Part 1: Introduction and Preparation Strategy

My Coding Interview Journey

I still remember my first technical interview. I was confident in my coding skills from personal projects, but when the interviewer asked me to reverse a linked list on a whiteboard, I froze. That experience taught me that coding interviews are a skill separate from being a good developer—a skill that requires deliberate practice.

After that humbling experience, I spent months preparing systematically. This preparation not only helped me land positions at several tech companies but also taught me how to approach problem-solving more effectively in my day-to-day work.

What Are Technical Coding Interviews?

Technical interviews assess your problem-solving abilities, coding skills, and communication. They typically consist of:

Interview Format

1. Coding Rounds (45-60 minutes each)

   • Algorithm and data structure problems

   • Live coding with an interviewer

   • Usually 1-2 problems per session

2. System Design (Senior positions)

   • Design scalable systems

   • Discuss trade-offs

   • Architecture decisions

3. Behavioral Interviews

   • Past experiences

   • Team collaboration

   • Conflict resolution

The Reality of Interview Preparation

From my experience, here's what I learned:

Common Misconceptions

❌ "I'm a great developer, so I'll do fine"

• Interviews test specific skills that require practice

• Being good at building projects doesn't automatically translate to interview success

❌ "I'll just solve 1000 problems"

• Quality over quantity matters

• Understanding patterns is more important than memorizing solutions

❌ "I need to know every algorithm"

• Focus on common patterns

• Deep understanding of fundamentals beats superficial knowledge of everything

What Actually Works

✅ Structured preparation with a clear plan ✅ Understanding patterns rather than memorizing solutions ✅ Consistent practice over cramming ✅ Mock interviews to simulate pressure ✅ Learning from mistakes through review

My Preparation Timeline

Here's the approach that worked for me:

3-Month Preparation Plan

Month 1: Foundations

• Week 1-2: Arrays and Strings

• Week 3-4: Hash Tables and Two Pointers

• Goal: 20-25 easy problems, understand solutions deeply

Month 2: Core Data Structures

• Week 1: Linked Lists and Stacks/Queues

• Week 2: Trees (Binary Trees, BST)

• Week 3: Graphs (BFS, DFS)

• Week 4: Heaps and Practice

• Goal: 25-30 medium problems

Month 3: Advanced Topics

• Week 1-2: Dynamic Programming

• Week 3: Mock Interviews

• Week 4: Review weak areas

• Goal: 15-20 medium/hard problems, 5+ mock interviews

Daily Schedule

When I was preparing while working full-time:

Morning (6:00 AM - 7:30 AM):
- 1 new problem or review previous day's problem
- Focus on understanding, not just solving

Evening (8:00 PM - 9:30 PM):
- Study data structure theory
- Practice pattern recognition
- Review solutions and write notes

Weekends:
- Saturday: 3-4 problems + 1 mock interview
- Sunday: Review week's problems, update notes

Essential Resources

Based on what helped me most:

Learning Platforms

1. LeetCode (My primary platform)

   • Best problem collection

   • Company-specific questions

   • Discussion forum with explanations

2. NeetCode (Pattern-based learning)

   • Curated problem list

   • Video explanations

   • Pattern recognition focus

3. AlgoExpert (Structured curriculum)

   • Video walkthroughs

   • Well-organized

   • Good for beginners

Books I Read

1. "Cracking the Coding Interview" by Gayle Laakmann McDowell

   • Essential preparation guide

   • Interview process insights

   • Practice problems

2. "Elements of Programming Interviews in Python"

   • Python-specific solutions

   • In-depth explanations

   • Advanced techniques

YouTube Channels

• NeetCode: Pattern-based problem solving

• Back To Back SWE: Deep algorithm explanations

• Errichto: Competitive programming insights

Understanding Problem Difficulty

From my experience, here's how I categorize problems:

Easy Problems

Characteristics:

• Direct application of concepts

• Usually 1-2 steps to solution

• Common in screening rounds

Example: Two Sum

def two_sum(nums: list[int], target: int) -> list[int]:
    """Find two numbers that add up to target"""
    seen = {}
    for i, num in enumerate(nums):
        complement = target - num
        if complement in seen:
            return [seen[complement], i]
        seen[num] = i
    return []

Medium Problems

Characteristics:

• Multiple concepts combined

• Requires pattern recognition

• Most common in interviews

Example: Longest Substring Without Repeating Characters

def length_of_longest_substring(s: str) -> int:
    """Find length of longest substring without repeating characters"""
    char_map = {}
    left = 0
    max_length = 0
    
    for right, char in enumerate(s):
        if char in char_map and char_map[char] >= left:
            left = char_map[char] + 1
        
        char_map[char] = right
        max_length = max(max_length, right - left + 1)
    
    return max_length

Hard Problems

Characteristics:

• Complex logic

• Multiple optimizations needed

• Less common in most interviews

Truth: In my experience, only about 20% of interviews included hard problems, and they were usually for senior positions.

