Software Engineering Coding Interview Prep

Master data structures, algorithms, and coding patterns for top tech interviews

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Overview

This comprehensive guide covers everything you need to ace the coding portion of your technical interviews at any top company. From fundamental data structures to advanced dynamic programming, each topic includes:

• Clear explanations with visual diagrams
• Python solutions with complexity analysis
• Common interview patterns and follow-up questions
• Matplotlib visualization scripts for algorithm understanding

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Quick Navigation

Getting Started

Document	Description
Interview Tips	Proven strategies for technical interviews
Choosing a Language	Python vs Java vs C++ comparison
Practice Warm-Up	Three problems to calibrate your readiness

Fast Track (Limited Time?)

Document	Description
Fast Track Overview	2-week intensive preparation plan
Arrays & Pointers	Core techniques for 60%+ of interviews
Binary Search & Heaps	Logarithmic efficiency patterns
Trees & Tries	Pointer-based data structures
Backtracking & DP	Advanced problem-solving techniques

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Topic Coverage

Data Structures

Topic	Documents	Key Patterns
Arrays	Overview / Three Sum / Intervals	Two pointers, sorting, prefix sum
Hash Tables	Overview / Two Sum	O(1) lookup, counting
Strings	Overview / Substrings	Sliding window, two pointers
Linked Lists	Overview / LRU Cache	Fast/slow pointers, reversal
Trees	Overview / BST Operations	DFS, BFS, recursion
Graphs	Overview / Islands	DFS, BFS, Union-Find
Heaps	Overview / Median Stream	Priority queues, top-k
Stacks & Queues	Overview / Monotonic Stack	LIFO/FIFO patterns

Algorithms & Techniques

Topic	Documents	Key Patterns
Complexity	Big O / Space	Analysis, optimization
Coding Patterns	Overview / Two Pointers / Sliding Window	Pattern recognition
Searching & Sorting	Overview / Binary Search	O(log n) techniques
Recursion	Overview / Permutations	Backtracking templates
Dynamic Programming	Overview / Knapsack	State definition, recurrence

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Study Plan Recommendations

If You Have 2 Weeks

1. Start with Fast Track Overview
2. Master Coding Patterns
3. Practice 2-3 problems per data structure
4. Focus on Arrays, Trees, and Graphs

If You Have 1 Month

1. Complete all pattern documents
2. Work through each data structure systematically
3. Spend extra time on Dynamic Programming
4. Review Complexity Analysis for optimization

If You Have 2+ Months

1. Cover every document thoroughly
2. Implement solutions without looking at code
3. Practice explaining your approach out loud
4. Time yourself on problems (aim for 20-30 min each)

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Content Features

Visualizations

Each document includes:

• Mermaid diagrams for data structure visualization
• Matplotlib Python scripts for algorithm animations
• ASCII art showing step-by-step state changes
• Links to interactive tools (VisuAlgo, Algorithm Visualizer)

Code Quality

All Python solutions feature:

• Clean, readable implementations
• Time and space complexity analysis
• Multiple approaches when applicable
• Edge case handling

Interview-Focused Content

• Common interview patterns at top companies
• Follow-up questions interviewers ask
• Expected complexity standards
• Best practices for explaining your approach

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Quick Reference: Pattern Selection

Problem Type                    Pattern to Consider
─────────────────────────────────────────────────────
Sorted array + target          → Binary Search
Two elements summing to X      → Two Pointers / Hash Map
Contiguous subarray            → Sliding Window
Running sum queries            → Prefix Sum
Find cycle in linked list      → Fast/Slow Pointers
Level-order traversal          → BFS with Queue
Path finding / connectivity    → DFS / BFS / Union-Find
All permutations/combinations  → Backtracking
Optimal substructure           → Dynamic Programming
Top K elements                 → Heap

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Statistics

Category	Count
Total Documents	59
Data Structure Topics	8
Algorithm Topics	5
Individual Problems	80+
Code Examples	150+

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Good luck with your interview!

This guide is part of the ML Interview Playbook collection.