๐ง๐ผ๐ฝ ๐๐ผ๐บ๐ฝ๐ฎ๐ป๐ถ๐ฒ๐ ๐๐ถ๐ธ๐ฒ ๐๐ป๐ณ๐ผ๐๐๐ , ๐๐ฒ๐ป๐ฝ๐ฎ๐ฐ๐ ,๐&๐ง ,๐ฃ๐ต๐ถ๐น๐ถ๐ฝ๐ & ๐ข๐ฟ๐ฎ๐ฐ๐น๐ฒ ๐๐ถ๐ฟ๐ถ๐ป๐ด ๐
Roles Hiring:- Data Analyst, Software Engineer & Associate
Job Location:- Across India/WFH
Qualification:- Graduate/Post Graduate
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Select your experience & Complete The Registration Process
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Roles Hiring:- Data Analyst, Software Engineer & Associate
Job Location:- Across India/WFH
Qualification:- Graduate/Post Graduate
๐๐ฝ๐ฝ๐น๐ ๐ก๐ผ๐๐:-
https://bit.ly/44qMX2k
Select your experience & Complete The Registration Process
Select the company name & apply for the role that matches you
โค1
Forwarded from Coding Projects
๐๐ฅ๐๐ ๐ฅ๐ฒ๐๐ผ๐๐ฟ๐ฐ๐ฒ๐ ๐ง๐ผ ๐๐ฟ๐ฎ๐ฐ๐ธ ๐ฌ๐ผ๐๐ฟ ๐ก๐ฒ๐
๐ ๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐ ๐
Preparing for coding interviews? These free resources will help you crack your dream job! ๐
Ace Your Next Interview with These FREE Resources!๐จโ๐ป
๐๐ข๐ง๐ค๐:-
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All The Best ๐
Preparing for coding interviews? These free resources will help you crack your dream job! ๐
Ace Your Next Interview with These FREE Resources!๐จโ๐ป
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All The Best ๐
โค1
General tips for coding interviews
Always validate input first. Check for inputs that are invalid, empty, negative, or different. Never assume you are given the valid parameters. Alternatively, clarify with the interviewer whether you can assume valid input (usually yes), which can save you time from writing code that does input validation.
Are there any time and space complexities requirements or constraints?
Check for off-by-one errors.
In languages where there are no automatic type coercion, check that concatenation of values are of the same type: int,str, and list.
After you finish your code, use a few example inputs to test your solution.
Is the algorithm supposed to run multiple times, perhaps on a web server? If yes, the input can likely be pre-processed to improve the efficiency in each API call.
Use a mix of functional and imperative programming paradigms:
๐น Write pure functions as often as possible.
๐น Use pure functions because they are easier to reason with and can help reduce bugs in your implementation.
๐น Avoid mutating the parameters passed into your function, especially if they are passed by reference, unless you are sure of what you are doing.
๐น Achieve a balance between accuracy and efficiency. Use the right amount of functional and imperative code where appropriate. Functional programming is usually expensive in terms of space complexity because of non-mutation and the repeated allocation of new objects. On the other hand, imperative code is faster because you operate on existing objects.
๐น Avoid relying on mutating global variables. Global variables introduce state.
๐น Make sure that you do not accidentally mutate global variables, especially if you have to rely on them.
Always validate input first. Check for inputs that are invalid, empty, negative, or different. Never assume you are given the valid parameters. Alternatively, clarify with the interviewer whether you can assume valid input (usually yes), which can save you time from writing code that does input validation.
Are there any time and space complexities requirements or constraints?
Check for off-by-one errors.
In languages where there are no automatic type coercion, check that concatenation of values are of the same type: int,str, and list.
After you finish your code, use a few example inputs to test your solution.
Is the algorithm supposed to run multiple times, perhaps on a web server? If yes, the input can likely be pre-processed to improve the efficiency in each API call.
Use a mix of functional and imperative programming paradigms:
๐น Write pure functions as often as possible.
๐น Use pure functions because they are easier to reason with and can help reduce bugs in your implementation.
๐น Avoid mutating the parameters passed into your function, especially if they are passed by reference, unless you are sure of what you are doing.
๐น Achieve a balance between accuracy and efficiency. Use the right amount of functional and imperative code where appropriate. Functional programming is usually expensive in terms of space complexity because of non-mutation and the repeated allocation of new objects. On the other hand, imperative code is faster because you operate on existing objects.
