning
- Neural Networks Architecture
- CNNs for Image Data
- RNNs for Sequential Data
9. Natural Language Processing (NLP)
- Text Preprocessing
- Sentiment Analysis
- Word Embeddings (Word2Vec)
10. Data Visualization & Storytelling
- Dashboards (Tableau, Power BI)
- Telling Stories with Data
11. Model Deployment
- Deploy with Flask or Django
- Monitoring and Retraining Models
12. Big Data & Cloud
- Introduction to Hadoop, Spark
- Cloud Tools (AWS, Google Cloud)
13. Data Engineering Basics
- ETL Pipelines
- Data Warehousing (Redshift, BigQuery)
14. Ethics in Data Science
- Ethical Data Usage
- Bias in AI Models
15. Tools for Data Science
- Jupyter, Git, Docker
16. Career Path & Certifications
- Building a Data Science Portfolio
Like if you need similar content ๐๐
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5 Handy Tips to Master Data Science โฌ๏ธ
1๏ธโฃ Begin with introductory projects that cover the fundamental concepts of data science, such as data exploration, cleaning, and visualization. These projects will help you get familiar with common data science tools and libraries like Python (Pandas, NumPy, Matplotlib), R, SQL, and Excel
2๏ธโฃ Look for publicly available datasets from sources like Kaggle, UCI Machine Learning Repository. Working with real-world data will expose you to the challenges of messy, incomplete, and heterogeneous data, which is common in practical scenarios.
3๏ธโฃ Explore various data science techniques like regression, classification, clustering, and time series analysis. Apply these techniques to different datasets and domains to gain a broader understanding of their strengths, weaknesses, and appropriate use cases.
4๏ธโฃ Work on projects that involve the entire data science lifecycle, from data collection and cleaning to model building, evaluation, and deployment. This will help you understand how different components of the data science process fit together.
5๏ธโฃ Consistent practice is key to mastering any skill. Set aside dedicated time to work on data science projects, and gradually increase the complexity and scope of your projects as you gain more experience.
1๏ธโฃ Begin with introductory projects that cover the fundamental concepts of data science, such as data exploration, cleaning, and visualization. These projects will help you get familiar with common data science tools and libraries like Python (Pandas, NumPy, Matplotlib), R, SQL, and Excel
2๏ธโฃ Look for publicly available datasets from sources like Kaggle, UCI Machine Learning Repository. Working with real-world data will expose you to the challenges of messy, incomplete, and heterogeneous data, which is common in practical scenarios.
3๏ธโฃ Explore various data science techniques like regression, classification, clustering, and time series analysis. Apply these techniques to different datasets and domains to gain a broader understanding of their strengths, weaknesses, and appropriate use cases.
4๏ธโฃ Work on projects that involve the entire data science lifecycle, from data collection and cleaning to model building, evaluation, and deployment. This will help you understand how different components of the data science process fit together.
5๏ธโฃ Consistent practice is key to mastering any skill. Set aside dedicated time to work on data science projects, and gradually increase the complexity and scope of your projects as you gain more experience.
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๐ Web development project ideas for beginners
Personal Portfolio Website: Create a website showcasing your skills, projects, and resume. This will help you practice HTML, CSS, and potentially some JavaScript for interactivity.
To-Do List App: Build a simple to-do list application using HTML, CSS, and JavaScript. You can gradually enhance it by adding features like task priority, due dates, and local storage.
Blog Platform: Create a basic blog platform where users can create, edit, and delete posts. This will give you experience with user authentication, databases, and CRUD operations.
E-commerce Website: Design a mock e-commerce site to learn about product listings, shopping carts, and checkout processes. This project will introduce you to handling user input and creating dynamic content.
Weather App: Develop a weather app that fetches data from a weather API and displays current conditions and forecasts. This project will involve API integration and working with JSON data.
Recipe Sharing Site: Build a platform where users can share and browse recipes. You can implement search functionality and user authentication to enhance the project.
Social Media Dashboard: Create a simplified social media dashboard that displays metrics like followers, likes, and comments. This project will help you practice data visualization and working with APIs.
