Data Structures for Coding Interviews in Python Course Syllabus
Full curriculum breakdown — modules, lessons, estimated time, and outcomes.
An extensive, hands-on Python course that builds data structures from scratch and equips you with algorithmic rigor and challenge-readiness for top tech interview pipelines. This course spans approximately 30 hours of content, structured into eight focused modules that progress from foundational complexity analysis to mixed coding challenges. Each module combines conceptual learning with practical implementation, ensuring deep understanding and interview preparedness through pattern-based problem solving.
Module 1: Complexity Analysis
Estimated time: 2 hours
- Asymptotic notation
- Loop analysis
- Nested behavior
- Complexity trade-offs
Module 2: Lists & Arrays
Estimated time: 4 hours
- Python list operations
- Slicing
- In-place algorithms
- Subarray manipulation
Module 3: Linked Lists
Estimated time: 4 hours
- Singly and doubly linked list implementation
- Reversal
- Cycle detection
- Merging
Module 4: Stacks, Queues & Deques
Estimated time: 3 hours
- Stack/queue behavior
- Deque implementation
- LRU cache design patterns
Module 5: Trees & Graphs
Estimated time: 6 hours
- Binary tree/BST traversal
- Graph adjacency lists
- BFS/DFS
- Connectivity
Module 6: Heaps & Priority Queues
Estimated time: 2 hours
- Heap operations
- Top‑K elements
- Heap‑based problem-solving
Module 7: Hash Maps & Sets
Estimated time: 3 hours
- Dictionary internals
- Hash collisions
- Set use-cases
- Frequency counting
Module 8: Comprehensive Review & Mixed Challenges
Estimated time: 5 hours
- Recap all data structures
- Complexity comparisons
- Review patterns
Prerequisites
- Familiarity with basic Python syntax
- Understanding of fundamental programming concepts (variables, loops, conditionals)
- Basic exposure to functions and object-oriented principles in Python
What You'll Be Able to Do After
- Implement core data structures in Python from scratch
- Analyze time and space complexity using Big-O notation
- Solve common coding interview problems involving arrays, linked lists, trees, and graphs
- Apply pattern-based strategies to tackle unseen algorithm challenges
- Design efficient solutions using heaps, hash maps, and priority queues