๐ป๐ง 10 CODING PATTERNS EVERY BEGINNER SHOULD LEARN FOR INTERVIEWS
If you're preparing for coding interviews, don't try to memorize hundreds of solutions.
A better strategy is to learn problem-solving patterns.
Once you recognize the pattern, many seemingly different problems become much easier.
Here are 10 important ones ๐
1๏ธโฃ TWO POINTERS
Use two pointers to move through a data structure, often from opposite ends or at different speeds.
Commonly useful for:
โข Sorted arrays
โข Pair-sum problems
โข Removing duplicates
โข Palindrome problems
๐ Instead of repeatedly searching the entire array, two pointers can often reduce unnecessary work.
2๏ธโฃ SLIDING WINDOW
Use a moving window to examine a continuous portion of an array or string.
Useful for problems involving:
โข Subarrays
โข Substrings
โข Maximum/minimum sums
โข Longest or shortest ranges
Example idea: ""[1][2][3][4][5]
Instead of recalculating every range from scratch, maintain a window and update it as it moves.
3๏ธโฃ HASHING
Use a Hash Map or Set when you need fast lookup.
Useful for:
โข Finding duplicates
โข Counting frequencies
โข Checking whether an element exists
โข Finding pairs
โข Tracking previously seen values
๐ If a problem repeatedly asks "Have I seen this before?", think about hashing.
4๏ธโฃ BINARY SEARCH
Binary Search repeatedly divides a sorted search space into two parts.
Instead of checking: 1 โ 2 โ 3 โ 4 โ 5 โ... you eliminate half of the remaining possibilities after each comparison.
Time Complexity: O(log n)
It can also be applied to certain problems where you're searching for the answer within a monotonic range.
5๏ธโฃ FAST & SLOW POINTERS
Two pointers move at different speeds.
This pattern is commonly used with linked lists to:
โข Detect cycles
โข Find the middle node
โข Determine certain positional relationships
A classic example is the cycle-detection technique using a slow pointer and a fast pointer.
6๏ธโฃ STACK
A stack follows: LIFO โ Last In, First Out
Stacks are useful for:
โข Valid parentheses
โข Undo operations
โข Expression evaluation
โข Backtracking
โข Monotonic stack problems
If you need to process the most recently added item first, consider a stack.
7๏ธโฃ BFS & DFS
These are fundamental ways to traverse trees and graphs.
BFS โ Breadth-First Search
Explores nodes level by level.
Often useful for:
โข Shortest path in an unweighted graph
โข Level-order traversal
โข Finding nearby nodes
DFS โ Depth-First Search
Explores as deeply as possible before backtracking.
Often useful for:
โข Tree traversal
โข Graph traversal
โข Connected components
โข Backtracking-style problems
8๏ธโฃ BACKTRACKING
Backtracking builds a solution step by step.
When a choice doesn't work, you undo it and try another possibility.
Common problems: Permutations, Combinations, Subsets, Sudoku, N-Queens
Basic idea: Choose โ Explore โ Undo
9๏ธโฃ GREEDY
A greedy algorithm makes the best-looking choice at the current step.
The key question is: "Can making the best local choice lead to a globally optimal solution?"
Greedy approaches appear in problems involving:
โข Scheduling
โข Intervals
โข Resource allocation
โข Optimization
โ ๏ธ Not every optimization problem can be solved greedily.
If you're preparing for coding interviews, don't try to memorize hundreds of solutions.
A better strategy is to learn problem-solving patterns.
Once you recognize the pattern, many seemingly different problems become much easier.
Here are 10 important ones ๐
1๏ธโฃ TWO POINTERS
Use two pointers to move through a data structure, often from opposite ends or at different speeds.
Commonly useful for:
โข Sorted arrays
โข Pair-sum problems
โข Removing duplicates
โข Palindrome problems
๐ Instead of repeatedly searching the entire array, two pointers can often reduce unnecessary work.
2๏ธโฃ SLIDING WINDOW
Use a moving window to examine a continuous portion of an array or string.
Useful for problems involving:
โข Subarrays
โข Substrings
โข Maximum/minimum sums
โข Longest or shortest ranges
Example idea: ""[1][2][3][4][5]
Instead of recalculating every range from scratch, maintain a window and update it as it moves.
3๏ธโฃ HASHING
Use a Hash Map or Set when you need fast lookup.
Useful for:
โข Finding duplicates
โข Counting frequencies
โข Checking whether an element exists
โข Finding pairs
โข Tracking previously seen values
๐ If a problem repeatedly asks "Have I seen this before?", think about hashing.
4๏ธโฃ BINARY SEARCH
Binary Search repeatedly divides a sorted search space into two parts.
Instead of checking: 1 โ 2 โ 3 โ 4 โ 5 โ... you eliminate half of the remaining possibilities after each comparison.
Time Complexity: O(log n)
It can also be applied to certain problems where you're searching for the answer within a monotonic range.
5๏ธโฃ FAST & SLOW POINTERS
Two pointers move at different speeds.
