How to get job as python fresher?
1. Get Your Python Fundamentals Strong
You should have a clear understanding of Python syntax, statements, variables & operators, control structures, functions & modules, OOP concepts, exception handling, and various other concepts before going out for a Python interview.
2. Learn Python Frameworks
As a beginner, you’re recommended to start with Django as it is considered the standard framework for Python by many developers. An adequate amount of experience with frameworks will not only help you to dive deeper into the Python world but will also help you to stand out among other Python freshers.
3. Build Some Relevant Projects
You can start it by building several minor projects such as Number guessing game, Hangman Game, Website Blocker, and many others. Also, you can opt to build few advanced-level projects once you’ll learn several Python web frameworks and other trending technologies.
@crackingthecodinginterview
4. Get Exposure to Trending Technologies Using Python.
Python is being used with almost every latest tech trend whether it be Artificial Intelligence, Internet of Things (IOT), Cloud Computing, or any other. And getting exposure to these upcoming technologies using Python will not only make you industry-ready but will also give you an edge over others during a career opportunity.
5. Do an Internship & Grow Your Network.
You need to connect with those professionals who are already working in the same industry in which you are aspiring to get into such as Data Science, Machine learning, Web Development, etc.
1. Get Your Python Fundamentals Strong
You should have a clear understanding of Python syntax, statements, variables & operators, control structures, functions & modules, OOP concepts, exception handling, and various other concepts before going out for a Python interview.
2. Learn Python Frameworks
As a beginner, you’re recommended to start with Django as it is considered the standard framework for Python by many developers. An adequate amount of experience with frameworks will not only help you to dive deeper into the Python world but will also help you to stand out among other Python freshers.
3. Build Some Relevant Projects
You can start it by building several minor projects such as Number guessing game, Hangman Game, Website Blocker, and many others. Also, you can opt to build few advanced-level projects once you’ll learn several Python web frameworks and other trending technologies.
@crackingthecodinginterview
4. Get Exposure to Trending Technologies Using Python.
Python is being used with almost every latest tech trend whether it be Artificial Intelligence, Internet of Things (IOT), Cloud Computing, or any other. And getting exposure to these upcoming technologies using Python will not only make you industry-ready but will also give you an edge over others during a career opportunity.
5. Do an Internship & Grow Your Network.
You need to connect with those professionals who are already working in the same industry in which you are aspiring to get into such as Data Science, Machine learning, Web Development, etc.
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📩 Correct Way to Mail a Resume
📩 Message to a Recruiter After Seeing Their Job Posting
✉️ Warm Networking DM
Subject: Application For The [Role] at [Company Name]
Dear [Hiring Manager’s Name],
I hope you’re doing great. I came across the [Position Title] role at [Company Name] and was really excited about the opportunity to apply. With my experience in [mention key relevant experience], I believe I could bring value to your team.
I’ve attached my Resume for your review. I trust my background aligns with what you’re looking for, I’d love the chance to discuss how I can contribute to your team. Looking forward to hearing your thoughts!
Best regards,
[Your Name]
[Link To Linkedin]
[Link To Resume]
📩 Message to a Recruiter After Seeing Their Job Posting
Subject: Excited to Apply for [Position Title] at [Company Name]
Hi [Recruiter’s Name],
I trust you have a awesome day today 🙂
I just saw your post about the [Position Title] opening at [Company Name], and I couldn’t wait to reach out! I’ve been following [Company Name]
for a while now, and I truly admire [mention something specific—company’s projects, culture, values, recent achievements].
With my expertise in [mention relevant skills/experience], I believe I’d be a great fit for this role. I’ve attached my Resume for your review, and I’d love the chance to discuss how my experience can contribute to your team.
Would you be open to a quick chat?
Looking forward to your thoughts!
[Your Resume]
✉️ Warm Networking DM
Subject: Exploring Opportunities at [Company Name]
Hi [First Name],
I believe you have a wonderful day today 😊
I’m a [Your Role] specializing in [mention key skills]. I’ve been following [Company Name] for a while and love [mention something specific about their work, culture, or achievements].