Online Quiz App: Develop an online quiz application that lets users take quizzes on various topics. You can include features like multiple-choice questions, timers, and score tracking.
Personal Blog: Start your own blog by developing a content management system (CMS) where you can create, edit, and publish articles. This will give you hands-on experience with database management.
Event Countdown Timer: Build a countdown timer for upcoming events. You can make it interactive by allowing users to set their own event names and dates.
Remember, the key is to start small and gradually add complexity to your projects as you become more comfortable with different technologies concepts. These projects will not only showcase your skills to potential employers but also help you learn and grow as a web developer.
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ENJOY LEARNING ๐๐
Personal Portfolio Website: Create a website showcasing your skills, projects, and resume. This will help you practice HTML, CSS, and potentially some JavaScript for interactivity.
To-Do List App: Build a simple to-do list application using HTML, CSS, and JavaScript. You can gradually enhance it by adding features like task priority, due dates, and local storage.
Blog Platform: Create a basic blog platform where users can create, edit, and delete posts. This will give you experience with user authentication, databases, and CRUD operations.
E-commerce Website: Design a mock e-commerce site to learn about product listings, shopping carts, and checkout processes. This project will introduce you to handling user input and creating dynamic content.
Weather App: Develop a weather app that fetches data from a weather API and displays current conditions and forecasts. This project will involve API integration and working with JSON data.
Recipe Sharing Site: Build a platform where users can share and browse recipes. You can implement search functionality and user authentication to enhance the project.
Social Media Dashboard: Create a simplified social media dashboard that displays metrics like followers, likes, and comments. This project will help you practice data visualization and working with APIs.
Online Quiz App: Develop an online quiz application that lets users take quizzes on various topics. You can include features like multiple-choice questions, timers, and score tracking.
Personal Blog: Start your own blog by developing a content management system (CMS) where you can create, edit, and publish articles. This will give you hands-on experience with database management.
Event Countdown Timer: Build a countdown timer for upcoming events. You can make it interactive by allowing users to set their own event names and dates.
Remember, the key is to start small and gradually add complexity to your projects as you become more comfortable with different technologies concepts. These projects will not only showcase your skills to potential employers but also help you learn and grow as a web developer.
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Want to use ChatGPT at lightning speed?
You must tap in to ChatGPT's short cuts.
1. Go to ChatGPT
2. Bottom right '?' mark
3. Access keyboard shortcuts
Keyboard Shortcuts:
1. Show shortcuts: Ctrl + /
2. Focus chat input: Shift + Esc
3. Toggle sidebar: Ctrl + Shift + S
4. Open new chat: Ctrl + Shift + O
5. Copy last response: Ctrl + Shift + C
For example:
"Write a paper from ChatGPT's output."
1. Copy output: Ctrl + Shift + C
2. Open new chat: Ctrl + Shift + O
3. Ask it to write a paper on the info.
4. Ctrl V to paste in new information.
5. Press enter. Then paper completed.
(without ever touching your mouse)
Now THIS is ChatGPT mastery.
Move fast. Save time.
You must tap in to ChatGPT's short cuts.
1. Go to ChatGPT
2. Bottom right '?' mark
3. Access keyboard shortcuts
Keyboard Shortcuts:
1. Show shortcuts: Ctrl + /
2. Focus chat input: Shift + Esc
3. Toggle sidebar: Ctrl + Shift + S
4. Open new chat: Ctrl + Shift + O
5. Copy last response: Ctrl + Shift + C
For example:
"Write a paper from ChatGPT's output."
1. Copy output: Ctrl + Shift + C
2. Open new chat: Ctrl + Shift + O
3. Ask it to write a paper on the info.
4. Ctrl V to paste in new information.
5. Press enter. Then paper completed.
(without ever touching your mouse)
Now THIS is ChatGPT mastery.
Move fast. Save time.