This pattern is commonly used with linked lists to:
โข Detect cycles
โข Find the middle node
โข Determine certain positional relationships
A classic example is the cycle-detection technique using a slow pointer and a fast pointer.
6๏ธโฃ STACK
A stack follows: LIFO โ Last In, First Out
Stacks are useful for:
โข Valid parentheses
โข Undo operations
โข Expression evaluation
โข Backtracking
โข Monotonic stack problems
If you need to process the most recently added item first, consider a stack.
7๏ธโฃ BFS & DFS
These are fundamental ways to traverse trees and graphs.
BFS โ Breadth-First Search
Explores nodes level by level.
Often useful for:
โข Shortest path in an unweighted graph
โข Level-order traversal
โข Finding nearby nodes
DFS โ Depth-First Search
Explores as deeply as possible before backtracking.
Often useful for:
โข Tree traversal
โข Graph traversal
โข Connected components
โข Backtracking-style problems
8๏ธโฃ BACKTRACKING
Backtracking builds a solution step by step.
When a choice doesn't work, you undo it and try another possibility.
Common problems: Permutations, Combinations, Subsets, Sudoku, N-Queens
Basic idea: Choose โ Explore โ Undo
9๏ธโฃ GREEDY
A greedy algorithm makes the best-looking choice at the current step.
The key question is: "Can making the best local choice lead to a globally optimal solution?"
Greedy approaches appear in problems involving:
โข Scheduling
โข Intervals
โข Resource allocation
โข Optimization
โ ๏ธ Not every optimization problem can be solved greedily.
๐1
๐ DYNAMIC PROGRAMMING
Dynamic Programming, or DP, is used when a problem can be broken into smaller overlapping subproblems and their results can be reused.
Two important ideas are:
๐ Memoization โ Store results of previously solved states.
๐ Tabulation โ Build results iteratively from smaller states.
DP often appears in problems involving: Sequences, Paths, Knapsack-style problems, Optimization, Counting possibilities
๐ฅ HOW TO RECOGNIZE THE PATTERN
๐น Continuous subarray/substring โ Sliding Window
๐น Sorted data + search โ Binary Search
๐น Pair or opposite-end comparison โ Two Pointers
๐น Need fast lookup โ Hashing
๐น Most recent item first โ Stack
๐น Tree/graph traversal โ BFS / DFS
๐น Explore multiple possibilities โ Backtracking
๐น Repeated subproblems โ Dynamic Programming
๐น Local choices with provable optimality โ Greedy
๐ Double Tap โค๏ธ For More
Dynamic Programming, or DP, is used when a problem can be broken into smaller overlapping subproblems and their results can be reused.
Two important ideas are:
๐ Memoization โ Store results of previously solved states.
๐ Tabulation โ Build results iteratively from smaller states.
DP often appears in problems involving: Sequences, Paths, Knapsack-style problems, Optimization, Counting possibilities
๐ฅ HOW TO RECOGNIZE THE PATTERN
๐น Continuous subarray/substring โ Sliding Window
๐น Sorted data + search โ Binary Search
๐น Pair or opposite-end comparison โ Two Pointers
๐น Need fast lookup โ Hashing
๐น Most recent item first โ Stack
๐น Tree/graph traversal โ BFS / DFS
๐น Explore multiple possibilities โ Backtracking
๐น Repeated subproblems โ Dynamic Programming
๐น Local choices with provable optimality โ Greedy
๐ Double Tap โค๏ธ For More
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A-Z of essential data science concepts
A: Algorithm - A set of rules or instructions for solving a problem or completing a task.
B: Big Data - Large and complex datasets that traditional data processing applications are unable to handle efficiently.
C: Classification - A type of machine learning task that involves assigning labels to instances based on their characteristics.
D: Data Mining - The process of discovering patterns and extracting useful information from large datasets.
E: Ensemble Learning - A machine learning technique that combines multiple models to improve predictive performance.
F: Feature Engineering - The process of selecting, extracting, and transforming features from raw data to improve model performance.
G: Gradient Descent - An optimization algorithm used to minimize the error of a model by adjusting its parameters iteratively.
H: Hypothesis Testing - A statistical method used to make inferences about a population based on sample data.
I: Imputation - The process of replacing missing values in a dataset with estimated values.
J: Joint Probability - The probability of the intersection of two or more events occurring simultaneously.
K: K-Means Clustering - A popular unsupervised machine learning algorithm used for clustering data points into groups.
L: Logistic Regression - A statistical model used for binary classification tasks.
M: Machine Learning - A subset of artificial intelligence that enables systems to learn from data and improve performance over time.
N: Neural Network - A computer system inspired by the structure of the human brain, used for various machine learning tasks.
O: Outlier Detection - The process of identifying observations in a dataset that significantly deviate from the rest of the data points.
P: Precision and Recall - Evaluation metrics used to assess the performance of classification models.
Q: Quantitative Analysis - The process of using mathematical and statistical methods to analyze and interpret data.
R: Regression Analysis - A statistical technique used to model the relationship between a dependent variable and one or more independent variables.
S: Support Vector Machine - A supervised machine learning algorithm used for classification and regression tasks.