With experience in [mention a key project or skill], I believe I could bring value to your team. If you’re open to it, I’d love to chat about any opportunities, where my skills could be a great fit.
I know you must get a ton of messages, so I really appreciate your time. Looking forward to hearing from you!
Warm,
[Your Name]
[Your Resume]
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Coding Interview Resources
Let's now move to next important concept asked in coding interviews: Linked Lists: Linked Lists test your ability to handle pointers, edge cases, and memory efficiency. They show up in both beginner and advanced interview rounds. 2.1. Reverse a Linked List…
Let’s dive into the frequently asked coding interview topic now: Hashing & Maps
Hashing helps us access data quickly, making it a critical topic in coding interviews. HashMaps (or dictionaries) provide constant-time access and are used to solve problems like counting elements, finding duplicates, and mapping data efficiently.
3.1. Use Hash Maps for Fast Lookups
Example:
Check if two strings are anagrams.
Given s1 = "listen", s2 = "silent", are they anagrams?
Solution:
Use a hash map to count the frequency of characters in both strings and compare the counts.
Concept tested:
Efficient searching and counting with O(1) average time complexity.
3.2. Count Frequency of Elements
Example:
Given an array nums = [1, 2, 2, 3, 3, 3, 4], count the frequency of each element.
Solution:
Use a hash map to store the counts:
{1: 1, 2: 2, 3: 3, 4: 1}
It tests how well you can group data efficiently and manage it using hash-based structures.
3.3. Find Duplicates
Example:
Given arr = [4, 5, 6, 7, 5, 8], find the first duplicate.
Solution:
Use a hash map to track seen elements as you traverse the array. If you encounter an element already in the map, it’s a duplicate.
It tests your ability to solve problems with constant time lookups for duplicates.
3.4. Two Sum Problem
Example:
Given nums = [2, 7, 11, 15] and target = 9, return indices of the two numbers that add up to the target.
Solution:
Use a hash map to track the difference between the target and the current number. When you find a match, return the indices.
Concept tested:
Efficient search for pairs in a single pass with O(n) time complexity.
3.5. Implement LRU Cache
Example:
Design a cache that stores the most recently used items, evicting the least recently used when it exceeds its capacity.
Solution:
Use a hash map to store the cache and a doubly linked list to keep track of the order of usage. Combine both to make retrieval and eviction O(1).
This tests your ability to combine hash maps with other data structures like linked lists, and to implement efficient solutions with constraints.
Hash maps are frequently used in problems involving counting, grouping, and mapping, especially when you need to reduce time complexity from quadratic to linear.
React with ❤️ once you're ready for the next topic: Recursion & Backtracking
Top 7 Coding Interview Concepts: https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X/720
Top 7 Python Concepts: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L/1452
ENJOY LEARNING 👍👍
Hashing helps us access data quickly, making it a critical topic in coding interviews. HashMaps (or dictionaries) provide constant-time access and are used to solve problems like counting elements, finding duplicates, and mapping data efficiently.
3.1. Use Hash Maps for Fast Lookups
Example:
Check if two strings are anagrams.
Given s1 = "listen", s2 = "silent", are they anagrams?
Solution:
Use a hash map to count the frequency of characters in both strings and compare the counts.
Concept tested:
Efficient searching and counting with O(1) average time complexity.
3.2. Count Frequency of Elements
Example:
Given an array nums = [1, 2, 2, 3, 3, 3, 4], count the frequency of each element.
Solution:
Use a hash map to store the counts:
{1: 1, 2: 2, 3: 3, 4: 1}
It tests how well you can group data efficiently and manage it using hash-based structures.
3.3. Find Duplicates
Example:
Given arr = [4, 5, 6, 7, 5, 8], find the first duplicate.
Solution:
Use a hash map to track seen elements as you traverse the array. If you encounter an element already in the map, it’s a duplicate.
It tests your ability to solve problems with constant time lookups for duplicates.
3.4. Two Sum Problem
Example:
Given nums = [2, 7, 11, 15] and target = 9, return indices of the two numbers that add up to the target.