๐น Avoid relying on mutating global variables. Global variables introduce state.
๐น Make sure that you do not accidentally mutate global variables, especially if you have to rely on them.
โค1
๐๐๐ฒ ๐๐๐ญ๐๐ซ ๐๐ฅ๐๐๐๐ฆ๐๐ง๐ญ - ๐๐๐ญ ๐๐ฅ๐๐๐๐ ๐๐ง ๐๐จ๐ฉ ๐๐๐'๐ฌ ๐
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Hurry, limited seats available!๐โโ๏ธ
Learn Coding From Scratch - Lectures Taught By IIT Alumni
60+ Hiring Drives Every Month
๐๐ข๐ ๐ก๐ฅ๐ข๐ ๐ก๐ญ๐ฌ:-
๐ Trusted by 7500+ Students
๐ค 500+ Hiring Partners
๐ผ Avg. Rs. 7.4 LPA
๐ 41 LPA Highest Package
Eligibility: BTech / BCA / BSc / MCA / MSc
๐๐๐ ๐ข๐ฌ๐ญ๐๐ซ ๐๐จ๐ฐ๐ :-
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โค1
Here are some interview preparation tips ๐๐
Technical Interview
1. Review Core Concepts:
- Data Structures: Be comfortable with LinkedLists, Trees, Graphs, and their representations.
- Algorithms: Brush up on searching and sorting algorithms, time complexities, and common algorithms (like Dijkstraโs or A*).
- Programming Languages: Ensure you understand the language you are most comfortable with (e.g., C++, Java, Python) and know its standard library functions.
2. Practice Coding Problems:
- Utilize platforms like LeetCode, HackerRank, or CodeSignal to practice medium-level coding questions. Focus on common patterns and problem-solving strategies.
3. Mock Interviews: Conduct mock technical interviews with peers or mentors to build confidence and receive feedback.
Personal Interview
1. Prepare Your Story:
- Outline your educational journey, achievements, and any relevant projects. Emphasize experiences that demonstrate leadership, teamwork, and problem-solving skills.
- Be ready to discuss your challenges and how you overcame them.
2. Articulate Your Goals:
- Be clear about why you want to join the program and how it aligns with your career aspirations. Reflect on what you hope to gain from the experience.
- Focus on Fundamentals:
Be thorough with basic subjects like Operating Systems, Networking, OOP, and Databases. Clear concepts are key for technical interviews.
2. Common Interview Questions:
DSA:
- Implement various data structures like Linked Lists, Trees, Graphs, Stacks, and Queues.
- Understand searching and sorting algorithms: Binary Search, Merge Sort, Quick Sort, etc.
- Solve problems involving HashMaps, Sets, and other collections.
Sample DSA Questions
- Reverse a linked list.
- Find the first non-repeating character in a string.
- Detect a cycle in a graph.
- Implement a queue using two stacks.
- Find the lowest common ancestor in a binary tree.
3. Key Topics to Focus On
DSA:
- Arrays, Strings, Linked Lists, Trees, Graphs
- Recursion, Backtracking, Dynamic Programming
- Sorting and Searching Algorithms
- Time and Space Complexity
Core Subjects
- Operating Systems: Concepts like processes, threads, deadlocks, concurrency, and memory management.
- Database Management Systems (DBMS): Understanding SQL, Normalization, and database design.
- Object-Oriented Programming (OOP): Know about inheritance, polymorphism, encapsulation, and design patterns.
5. Tips
- Optimize Your Code: Write clean, optimized code. Discuss time and space complexities during interviews.
- Review Your Projects: Be ready to explain your past projects, the challenges you faced, and the technologies you used.....
Best Programming Resources: https://topmate.io/coding/898340
All the best ๐๐
Technical Interview
1. Review Core Concepts:
- Data Structures: Be comfortable with LinkedLists, Trees, Graphs, and their representations.
- Algorithms: Brush up on searching and sorting algorithms, time complexities, and common algorithms (like Dijkstraโs or A*).
- Programming Languages: Ensure you understand the language you are most comfortable with (e.g., C++, Java, Python) and know its standard library functions.
2. Practice Coding Problems:
- Utilize platforms like LeetCode, HackerRank, or CodeSignal to practice medium-level coding questions. Focus on common patterns and problem-solving strategies.