T: Time Series Analysis - The study of data collected over time to detect patterns, trends, and seasonal variations.
U: Unsupervised Learning - Machine learning techniques used to identify patterns and relationships in data without labeled outcomes.
V: Validation - The process of assessing the performance and generalization of a machine learning model using independent datasets.
W: Weka - A popular open-source software tool used for data mining and machine learning tasks.
X: XGBoost - An optimized implementation of gradient boosting that is widely used for classification and regression tasks.
Y: Yarn - A resource manager used in Apache Hadoop for managing resources across distributed clusters.
Z: Zero-Inflated Model - A statistical model used to analyze data with excess zeros, commonly found in count data.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.me/datasciencefun
Like if you need similar content ๐๐
Hope this helps you ๐
A: Algorithm - A set of rules or instructions for solving a problem or completing a task.
B: Big Data - Large and complex datasets that traditional data processing applications are unable to handle efficiently.
C: Classification - A type of machine learning task that involves assigning labels to instances based on their characteristics.
D: Data Mining - The process of discovering patterns and extracting useful information from large datasets.
E: Ensemble Learning - A machine learning technique that combines multiple models to improve predictive performance.
F: Feature Engineering - The process of selecting, extracting, and transforming features from raw data to improve model performance.
G: Gradient Descent - An optimization algorithm used to minimize the error of a model by adjusting its parameters iteratively.
H: Hypothesis Testing - A statistical method used to make inferences about a population based on sample data.
I: Imputation - The process of replacing missing values in a dataset with estimated values.
J: Joint Probability - The probability of the intersection of two or more events occurring simultaneously.
K: K-Means Clustering - A popular unsupervised machine learning algorithm used for clustering data points into groups.
L: Logistic Regression - A statistical model used for binary classification tasks.
M: Machine Learning - A subset of artificial intelligence that enables systems to learn from data and improve performance over time.
N: Neural Network - A computer system inspired by the structure of the human brain, used for various machine learning tasks.
O: Outlier Detection - The process of identifying observations in a dataset that significantly deviate from the rest of the data points.
P: Precision and Recall - Evaluation metrics used to assess the performance of classification models.
Q: Quantitative Analysis - The process of using mathematical and statistical methods to analyze and interpret data.
R: Regression Analysis - A statistical technique used to model the relationship between a dependent variable and one or more independent variables.
S: Support Vector Machine - A supervised machine learning algorithm used for classification and regression tasks.
T: Time Series Analysis - The study of data collected over time to detect patterns, trends, and seasonal variations.
U: Unsupervised Learning - Machine learning techniques used to identify patterns and relationships in data without labeled outcomes.
V: Validation - The process of assessing the performance and generalization of a machine learning model using independent datasets.
W: Weka - A popular open-source software tool used for data mining and machine learning tasks.
X: XGBoost - An optimized implementation of gradient boosting that is widely used for classification and regression tasks.
Y: Yarn - A resource manager used in Apache Hadoop for managing resources across distributed clusters.
Z: Zero-Inflated Model - A statistical model used to analyze data with excess zeros, commonly found in count data.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.me/datasciencefun
Like if you need similar content ๐๐
Hope this helps you ๐
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Microsoft-focused learning paths can help you strengthen your resume and prepare for in-demand tech and data roles.
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โ Build practical, job-ready skills
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Hereโs a collection of company-specific resources to help you understand their interview and hiring processes.
๐ฏ Interview Preparation Guides For:
๐ Amazon โ Interviewing Guide
๐ต Google โ Interview Tips
๐ช Microsoft โ Hiring & Interview Tips
๐ข NVIDIA โ Hiring Process
๐ท Meta โ Software Engineering Interview Prep
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โ
Coding Interview Acronyms You MUST Know ๐ป๐ฅ
DSA โ Data Structures & Algorithms
CPU โ Central Processing Unit
RAM โ Random Access Memory
DBMS โ Database Management System
RDBMS โ Relational Database Management System
ACID โ Atomicity, Consistency, Isolation, Durability
OLTP โ Online Transaction Processing
OLAP โ Online Analytical Processing
TCP โ Transmission Control Protocol
IP โ Internet Protocol
DNS โ Domain Name System
MVC โ Model View Controller
MVVM โ Model View ViewModel
SDLC โ Software Development Life Cycle
CI/CD โ Continuous Integration / Continuous Deployment
JWT โ JSON Web Token
ORM โ Object Relational Mapping
API โ Application Programming Interface
REST โ Representational State Transfer
SOAP โ Simple Object Access Protocol
Big O โ Time & Space Complexity Notation
FIFO โ First In First Out
LIFO โ Last In First Out
๐ฌ Double Tap โค๏ธ for more!