The Interview Process Flow

Based on companies I've interviewed with:

Building the Right Mindset

What I learned about mindset through my journey:

Embrace Failure as Learning

In my preparation, I failed at problems constantly. Here's how I reframed it:

❌ Wrong mindset: "I couldn't solve this, I'm not good enough" ✅ Right mindset: "I learned a new pattern I didn't know before"

The 30-Minute Rule

My personal rule when practicing:

• Spend 15-20 minutes attempting the problem

• If stuck, look at hints (not full solution)

• After 30 minutes without progress, study the solution

• Critical: Implement it yourself, don't copy-paste

• Come back and solve it again the next day

Track Your Progress

I maintained a spreadsheet with:

• Problem name and link

• Difficulty

• Patterns used

• Time taken

• Whether I solved it independently

• Review dates

This showed me my progress and identified weak areas.

Common Patterns Preview

These are the patterns I encountered most frequently:

1. Two Pointers (Arrays/Strings)

2. Sliding Window (Subarray problems)

3. Hash Maps (Fast lookups)

4. BFS/DFS (Trees/Graphs)

5. Dynamic Programming (Optimization)

6. Binary Search (Sorted arrays)

7. Backtracking (Generating combinations)

We'll explore each in detail in upcoming parts.

The Communication Component

Something I initially underestimated: communication matters as much as coding.

What Interviewers Look For

From conducting interviews myself, I evaluate:

1. Clarifying Questions

   • Do they ask about edge cases?

   • Do they understand the problem fully?

2. Thinking Process

   • Can they explain their approach?

   • Do they consider trade-offs?

3. Code Quality

   • Is the code clean and readable?

   • Do they use meaningful variable names?

4. Testing Mindset

   • Do they test their solution?

   • Do they think about edge cases?

Example Communication Flow

Poor approach:

Interviewer: "Find the duplicate number in an array"
Candidate: *starts coding immediately*

Good approach:

Interviewer: "Find the duplicate number in an array"

Candidate: 
"Let me make sure I understand. We have an array of integers, 
and exactly one number appears more than once? 

Are there any constraints on:
- Array size?
- Range of numbers?
- Space complexity?

Can I assume the array is not sorted?

Okay, let me think about approaches:
1. Hash set - O(n) time, O(n) space
2. Sort then scan - O(n log n) time, O(1) space
3. Since numbers are in range 1 to n, I could use array as hash

Which approach would you prefer I implement?"

Setting Realistic Expectations

Based on my experience and observing others:

Interview Success Rates

From my observation:

• Entry Level: 5-10% offer rate (lots of applicants)

• Mid Level: 10-20% offer rate (fewer applicants, higher bar)

• Senior Level: 15-25% offer rate (specialized skills)

Key insight: Rejection is normal. I was rejected by multiple companies before getting offers. Each rejection taught me something.

Time Investment

Realistic time needed based on starting point:

• Strong CS background: 1-2 months of focused prep

• Self-taught developer: 3-4 months of structured prep

• Career switcher: 4-6 months of intensive prep

I fell into the second category and spent 3 months preparing.

Your First Step

Here's what to do today:

1. Assess Your Level

   • Try 3 easy problems on LeetCode

   • Try 1 medium problem

   • Be honest about what you know vs. don't know

2. Set Up Your Environment

   # Create a practice structure
   coding-interview-prep/
   ├── patterns/
   │   ├── two_pointers.py
   │   ├── sliding_window.py
   │   └── ...
   ├── problems/
   │   ├── arrays/
   │   ├── strings/
   │   └── ...
   └── notes/
       └── learnings.md

3. Create Your Study Plan

   • Block time in your calendar

   • Set weekly goals

   • Join a study group or find an accountability partner

Key Takeaways

From my preparation journey:

1. Interview skills are learnable - You don't need to be a genius

2. Consistency beats intensity - Daily practice trumps marathon sessions

3. Understand, don't memorize - Focus on patterns and principles

4. Communication is crucial - Talk through your thinking process

5. Track your progress - It keeps you motivated and identifies gaps

6. Mock interviews are essential - Nothing simulates pressure like practice

7. Rejection is part of the process - Learn and move forward

What's Next?

In Part 2: Problem-Solving Framework and Patterns, we'll dive into:

• A systematic approach to solving any coding problem

• The most common coding patterns with Python examples

• How to recognize which pattern to apply

• Effective communication strategies during interviews

We'll start building your problem-solving toolkit!

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This article is based on my personal journey preparing for and conducting technical interviews at various tech companies.