3. Mock Interviews: Conduct mock technical interviews with peers or mentors to build confidence and receive feedback.
Personal Interview
1. Prepare Your Story:
- Outline your educational journey, achievements, and any relevant projects. Emphasize experiences that demonstrate leadership, teamwork, and problem-solving skills.
- Be ready to discuss your challenges and how you overcame them.
2. Articulate Your Goals:
- Be clear about why you want to join the program and how it aligns with your career aspirations. Reflect on what you hope to gain from the experience.
- Focus on Fundamentals:
Be thorough with basic subjects like Operating Systems, Networking, OOP, and Databases. Clear concepts are key for technical interviews.
2. Common Interview Questions:
DSA:
- Implement various data structures like Linked Lists, Trees, Graphs, Stacks, and Queues.
- Understand searching and sorting algorithms: Binary Search, Merge Sort, Quick Sort, etc.
- Solve problems involving HashMaps, Sets, and other collections.
Sample DSA Questions
- Reverse a linked list.
- Find the first non-repeating character in a string.
- Detect a cycle in a graph.
- Implement a queue using two stacks.
- Find the lowest common ancestor in a binary tree.
3. Key Topics to Focus On
DSA:
- Arrays, Strings, Linked Lists, Trees, Graphs
- Recursion, Backtracking, Dynamic Programming
- Sorting and Searching Algorithms
- Time and Space Complexity
Core Subjects
- Operating Systems: Concepts like processes, threads, deadlocks, concurrency, and memory management.
- Database Management Systems (DBMS): Understanding SQL, Normalization, and database design.
- Object-Oriented Programming (OOP): Know about inheritance, polymorphism, encapsulation, and design patterns.
5. Tips
- Optimize Your Code: Write clean, optimized code. Discuss time and space complexities during interviews.
- Review Your Projects: Be ready to explain your past projects, the challenges you faced, and the technologies you used.....
Best Programming Resources: https://topmate.io/coding/898340
All the best ๐๐
โค3
๐๐ผ๐ผ๐๐ ๐ฌ๐ผ๐๐ฟ ๐ฅ๐ฒ๐๐๐บ๐ฒ ๐๐ถ๐๐ต ๐ง๐ต๐ฒ๐๐ฒ ๐๐ฎ๐ป๐ฑ๐-๐ข๐ป ๐ฃ๐๐๐ต๐ผ๐ป ๐ฃ๐ฟ๐ผ๐ท๐ฒ๐ฐ๐๐ (๐๐ฟ๐ฒ๐ฒ ๐ฌ๐ผ๐๐ง๐๐ฏ๐ฒ ๐ง๐๐๐ผ๐ฟ๐ถ๐ฎ๐น๐)๐
Want to stand out with real Python experience?๐จโ๐ป๐ก
These full-length YouTube tutorials walk you through resume-worthy projects โ perfect for beginners aiming to move beyond theory.๐๐
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Here are 5 projects you can start today๐โ ๏ธ
Want to stand out with real Python experience?๐จโ๐ป๐ก
These full-length YouTube tutorials walk you through resume-worthy projects โ perfect for beginners aiming to move beyond theory.๐๐
๐๐ข๐ง๐ค๐:-
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Here are 5 projects you can start today๐โ ๏ธ
โค2
๏ธ๐๐ฒ๐ฎ๐ฟ๐ป ๐๐ & ๐ ๐ ๐ณ๐ผ๐ฟ ๐๐ฅ๐๐ โ ๐ก๐ผ ๐ฃ๐ฟ๐ถ๐ผ๐ฟ ๐๐
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Importance of AI in Data Analytics
AI is transforming the way data is analyzed and insights are generated. Here's how AI adds value in data analytics:
1. Automated Data Cleaning
AI helps in detecting anomalies, missing values, and outliers automatically, improving data quality and saving analysts hours of manual work.
2. Faster & Smarter Decision Making
AI models can process massive datasets in seconds and suggest actionable insights, enabling real-time decision-making.
3. Predictive Analytics
AI enables forecasting future trends and behaviors using machine learning models (e.g., sales predictions, churn forecasting).
4. Natural Language Processing (NLP)
AI can analyze unstructured data like reviews, feedback, or comments using sentiment analysis, keyword extraction, and topic modeling.
5. Pattern Recognition
AI uncovers hidden patterns, correlations, and clusters in data that traditional analysis may miss.