DSA โ Data Structures & Algorithms
CPU โ Central Processing Unit
RAM โ Random Access Memory
DBMS โ Database Management System
RDBMS โ Relational Database Management System
ACID โ Atomicity, Consistency, Isolation, Durability
OLTP โ Online Transaction Processing
OLAP โ Online Analytical Processing
TCP โ Transmission Control Protocol
IP โ Internet Protocol
DNS โ Domain Name System
MVC โ Model View Controller
MVVM โ Model View ViewModel
SDLC โ Software Development Life Cycle
CI/CD โ Continuous Integration / Continuous Deployment
JWT โ JSON Web Token
ORM โ Object Relational Mapping
API โ Application Programming Interface
REST โ Representational State Transfer
SOAP โ Simple Object Access Protocol
Big O โ Time & Space Complexity Notation
FIFO โ First In First Out
LIFO โ Last In First Out
๐ฌ Double Tap โค๏ธ for more!
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Python Roadmap: Beginner to Advanced ๐๐ก
1๏ธโฃ Basics
โข Syntax, indentation
โข Variables & data types
โข Operators (arithmetic, logical, comparison)
โข Input/output
โข Comments
2๏ธโฃ Control Flow
โข if, elif, else
โข for loops
โข while loops
โข break, continue, pass
3๏ธโฃ Data Structures
โข Lists, Tuples, Sets, Dictionaries
โข List comprehensions
โข Nested structures
4๏ธโฃ Functions
โข def, return
โข Arguments (default, args, *kwargs)
โข Scope (local vs global)
โข Lambda functions
5๏ธโฃ Strings & File Handling
โข String methods
โข f-strings
โข Reading/writing text and CSV files
6๏ธโฃ Modules & Packages
โข import, from-import
โข Python Standard Library (math, random, datetime, os)
โข Creating your own module
7๏ธโฃ Error Handling
โข try-except
โข finally, raise
โข Custom exceptions
8๏ธโฃ Object-Oriented Programming (OOP)
โข Classes & objects
โข init, self
โข Inheritance
โข Encapsulation & polymorphism
9๏ธโฃ Advanced Concepts
โข Iterators & generators
โข Decorators
โข Context managers
โข Regular expressions
โข Comprehensions (dict, set)
๐ Working with Libraries
โข NumPy, Pandas
โข Matplotlib, Seaborn
โข Requests, BeautifulSoup (web scraping)
1๏ธโฃ1๏ธโฃ Web Development
โข Flask or Django basics
โข Routing, templates
โข REST APIs
1๏ธโฃ2๏ธโฃ Data Science / ML Intro
โข Jupyter notebooks
โข Scikit-learn basics
โข Model training & evaluation
1๏ธโฃ3๏ธโฃ Automation & Scripting
โข Automate files, emails, or browser (Selenium)
โข Schedule scripts
Project Ideas:
โข Calculator
โข Web scraper
โข To-do app
โข API backend
โข Data dashboard
๐ฌ Tap โค๏ธ for more!
1๏ธโฃ Basics
โข Syntax, indentation
โข Variables & data types
โข Operators (arithmetic, logical, comparison)
โข Input/output
โข Comments
2๏ธโฃ Control Flow
โข if, elif, else
โข for loops
โข while loops
โข break, continue, pass
3๏ธโฃ Data Structures
โข Lists, Tuples, Sets, Dictionaries
โข List comprehensions
โข Nested structures
4๏ธโฃ Functions
โข def, return
โข Arguments (default, args, *kwargs)
โข Scope (local vs global)
โข Lambda functions
5๏ธโฃ Strings & File Handling
โข String methods
โข f-strings
โข Reading/writing text and CSV files
6๏ธโฃ Modules & Packages
โข import, from-import
โข Python Standard Library (math, random, datetime, os)
โข Creating your own module
7๏ธโฃ Error Handling
โข try-except
โข finally, raise
โข Custom exceptions
8๏ธโฃ Object-Oriented Programming (OOP)
โข Classes & objects
โข init, self
โข Inheritance
โข Encapsulation & polymorphism
9๏ธโฃ Advanced Concepts
โข Iterators & generators
โข Decorators
โข Context managers
โข Regular expressions
โข Comprehensions (dict, set)
๐ Working with Libraries
โข NumPy, Pandas
โข Matplotlib, Seaborn
โข Requests, BeautifulSoup (web scraping)
1๏ธโฃ1๏ธโฃ Web Development
โข Flask or Django basics
โข Routing, templates
โข REST APIs
1๏ธโฃ2๏ธโฃ Data Science / ML Intro
โข Jupyter notebooks
โข Scikit-learn basics
โข Model training & evaluation
1๏ธโฃ3๏ธโฃ Automation & Scripting
โข Automate files, emails, or browser (Selenium)
โข Schedule scripts
Project Ideas:
โข Calculator
โข Web scraper
โข To-do app
โข API backend
โข Data dashboard
๐ฌ Tap โค๏ธ for more!
โค5
๐ ๐ ๐ฎ๐๐๐ฒ๐ฟ ๐๐ป-๐๐ฒ๐บ๐ฎ๐ป๐ฑ ๐ง๐ฒ๐ฐ๐ต ๐ฆ๐ธ๐ถ๐น๐น๐ ๐ณ๐ผ๐ฟ ๐๐ฅ๐๐ ๐ถ๐ป ๐ฎ๐ฌ๐ฎ๐ฒ ๐ฅ
Want to upgrade your tech skills without spending money?