๐1
cmd.pdf
213.5 KB
๐ฐ Cmd command lines pdf ๐
React โค๏ธ for more
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Top 10 programming languages & frameworks for beginner web developers:
1. HTML/CSS โ Basics of web structure & styling
2. JavaScript โ Adds interactivity
3. Python โ Backend & versatility
4. PHP โ Server-side scripting
5. SQL โ Database management
6. Ruby on Rails โ Easy backend framework
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9. Angular โ Framework for building dynamic UIs
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1. HTML/CSS โ Basics of web structure & styling
2. JavaScript โ Adds interactivity
3. Python โ Backend & versatility
4. PHP โ Server-side scripting
5. SQL โ Database management
6. Ruby on Rails โ Easy backend framework
7. Node.js โ JavaScript backend runtime
8. React โ Popular frontend library
9. Angular โ Framework for building dynamic UIs
10. Bootstrap โ Simplifies responsive design
Web Development Best Resources: https://topmate.io/coding/930165
ENJOY LEARNING ๐๐
๐2
๐๐ป๐ฑ๐ถ๐ฎ'๐ ๐๐ถ๐ด๐ด๐ฒ๐๐ ๐๐ฟ๐ถ๐๐ฒ ๐๐ผ๐ฟ ๐๐ผ๐น๐น๐ฒ๐ด๐ฒ ๐ฆ๐๐๐ฑ๐ฒ๐ป๐๐ ๐
Get Recognition from Top Companies like Emerson, Flex, TVS, Cargill & Many More
โ Win Prizes Worth Rs 20 Lacs
โ Paid Internship with Top MNCs
โ Recognition from Top Companies
โ Make Your CV Stand Out!
Eligibility: - Students Currently Pursuing UG/PG Courses
๐ฅ๐ฒ๐ด๐ถ๐๐๐ฒ๐ฟ ๐๐ผ๐ฟ ๐๐ฅ๐๐๐:-
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Golden Opportunity for All College Students ๐ซ
Get Recognition from Top Companies like Emerson, Flex, TVS, Cargill & Many More
โ Win Prizes Worth Rs 20 Lacs
โ Paid Internship with Top MNCs
โ Recognition from Top Companies
โ Make Your CV Stand Out!
Eligibility: - Students Currently Pursuing UG/PG Courses
๐ฅ๐ฒ๐ด๐ถ๐๐๐ฒ๐ฟ ๐๐ผ๐ฟ ๐๐ฅ๐๐๐:-
https://pdlink.in/3RHDZ9r
Golden Opportunity for All College Students ๐ซ
โ๏ธ 8 ChatGPT prompts to use when you need a spark of inspiration
Use those prompts as a starting point to move forward when you are at a dead end or have lost your way:
1. Improve your decision making
๐ก Prompt:
I am trying to decide if I should [insert decision]. Give me a list of pros and cons that will help me make this decision.
2. Learn from the best
๐ก Prompt:
Analyze the top performers in [insert your field of work]. Give me a list of the most important lessons I can learn from them to boost my productivity.
3. Your personalized tutor
๐ก Prompt:
I am currently learning about [insert topic]. Ask me a series of questions that will test my knowledge. Identify knowledge gaps in my answers and give me better answers to fill those gaps.
4. ChatGPT as your intern
๐ก Prompt:
I am creating a report about [insert topic]. Research and create an in-depth report with a step-by-step guide that will help readers understand how to [insert outcome].
5. Learn any new skill
๐ก Prompt:
I want to learn [insert skill]. Generate a 30 day plan that will help a beginner like me learn the skill from scratch.
6. Learn faster than ever with the 80/20 technique
๐ก Prompt:
I want to learn about [insert topic]. Identify and share the most important 20% of learnings from this topic that will help me understand 80% of it.
7. Get ChatGPT to write prompts for you
๐ก Prompt:
I am a/an [insert your profession]. Generate a list of most powerful prompts that will help someone in my profession get more done and save time.
8. Rewrite and simplify complex texts
๐ก Prompt:
Rewrite the text below in simple and easy to understand words. Simple and easy enough for anyone who doesn't know the subject to understand what I'm trying to say.
Use those prompts as a starting point to move forward when you are at a dead end or have lost your way:
1. Improve your decision making
๐ก Prompt:
I am trying to decide if I should [insert decision]. Give me a list of pros and cons that will help me make this decision.
2. Learn from the best
๐ก Prompt:
Analyze the top performers in [insert your field of work]. Give me a list of the most important lessons I can learn from them to boost my productivity.