6. Personalization & Recommendation
AI algorithms power recommendation systems (like on Netflix, Amazon) that personalize user experiences based on behavioral data.
7. Data Visualization Enhancement
AI auto-generates dashboards, chooses best chart types, and highlights key anomalies or insights without manual intervention.
8. Fraud Detection & Risk Analysis
AI models detect fraud and mitigate risks in real-time using anomaly detection and classification techniques.
9. Chatbots & Virtual Analysts
AI-powered tools like ChatGPT allow users to interact with data using natural language, removing the need for technical skills.
10. Operational Efficiency
AI automates repetitive tasks like report generation, data transformation, and alertsโfreeing analysts to focus on strategy.
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
#dataanalytics
AI is transforming the way data is analyzed and insights are generated. Here's how AI adds value in data analytics:
1. Automated Data Cleaning
AI helps in detecting anomalies, missing values, and outliers automatically, improving data quality and saving analysts hours of manual work.
2. Faster & Smarter Decision Making
AI models can process massive datasets in seconds and suggest actionable insights, enabling real-time decision-making.
3. Predictive Analytics
AI enables forecasting future trends and behaviors using machine learning models (e.g., sales predictions, churn forecasting).
4. Natural Language Processing (NLP)
AI can analyze unstructured data like reviews, feedback, or comments using sentiment analysis, keyword extraction, and topic modeling.
5. Pattern Recognition
AI uncovers hidden patterns, correlations, and clusters in data that traditional analysis may miss.
6. Personalization & Recommendation
AI algorithms power recommendation systems (like on Netflix, Amazon) that personalize user experiences based on behavioral data.
7. Data Visualization Enhancement
AI auto-generates dashboards, chooses best chart types, and highlights key anomalies or insights without manual intervention.
8. Fraud Detection & Risk Analysis
AI models detect fraud and mitigate risks in real-time using anomaly detection and classification techniques.
9. Chatbots & Virtual Analysts
AI-powered tools like ChatGPT allow users to interact with data using natural language, removing the need for technical skills.
10. Operational Efficiency
AI automates repetitive tasks like report generation, data transformation, and alertsโfreeing analysts to focus on strategy.
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
#dataanalytics
โค1
AL ML 3 Months Roadmap_250530_164720.pdf
374.3 KB
๐ Here is a AI/ML Roadmap of 3 Months + Free Resources ๐
React โค๏ธ For More
React โค๏ธ For More
โค2
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โค1
Essential Pandas Functions for Data Analysis
Data Loading:
pd.read_csv() - Load data from a CSV file.
pd.read_excel() - Load data from an Excel file.
Data Inspection:
df.head(n) - View the first n rows.
df.info() - Get a summary of the dataset.
df.describe() - Generate summary statistics.
Data Manipulation:
df.drop(columns=['col1', 'col2']) - Remove specific columns.
df.rename(columns={'old_name': 'new_name'}) - Rename columns.
df['col'] = df['col'].apply(func) - Apply a function to a column.
Filtering and Sorting:
df[df['col'] > value] - Filter rows based on a condition.
df.sort_values(by='col', ascending=True) - Sort rows by a column.
Aggregation:
df.groupby('col').sum() - Group data and compute the sum.
df['col'].value_counts() - Count unique values in a column.
Merging and Joining:
pd.merge(df1, df2, on='key') - Merge two DataFrames.
pd.concat([df1, df2]) - Concatenate
Here you can find essential Python Interview Resources๐
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Like this post for more resources like this ๐โฅ๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
Data Loading:
pd.read_csv() - Load data from a CSV file.
pd.read_excel() - Load data from an Excel file.
Data Inspection:
df.head(n) - View the first n rows.
df.info() - Get a summary of the dataset.
df.describe() - Generate summary statistics.
Data Manipulation:
df.drop(columns=['col1', 'col2']) - Remove specific columns.
df.rename(columns={'old_name': 'new_name'}) - Rename columns.
df['col'] = df['col'].apply(func) - Apply a function to a column.
Filtering and Sorting:
df[df['col'] > value] - Filter rows based on a condition.
df.sort_values(by='col', ascending=True) - Sort rows by a column.
Aggregation:
df.groupby('col').sum() - Group data and compute the sum.
df['col'].value_counts() - Count unique values in a column.