Here are some excellent FREE YouTube resources to learn high-demand technologies through tutorials and hands-on practice.
๐ฅ Learn โ Practice โ Build Projects โ Upgrade Your Resume
๐ ๐๐ป๐ฟ๐ผ๐น๐น ๐ณ๐ผ๐ฟ ๐๐ฅ๐๐ ๐:-
https://pdlink.in/4x3B9hb
๐ฏ Perfect for Students โข Freshers โข Job Seekers โข Working Professionals
Want to upgrade your tech skills without spending money?
Here are some excellent FREE YouTube resources to learn high-demand technologies through tutorials and hands-on practice.
๐ฅ Learn โ Practice โ Build Projects โ Upgrade Your Resume
๐ ๐๐ป๐ฟ๐ผ๐น๐น ๐ณ๐ผ๐ฟ ๐๐ฅ๐๐ ๐:-
https://pdlink.in/4x3B9hb
๐ฏ Perfect for Students โข Freshers โข Job Seekers โข Working Professionals
Steps to ๐๐๐ญ ๐๐ง๐ญ๐๐ซ๐ฏ๐ข๐๐ฐ ๐๐๐ฅ๐ฅ๐ฌ from LinkedIn:
1. ๐๐ฉ๐ฉ๐ฅ๐ฒ ๐๐๐ข๐ฅ๐ฒ: Submit applications for 30-40 jobs daily to increase visibility.
2. ๐๐ข๐ฏ๐๐ซ๐ฌ๐ข๐๐ฒ ๐๐ฉ๐ฉ๐ฅ๐ข๐๐๐ญ๐ข๐จ๐ง๐ฌ: Apply for various job types, not just "easy apply" options.
3. ๐๐ฉ๐ฉ๐ฅ๐ฒ ๐๐ซ๐จ๐ฆ๐ฉ๐ญ๐ฅ๐ฒ: Turn on job alerts and apply as soon as positions are posted.
4. ๐๐๐๐ค ๐๐๐๐๐ซ๐ซ๐๐ฅ๐ฌ: For dream companies, quickly request referrals from employees. Connect with several people for better chances.
5. ๐๐ ๐๐ข๐ซ๐๐๐ญ ๐๐จ๐ซ ๐๐๐๐๐ซ๐ซ๐๐ฅs: Don't start with "Hi" or "Hello". Send a cold message (short and crisp) with what you need and the job link. If you get a response, you can share your resume for referral. Follow up after one day if needed.
6. ๐๐ฉ๐ฉ๐ฅ๐ฒ ๐๐ข๐ญ๐ก๐ข๐ง ๐๐ฅ๐ข๐ ๐ข๐๐ข๐ฅ๐ข๐ญ๐ฒ: Only apply or seek referrals for roles where you meet the qualifications (or close enough).
7. ๐๐ฉ๐ญ๐ข๐ฆ๐ข๐ณ๐ ๐๐จ๐ฎ๐ซ ๐๐ซ๐จ๐๐ข๐ฅ๐: Build a network of 500+ connections, update experiences, use a professional photo, and list relevant skills.
8. ๐๐จ๐ง๐ง๐๐๐ญ ๐ฐ๐ข๐ญ๐ก ๐๐๐๐ซ๐ฎ๐ข๐ญ๐๐ซ๐ฌ: After applying, connect with job posters and recruiters, and send your CV with a cold message (short and crisp).
9. ๐๐ง๐ก๐๐ง๐๐ ๐๐ข๐ฌ๐ข๐๐ข๐ฅ๐ข๐ญ๐ฒ: Keep your profile visible, send connection requests, and share relevant content.
10. ๐๐๐ซ๐ฌ๐จ๐ง๐๐ฅ๐ข๐ณ๐ ๐๐จ๐ง๐ง๐๐๐ญ๐ข๐จ๐ง ๐๐๐ช๐ฎ๐๐ฌ๐ญ๐ฌ: Customize requests to explain your interest.
11. ๐๐ง๐ ๐๐ ๐ ๐ฐ๐ข๐ญ๐ก ๐๐จ๐ง๐ญ๐๐ง๐ญ: Like, comment, and share posts to stay visible and expand your network.
12. ๐๐ก๐จ๐ฐ๐๐๐ฌ๐ ๐๐ฑ๐ฉ๐๐ซ๐ญ๐ข๐ฌ๐: Publish articles or posts about your field to attract potential employers.
13. ๐๐จ๐ข๐ง ๐๐ซ๐จ๐ฎ๐ฉ๐ฌ: Participate in industry-related LinkedIn groups to engage and expand your network.
14. ๐๐ฉ๐๐๐ญ๐ ๐๐๐๐๐ฅ๐ข๐ง๐ ๐๐ง๐ ๐๐ฎ๐ฆ๐ฆ๐๐ซ๐ฒ: Reflect your current role, skills, and aspirations with relevant keywords.
