Python DSA Course

Lesson 1 • Functions and Scope

Defines reusable functions, default arguments, and variable scope rules. Functions are the primary unit of algorithm implementation in this course.

Lesson 2 • Built-in Collections Overview

Surveys lists, tuples, dicts, and sets with their time complexities. Provides the practical container knowledge needed before custom structures are built.

Lesson 3 • Modules, Libraries, and I/O

Introduces importing modules, using the standard library, and reading input for competitive-style problems. Prepares students for structured problem-solving workflows.

Lesson 1 • Big-O, Big-Theta, and Big-Omega

Defines the three asymptotic notations and their mathematical meaning. Students apply each notation to classify algorithm behavior precisely.

Lesson 2 • Measuring Algorithm Performance

Introduces runtime measurement concepts and why theoretical analysis outperforms benchmarking alone. Sets the analytical mindset for every chapter that follows.

Lesson 3 • Analyzing Loops and Recursion

Teaches systematic analysis of nested loops and recursive calls. Directly applicable to every sorting and searching algorithm in later chapters.

Lesson 1 • Two-Pointer and Sliding Window

Introduces the two-pointer and sliding-window patterns for O(n) solutions on sorted or sequential data. Reduces brute-force O(n²) approaches significantly.

Lesson 2 • Hash Tables and Collision Handling

Explains hash function design, collision resolution, and Python dict internals. Students implement custom hash maps and solve lookup problems in O(1).

Lesson 3 • Array Manipulation Techniques

Covers in-place operations, prefix sums, and difference arrays for range queries. These patterns appear in the majority of interview-style problems.

Lesson 1 • Queue, Deque, and Priority Queue

Implements FIFO queues, double-ended deques, and heap-based priority queues. Prepares students for BFS and scheduling algorithm problems.

Lesson 2 • Singly and Doubly Linked Lists

Builds node-based linked list classes with insertion, deletion, and traversal. Establishes pointer manipulation skills used in trees and graphs later.

Lesson 3 • Stack Implementation and Applications

Implements stacks using lists and linked lists, then applies them to parsing and evaluation problems. Stacks underpin recursion simulation and backtracking.

Lesson 1 • Recursion Fundamentals

Defines base cases, recursive calls, and call-stack behavior with visual tracing. Correct recursion design is prerequisite for divide-and-conquer and backtracking.

Lesson 2 • Divide and Conquer Sorting

Implements merge sort and quicksort with full recurrence analysis. Students understand partition strategies and stability trade-offs.

Lesson 3 • Searching Algorithms

Implements binary search and its variants on sorted arrays and answer spaces. Binary search on the answer is a key pattern for optimization problems.

Lesson 1 • Tree Problem Patterns

Covers path sum, lowest common ancestor, and diameter problems using recursion. These patterns appear frequently in technical interviews and system design.

Lesson 2 • Tries and Segment Trees

Builds prefix tries for string search and segment trees for range queries. These specialized trees solve problems that BSTs and heaps cannot handle efficiently.

Lesson 3 • Balanced Trees and Heaps

Introduces AVL and red-black tree balancing concepts and heap structure. Students understand when self-balancing trees are necessary for performance guarantees.

Lesson 4 • Binary Tree Fundamentals

Defines tree terminology, node structure, and recursive traversal orders. Traversal patterns are the foundation for all tree-based algorithms.

Lesson 1 • Topological Sort and Cycle Detection

Applies Kahn's algorithm and DFS-based topological sort to DAGs. Cycle detection and ordering are essential for dependency resolution problems.

Lesson 2 • Minimum Spanning Trees and Union-Find

Implements Kruskal's and Prim's algorithms alongside the Union-Find structure. These tools solve network connectivity and clustering problems efficiently.

Lesson 3 • Shortest Path Algorithms

Implements Dijkstra, Bellman-Ford, and Floyd-Warshall for weighted graphs. Students select the correct algorithm based on graph properties and constraints.

Lesson 4 • BFS and DFS Traversals

Implements iterative BFS and recursive DFS with visited tracking. These two traversals underpin nearly every graph algorithm in this chapter.

Lesson 1 • Classic DP Problem Patterns

Solves knapsack, longest subsequence, and coin change problems as canonical DP templates. Mastering these templates accelerates solving novel DP problems.

Lesson 2 • DP Foundations and Memoization

Defines overlapping subproblems and optimal substructure as DP prerequisites. Top-down memoization converts recursive solutions into efficient DP solutions.

Lesson 3 • Tabulation and Space Optimization

Builds bottom-up DP tables and reduces space using rolling arrays. Space-optimized DP is critical for large-input constraints in competitive problems.

Lesson 4 • Interval and Grid DP

Applies DP to interval merging and grid path problems with 2D state spaces. These patterns extend DP skills to matrix and range-based problem types.