Solution:
Use a hash map to track the difference between the target and the current number. When you find a match, return the indices.
Concept tested:
Efficient search for pairs in a single pass with O(n) time complexity.
3.5. Implement LRU Cache
Example:
Design a cache that stores the most recently used items, evicting the least recently used when it exceeds its capacity.
Solution:
Use a hash map to store the cache and a doubly linked list to keep track of the order of usage. Combine both to make retrieval and eviction O(1).
This tests your ability to combine hash maps with other data structures like linked lists, and to implement efficient solutions with constraints.
Hash maps are frequently used in problems involving counting, grouping, and mapping, especially when you need to reduce time complexity from quadratic to linear.
React with ❤️ once you're ready for the next topic: Recursion & Backtracking
Top 7 Coding Interview Concepts: https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X/720
Top 7 Python Concepts: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L/1452
ENJOY LEARNING 👍👍
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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:
🔹 Write pure functions as often as possible.
🔹 Use pure functions because they are easier to reason with and can help reduce bugs in your implementation.
🔹 Avoid mutating the parameters passed into your function, especially if they are passed by reference, unless you are sure of what you are doing.
🔹 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.
🔹 Avoid relying on mutating global variables. Global variables introduce state.
🔹 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:
🔹 Write pure functions as often as possible.
🔹 Use pure functions because they are easier to reason with and can help reduce bugs in your implementation.
🔹 Avoid mutating the parameters passed into your function, especially if they are passed by reference, unless you are sure of what you are doing.
🔹 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.
🔹 Avoid relying on mutating global variables. Global variables introduce state.
🔹 Make sure that you do not accidentally mutate global variables, especially if you have to rely on them.
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Javascript is everywhere. Millions of webpages are built on JS.
Let’s discuss some of the basic concept of javascript which are important to learn for any Javascript developer.
1 Scope
2 Hoisting
3 Closures
4 Callbacks
5 Promises
6 Async & Await
Let’s discuss some of the basic concept of javascript which are important to learn for any Javascript developer.
1 Scope
2 Hoisting
3 Closures
4 Callbacks
5 Promises
6 Async & Await
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Top 10 Must-Know Coding Concepts every interviewer expects you to know.
Save this. Share this. 👇
*1. Arrays & Strings – The Basics That Build Everything*
Arrays are ordered collections. Strings are just arrays of characters.
You’ll use them in 90% of coding problems.
Beginner Example: Find the max number in an array, reverse a string, check if it’s a palindrome.
Start with: Leetcode Easy Array Problems
*2. Hashing – Remember Stuff Fast*
What it is: Like a super-efficient locker room. You store and find things instantly using keys.
Real Use-case: Count frequencies, detect duplicates, group similar data.
Example: Check if two strings are anagrams.
Use: HashMap or Dictionary in Python
*3. Recursion – When Functions Call Themselves*
What it is: A function solving a smaller version of the same problem.
Looks Scary? It’s not. Think of solving a puzzle by solving one piece at a time.
Example: Factorial, Fibonacci numbers.
Golden Rule: Always define a base case, or it loops forever!
*4. Backtracking – Trial & Error, Smartly Done*
What it is: Try all possible options, but drop paths that don’t work early.
Real World Analogy: Like navigating a maze – go back if you hit a wall.
Example: Sudoku Solver, N-Queens Problem
*5. Dynamic Programming (DP) – Avoid Repeating Work*
What it is: Break problems into smaller parts and store the result so you don’t repeat it.
Example: Fibonacci using DP instead of recursion (faster!)
*6. Sliding Window – Efficient Way to Check Patterns in a Row*
What it is: Instead of checking every combination, move a “window” across the array to find answers.
Example: Find max sum of subarray of size K.
Great for string and array problems.
*7. Trees – Hierarchical Data You Must Understand*
What it is: Like a family tree. Each node can have children.
Key Terms: Root, Leaf, Binary Tree, BST
Why it’s asked: Real apps like file systems, websites use trees.