15. ๐๐๐ช๐ฎ๐๐ฌ๐ญ ๐๐๐๐จ๐ฆ๐ฆ๐๐ง๐๐๐ญ๐ข๐จ๐ง๐ฌ: Get endorsements from colleagues, managers, and clients.
16. ๐ ๐จ๐ฅ๐ฅ๐จ๐ฐ ๐๐จ๐ฆ๐ฉ๐๐ง๐ข๐๐ฌ: Stay updated on job openings and company news by following your target companies.
1. ๐๐ฉ๐ฉ๐ฅ๐ฒ ๐๐๐ข๐ฅ๐ฒ: Submit applications for 30-40 jobs daily to increase visibility.
2. ๐๐ข๐ฏ๐๐ซ๐ฌ๐ข๐๐ฒ ๐๐ฉ๐ฉ๐ฅ๐ข๐๐๐ญ๐ข๐จ๐ง๐ฌ: Apply for various job types, not just "easy apply" options.
3. ๐๐ฉ๐ฉ๐ฅ๐ฒ ๐๐ซ๐จ๐ฆ๐ฉ๐ญ๐ฅ๐ฒ: Turn on job alerts and apply as soon as positions are posted.
4. ๐๐๐๐ค ๐๐๐๐๐ซ๐ซ๐๐ฅ๐ฌ: For dream companies, quickly request referrals from employees. Connect with several people for better chances.
5. ๐๐ ๐๐ข๐ซ๐๐๐ญ ๐๐จ๐ซ ๐๐๐๐๐ซ๐ซ๐๐ฅs: Don't start with "Hi" or "Hello". Send a cold message (short and crisp) with what you need and the job link. If you get a response, you can share your resume for referral. Follow up after one day if needed.
6. ๐๐ฉ๐ฉ๐ฅ๐ฒ ๐๐ข๐ญ๐ก๐ข๐ง ๐๐ฅ๐ข๐ ๐ข๐๐ข๐ฅ๐ข๐ญ๐ฒ: Only apply or seek referrals for roles where you meet the qualifications (or close enough).
7. ๐๐ฉ๐ญ๐ข๐ฆ๐ข๐ณ๐ ๐๐จ๐ฎ๐ซ ๐๐ซ๐จ๐๐ข๐ฅ๐: Build a network of 500+ connections, update experiences, use a professional photo, and list relevant skills.
8. ๐๐จ๐ง๐ง๐๐๐ญ ๐ฐ๐ข๐ญ๐ก ๐๐๐๐ซ๐ฎ๐ข๐ญ๐๐ซ๐ฌ: After applying, connect with job posters and recruiters, and send your CV with a cold message (short and crisp).
9. ๐๐ง๐ก๐๐ง๐๐ ๐๐ข๐ฌ๐ข๐๐ข๐ฅ๐ข๐ญ๐ฒ: Keep your profile visible, send connection requests, and share relevant content.
10. ๐๐๐ซ๐ฌ๐จ๐ง๐๐ฅ๐ข๐ณ๐ ๐๐จ๐ง๐ง๐๐๐ญ๐ข๐จ๐ง ๐๐๐ช๐ฎ๐๐ฌ๐ญ๐ฌ: Customize requests to explain your interest.
11. ๐๐ง๐ ๐๐ ๐ ๐ฐ๐ข๐ญ๐ก ๐๐จ๐ง๐ญ๐๐ง๐ญ: Like, comment, and share posts to stay visible and expand your network.
12. ๐๐ก๐จ๐ฐ๐๐๐ฌ๐ ๐๐ฑ๐ฉ๐๐ซ๐ญ๐ข๐ฌ๐: Publish articles or posts about your field to attract potential employers.
13. ๐๐จ๐ข๐ง ๐๐ซ๐จ๐ฎ๐ฉ๐ฌ: Participate in industry-related LinkedIn groups to engage and expand your network.
14. ๐๐ฉ๐๐๐ญ๐ ๐๐๐๐๐ฅ๐ข๐ง๐ ๐๐ง๐ ๐๐ฎ๐ฆ๐ฆ๐๐ซ๐ฒ: Reflect your current role, skills, and aspirations with relevant keywords.
15. ๐๐๐ช๐ฎ๐๐ฌ๐ญ ๐๐๐๐จ๐ฆ๐ฆ๐๐ง๐๐๐ญ๐ข๐จ๐ง๐ฌ: Get endorsements from colleagues, managers, and clients.
16. ๐ ๐จ๐ฅ๐ฅ๐จ๐ฐ ๐๐จ๐ฆ๐ฉ๐๐ง๐ข๐๐ฌ: Stay updated on job openings and company news by following your target companies.
โค4
๐ ๐๐ฅ๐๐ ๐๐ถ๐๐ถ ๐ฉ๐ถ๐ฟ๐๐๐ฎ๐น ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐ฃ๐ฟ๐ผ๐ด๐ฟ๐ฎ๐บ๐ ๐ | Boost Your Resume
Citi offers virtual experience programs designed to help students and freshers develop job-ready skills through real-world tasks.