Merging and Joining:
pd.merge(df1, df2, on='key') - Merge two DataFrames.
pd.concat([df1, df2]) - Concatenate
Here you can find essential Python Interview Resources๐
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Like this post for more resources like this ๐โฅ๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
โค2
๐๐๐น๐น๐๐๐ฎ๐ฐ๐ธ ๐๐ฒ๐๐ฒ๐น๐ผ๐ฝ๐บ๐ฒ๐ป๐ ๐๐ฅ๐๐ ๐๐ฒ๐บ๐ผ ๐๐น๐ฎ๐๐ ๐๐ป ๐๐๐ฑ๐ฒ๐ฟ๐ฎ๐ฏ๐ฎ๐ฑ/๐ฃ๐๐ป๐ฒ ๐
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Essential Programming Languages to Learn Data Science ๐๐
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 ๐๐
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 LEARNING๐๐
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 ๐๐
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 LEARNING๐๐
โค1
Product team cases where a #productteams improved content discovery
Case: Netflix and Personalized Content Recommendations
Problem: Netflix wanted to improve user engagement by enhancing content discovery and reducing churn.
Solution: Using a product outcome mindset, Netflix's product team developed a recommendation algorithm that analyzed user viewing behavior and preferences to offer personalized content suggestions.
Outcome: Netflix saw a significant increase in user engagement, with the personalized recommendations leading to higher watch times and reduced churn.
Learn more: You can read about Netflix's recommendation system in various articles and research papers, such as "Netflix Recommendations: Beyond the 5 stars" (by Netflix).
Case: Spotify and Music Discovery
Problem: Spotify users were overwhelmed by the vast music library and struggled to discover new music.
Solution: Spotify's product team used data-driven insights to create personalized playlists like "Discover Weekly" and "Release Radar," tailored to users' listening habits.
Outcome: The personalized playlists increased user engagement, time spent on the platform, and the likelihood of users discovering and enjoying new music.
Link: Learn more about Spotify's approach to music discovery in articles like "How Spotify Discover Weekly and Release Radar Playlist Work" (by The Verge).
Case: Netflix and Personalized Content Recommendations
Problem: Netflix wanted to improve user engagement by enhancing content discovery and reducing churn.
Solution: Using a product outcome mindset, Netflix's product team developed a recommendation algorithm that analyzed user viewing behavior and preferences to offer personalized content suggestions.
Outcome: Netflix saw a significant increase in user engagement, with the personalized recommendations leading to higher watch times and reduced churn.
Learn more: You can read about Netflix's recommendation system in various articles and research papers, such as "Netflix Recommendations: Beyond the 5 stars" (by Netflix).
Case: Spotify and Music Discovery
Problem: Spotify users were overwhelmed by the vast music library and struggled to discover new music.
Solution: Spotify's product team used data-driven insights to create personalized playlists like "Discover Weekly" and "Release Radar," tailored to users' listening habits.
Outcome: The personalized playlists increased user engagement, time spent on the platform, and the likelihood of users discovering and enjoying new music.
Link: Learn more about Spotify's approach to music discovery in articles like "How Spotify Discover Weekly and Release Radar Playlist Work" (by The Verge).
โค1
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Roles Hiring:- Tech & Non Tech Roles
Salary Range :- 5 To 24LPA
Qualification:- Graduate/Post Graduate
๐๐ฝ๐ฝ๐น๐ ๐ก๐ผ๐๐:-
https://bit.ly/44qMX2k
Select your experience & Complete The Registration Process
Select the company name & apply for the role that matches you
โค1
9 tips to get better at debugging code:
Read error messages carefully โ they often tell you everything
Use print/log statements to trace code execution
Check one small part at a time
Reproduce the bug consistently
Use a debugger to step through code line by line
Compare working vs broken code
Check for typos, null values, and off-by-one errors
Rubber duck debugging โ explain your code out loud
Take breaks โ fresh eyes spot bugs faster
Coding Interview Resources:๐ https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X
ENJOY LEARNING ๐๐
Read error messages carefully โ they often tell you everything
Use print/log statements to trace code execution
Check one small part at a time
Reproduce the bug consistently
Use a debugger to step through code line by line
Compare working vs broken code
Check for typos, null values, and off-by-one errors
Rubber duck debugging โ explain your code out loud
Take breaks โ fresh eyes spot bugs faster
Coding Interview Resources:๐ https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X
ENJOY LEARNING ๐๐
โค1
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โค1