Example: Inorder/Preorder/Postorder Traversals
*8. Graphs – Networks of Connections*
What it is: Nodes connected by edges. Can go in any direction.
Examples: Maps, social media friends, recommendation engines
Learn: BFS (Breadth-First Search), DFS (Depth-First Search)
*9. Greedy – Pick Best at Every Step (Fast but Risky)*
What it is: Make the best local choice hoping it leads to the global best.
Good For Simple optimization problems
Example: Activity Selection, Coin Change (with greedy strategy)
*10. Bit Manipulation – Play with 0s and 1s*
What it is: Perform operations directly on binary representations. It’s super fast and memory-efficient
Example: Check if a number is a power of 2, find the only non-repeating element
What to Do Next (Action Plan):
- Start with Arrays, then move to Hashing
- Try Recursion + Backtracking next
- Once comfy, go into DP, Graphs, and Trees
- Use platforms like Leetcode (easy → medium), GeeksforGeeks, or Neetcode
If this helped, drop a ❤️ and share with your coding gang.
Programming Resources: 👇 https://whatsapp.com/channel/0029VahiFZQ4o7qN54LTzB17
Save this. Share this. 👇
*1. Arrays & Strings – The Basics That Build Everything*
Arrays are ordered collections. Strings are just arrays of characters.
You’ll use them in 90% of coding problems.
Beginner Example: Find the max number in an array, reverse a string, check if it’s a palindrome.
Start with: Leetcode Easy Array Problems
*2. Hashing – Remember Stuff Fast*
What it is: Like a super-efficient locker room. You store and find things instantly using keys.
Real Use-case: Count frequencies, detect duplicates, group similar data.
Example: Check if two strings are anagrams.
Use: HashMap or Dictionary in Python
*3. Recursion – When Functions Call Themselves*
What it is: A function solving a smaller version of the same problem.
Looks Scary? It’s not. Think of solving a puzzle by solving one piece at a time.
Example: Factorial, Fibonacci numbers.
Golden Rule: Always define a base case, or it loops forever!
*4. Backtracking – Trial & Error, Smartly Done*
What it is: Try all possible options, but drop paths that don’t work early.
Real World Analogy: Like navigating a maze – go back if you hit a wall.
Example: Sudoku Solver, N-Queens Problem
*5. Dynamic Programming (DP) – Avoid Repeating Work*
What it is: Break problems into smaller parts and store the result so you don’t repeat it.
Example: Fibonacci using DP instead of recursion (faster!)
*6. Sliding Window – Efficient Way to Check Patterns in a Row*
What it is: Instead of checking every combination, move a “window” across the array to find answers.
Example: Find max sum of subarray of size K.
Great for string and array problems.
*7. Trees – Hierarchical Data You Must Understand*
What it is: Like a family tree. Each node can have children.
Key Terms: Root, Leaf, Binary Tree, BST
Why it’s asked: Real apps like file systems, websites use trees.
Example: Inorder/Preorder/Postorder Traversals
*8. Graphs – Networks of Connections*
What it is: Nodes connected by edges. Can go in any direction.
Examples: Maps, social media friends, recommendation engines
Learn: BFS (Breadth-First Search), DFS (Depth-First Search)
*9. Greedy – Pick Best at Every Step (Fast but Risky)*
What it is: Make the best local choice hoping it leads to the global best.
Good For Simple optimization problems
Example: Activity Selection, Coin Change (with greedy strategy)
*10. Bit Manipulation – Play with 0s and 1s*
What it is: Perform operations directly on binary representations. It’s super fast and memory-efficient
Example: Check if a number is a power of 2, find the only non-repeating element
What to Do Next (Action Plan):
- Start with Arrays, then move to Hashing
- Try Recursion + Backtracking next
- Once comfy, go into DP, Graphs, and Trees
- Use platforms like Leetcode (easy → medium), GeeksforGeeks, or Neetcode
If this helped, drop a ❤️ and share with your coding gang.
Programming Resources: 👇 https://whatsapp.com/channel/0029VahiFZQ4o7qN54LTzB17
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