โ 100% FREE
โ Self-paced learning
โ Real-world projects
โ Certificate on completion
โ Add the experience to your Resume & LinkedIn
๐ ๐๐ป๐ฟ๐ผ๐น๐น ๐ณ๐ผ๐ฟ ๐๐ฅ๐๐ ๐:-
https://pdlink.in/4zZqJ4U
๐ฅ Learn โ Complete Projects โ Earn Certificate โ Strengthen Your Resume
Citi offers virtual experience programs designed to help students and freshers develop job-ready skills through real-world tasks.
โ 100% FREE
โ Self-paced learning
โ Real-world projects
โ Certificate on completion
โ Add the experience to your Resume & LinkedIn
๐ ๐๐ป๐ฟ๐ผ๐น๐น ๐ณ๐ผ๐ฟ ๐๐ฅ๐๐ ๐:-
https://pdlink.in/4zZqJ4U
๐ฅ Learn โ Complete Projects โ Earn Certificate โ Strengthen Your Resume
โค1
๐ ๐ง๐๐ง๐ ๐๐ฟ๐ผ๐๐ฝ ๐๐ฅ๐๐ ๐ฉ๐ถ๐ฟ๐๐๐ฎ๐น ๐๐ป๐๐ฒ๐ฟ๐ป๐๐ต๐ถ๐ฝ ๐ฃ๐ฟ๐ผ๐ด๐ฟ๐ฎ๐บ๐ ๐
Tata Group/TCS virtual job simulations let you work through industry-style tasks and strengthen your resume.
๐ 3 FREE Virtual Programs:
๐ Data Visualisation
๐ Cybersecurity
๐ฑ ESG (Environmental, Social & Governance)
๐ป Virtual & flexible
๐ Free Certificate on Completion
๐ Add the experience to your Resume/LinkedIn
๐ ๐๐ป๐ฟ๐ผ๐น๐น ๐ณ๐ผ๐ฟ ๐๐ฅ๐๐ ๐:-
https://pdlink.in/4yoXEOI
๐ฅ Perfect for Students โข Freshers โข Job Seekers
Tata Group/TCS virtual job simulations let you work through industry-style tasks and strengthen your resume.
๐ 3 FREE Virtual Programs:
๐ Data Visualisation
๐ Cybersecurity
๐ฑ ESG (Environmental, Social & Governance)
๐ป Virtual & flexible
๐ Free Certificate on Completion
๐ Add the experience to your Resume/LinkedIn
๐ ๐๐ป๐ฟ๐ผ๐น๐น ๐ณ๐ผ๐ฟ ๐๐ฅ๐๐ ๐:-
https://pdlink.in/4yoXEOI
๐ฅ Perfect for Students โข Freshers โข Job Seekers
โค1
๐ ๐ง๐ผ๐ฝ ๐ง๐ฒ๐ฐ๐ต ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป๐ ๐๐ผ ๐๐ฎ๐ป๐ฑ ๐๐ถ๐ด๐ต-๐ฃ๐ฎ๐๐ถ๐ป๐ด ๐๐ผ๐ฏ๐ ๐ถ๐ป ๐ฎ๐ฌ๐ฎ๐ฒ๐
๐ฐ Highest Salary: โน41 LPA
๐ Average Salary: โน7.4 LPA
๐ 2,000+ Students Placed
๐ข 500+ Hiring Partners
๐ป Full Stack :- https://pdlink.in/3SuUeuD
๐ Data Analytics :- https://pdlink.in/45vk5ph
๐ซAI Engineering :- https://pdlink.in/4fWJVID
๐ฅ Take the first step towards your high-paying tech career in 2026!
๐ฐ Highest Salary: โน41 LPA
๐ Average Salary: โน7.4 LPA
๐ 2,000+ Students Placed
๐ข 500+ Hiring Partners
๐ป Full Stack :- https://pdlink.in/3SuUeuD
๐ Data Analytics :- https://pdlink.in/45vk5ph
๐ซAI Engineering :- https://pdlink.in/4fWJVID
๐ฅ Take the first step towards your high-paying tech career in 2026!
๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ ๐๐ฅ๐๐ ๐ข๐ป๐น๐ถ๐ป๐ฒ ๐ ๐ฎ๐๐๐ฒ๐ฟ๐ฐ๐น๐ฎ๐๐ ๐
๐ซAccelerate your career in Data Science
๐ซDiscover the skills, tools and career roadmap needed to enter this high-demand field.
๐ฅ Beginner-friendly online sessionโno prior experience required!
๐ฅ๐ฒ๐ด๐ถ๐๐๐ฒ๐ฟ ๐๐ผ๐ฟ ๐๐ฅ๐๐ ๐:-
https://pdlink.in/46adC3l
(Only few slots left )
๐ Date: September 11, 2026
โฐ Time: 7:00 PM
๐ซAccelerate your career in Data Science
๐ซDiscover the skills, tools and career roadmap needed to enter this high-demand field.
๐ฅ Beginner-friendly online sessionโno prior experience required!