3. Your personalized tutor
๐ก Prompt:
I am currently learning about [insert topic]. Ask me a series of questions that will test my knowledge. Identify knowledge gaps in my answers and give me better answers to fill those gaps.
4. ChatGPT as your intern
๐ก Prompt:
I am creating a report about [insert topic]. Research and create an in-depth report with a step-by-step guide that will help readers understand how to [insert outcome].
5. Learn any new skill
๐ก Prompt:
I want to learn [insert skill]. Generate a 30 day plan that will help a beginner like me learn the skill from scratch.
6. Learn faster than ever with the 80/20 technique
๐ก Prompt:
I want to learn about [insert topic]. Identify and share the most important 20% of learnings from this topic that will help me understand 80% of it.
7. Get ChatGPT to write prompts for you
๐ก Prompt:
I am a/an [insert your profession]. Generate a list of most powerful prompts that will help someone in my profession get more done and save time.
8. Rewrite and simplify complex texts
๐ก Prompt:
Rewrite the text below in simple and easy to understand words. Simple and easy enough for anyone who doesn't know the subject to understand what I'm trying to say.
๐5
๐๐ฐ๐ฐ๐ฒ๐ป๐๐๐ฟ๐ฒ ๐ญ๐ฌ๐ฌ% ๐๐ฅ๐๐ ๐ฉ๐ถ๐ฟ๐๐๐ฎ๐น ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐ฃ๐ฟ๐ผ๐ด๐ฟ๐ฎ๐บ๐ ๐
- Data Analytics and Visualization
- Coding: Development
- Project Management
- Software Engineering
These are perfect for students, freshers, or job seekers looking to stand out in a competitive job market.
๐๐ข๐ง๐ค ๐:-
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- Data Analytics and Visualization
- Coding: Development
- Project Management
- Software Engineering
These are perfect for students, freshers, or job seekers looking to stand out in a competitive job market.
๐๐ข๐ง๐ค ๐:-
https://pdlink.in/3EmuAkw
Enroll For FREE & Get Certified๐
Many people still aren't fully utilizing the power of Telegram.
There are numerous channels on Telegram that can help you find the latest job and internship opportunities?
Here are some of my top channel recommendations to help you get started ๐๐
Latest Jobs & Internships: https://t.iss.one/getjobss
Jobs Preparation Resources:
https://t.iss.one/jobinterviewsprep
Web Development Jobs:
https://t.iss.one/webdeveloperjob
Data Science Jobs:
https://t.iss.one/datasciencej
Interview Tips:
https://t.iss.one/Interview_Jobs
Data Analyst Jobs:
https://t.iss.one/jobs_SQL
AI Jobs:
https://t.iss.one/AIjobz
Remote Jobs:
https://t.iss.one/jobs_us_uk
FAANG Jobs:
https://t.iss.one/FAANGJob
Software Developer Jobs: https://t.iss.one/internshiptojobs
If you found this helpful, donโt forget to like, share, and follow for more resources that can boost your career journey!
Let me know if you know any other useful telegram channel
ENJOY LEARNING๐๐
There are numerous channels on Telegram that can help you find the latest job and internship opportunities?
Here are some of my top channel recommendations to help you get started ๐๐
Latest Jobs & Internships: https://t.iss.one/getjobss
Jobs Preparation Resources:
https://t.iss.one/jobinterviewsprep
Web Development Jobs:
https://t.iss.one/webdeveloperjob
Data Science Jobs:
https://t.iss.one/datasciencej
Interview Tips:
https://t.iss.one/Interview_Jobs
Data Analyst Jobs:
https://t.iss.one/jobs_SQL
AI Jobs:
https://t.iss.one/AIjobz
Remote Jobs:
https://t.iss.one/jobs_us_uk
FAANG Jobs:
https://t.iss.one/FAANGJob
Software Developer Jobs: https://t.iss.one/internshiptojobs
If you found this helpful, donโt forget to like, share, and follow for more resources that can boost your career journey!
Let me know if you know any other useful telegram channel
ENJOY LEARNING๐๐
๐3