๐ฅ๐ฒ๐ด๐ถ๐๐๐ฒ๐ฟ ๐๐ผ๐ฟ ๐๐ฅ๐๐ ๐:-
https://pdlink.in/46adC3l
(Only few slots left )
๐ Date: September 11, 2026
โฐ Time: 7:00 PM
๐ง๐ผ๐ฝ ๐ฑ ๐๐ฅ๐๐ ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐๐ผ ๐๐ถ๐ฐ๐ธ๐๐๐ฎ๐ฟ๐ ๐ฌ๐ผ๐๐ฟ ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ ๐๐ฎ๐ฟ๐ฒ๐ฒ๐ฟ ๐
Want to start a career in Data Science without spending money?
Here are 5 beginner-friendly learning resources covering essential skills such as Python, SQL, Machine Learning and hands-on projects.
๐ ๐๐ป๐ฟ๐ผ๐น๐น ๐ณ๐ผ๐ฟ ๐๐ฅ๐๐ ๐:-
https://pdlink.in/4ilAmok
๐ฏ Perfect for Students โข Freshers โข Beginners โข Aspiring Data Scientists
๐ก Learn โ Practice โ Build Projects โ Create Your Portfolio
Want to start a career in Data Science without spending money?
Here are 5 beginner-friendly learning resources covering essential skills such as Python, SQL, Machine Learning and hands-on projects.
๐ ๐๐ป๐ฟ๐ผ๐น๐น ๐ณ๐ผ๐ฟ ๐๐ฅ๐๐ ๐:-
https://pdlink.in/4ilAmok
๐ฏ Perfect for Students โข Freshers โข Beginners โข Aspiring Data Scientists
๐ก Learn โ Practice โ Build Projects โ Create Your Portfolio
โ
Useful Coding Platforms for Beginners ๐ป๐
1๏ธโฃ freeCodeCamp
โฆ Learn HTML, CSS, JavaScript, Python, Data Science
โฆ 100% free, project-based, certifications included
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2๏ธโฃ The Odin Project
โฆ Full web development curriculum (Frontend + Backend)
โฆ Hands-on projects and GitHub practice
โฆ Great for becoming a full-stack developer
3๏ธโฃ Codecademy (Free Tier)
โฆ Interactive lessons in Python, JavaScript, HTML/CSS, SQL
โฆ Great UI and beginner-friendly platform
4๏ธโฃ Coursera (Free Auditing)
โฆ Learn Python, Data Analysis, Algorithms, etc. from top universities
โฆ Use โAuditโ option to access most courses for free
5๏ธโฃ edX (Audit for Free)
โฆ Free university-level programming courses
โฆ Python, Java, C++, Web Dev, and more
6๏ธโฃ W3Schools
โฆ Simple tutorials for HTML, CSS, JS, PHP, SQL
โฆ Try code in-browser
โฆ Good for quick learning or syntax reference
7๏ธโฃ Sololearn
โฆ Free mobile app to learn Python, C++, Java, JS, etc.
โฆ Practice with code snippets and community support
8๏ธโฃ Khan Academy
โฆ Learn programming basics, algorithms, and JS animations
โฆ Visual and beginner-friendly
9๏ธโฃ Harvard CS50 (via edX)
โฆ One of the best free intro to Computer Science courses
โฆ Project-based and in-depth
๐ Exercism
โฆ Practice coding in 60+ languages
โฆ Real feedback from mentors
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๐ฌ Save this & Tap โค๏ธ if this helped you!
1๏ธโฃ freeCodeCamp
โฆ Learn HTML, CSS, JavaScript, Python, Data Science
โฆ 100% free, project-based, certifications included
โฆ Ideal for self-paced learners
2๏ธโฃ The Odin Project
โฆ Full web development curriculum (Frontend + Backend)
โฆ Hands-on projects and GitHub practice
โฆ Great for becoming a full-stack developer
3๏ธโฃ Codecademy (Free Tier)
โฆ Interactive lessons in Python, JavaScript, HTML/CSS, SQL
โฆ Great UI and beginner-friendly platform
4๏ธโฃ Coursera (Free Auditing)
โฆ Learn Python, Data Analysis, Algorithms, etc. from top universities
โฆ Use โAuditโ option to access most courses for free
5๏ธโฃ edX (Audit for Free)
โฆ Free university-level programming courses
โฆ Python, Java, C++, Web Dev, and more
6๏ธโฃ W3Schools
โฆ Simple tutorials for HTML, CSS, JS, PHP, SQL
โฆ Try code in-browser
โฆ Good for quick learning or syntax reference
7๏ธโฃ Sololearn
โฆ Free mobile app to learn Python, C++, Java, JS, etc.
โฆ Practice with code snippets and community support
8๏ธโฃ Khan Academy
โฆ Learn programming basics, algorithms, and JS animations
โฆ Visual and beginner-friendly
9๏ธโฃ Harvard CS50 (via edX)
โฆ One of the best free intro to Computer Science courses
โฆ Project-based and in-depth
๐ Exercism
โฆ Practice coding in 60+ languages
โฆ Real feedback from mentors
โฆ Ideal for improving problem-solving
๐ฌ Save this & Tap โค๏ธ if this helped you!
โค4