Python with DSA Course

Lesson 1 • Functions and Scope

Defines reusable functions with parameters, return values, and scope rules. Prepares students to encapsulate DSA logic in clean, testable units.

Lesson 2 • Control Flow and Iteration

Teaches if/elif/else branching, for and while loops, and loop control statements. Enables students to express algorithmic logic in Python.

Lesson 3 • Error Handling and Debugging

Introduces exceptions, try/except blocks, and debugging tools. Equips students to write robust code and diagnose failures in complex programs.

Lesson 4 • Core Data Types and Variables

Covers integers, floats, strings, booleans, and type casting. Provides the data primitives used in every algorithm implementation ahead.

Lesson 1 • Iterators, Generators, and Lazy Evaluation

Explains the iterator protocol, generator functions, and yield. Enables memory-efficient traversal of large data sets in DSA problems.

Lesson 2 • Lists and Tuples in Depth

Explores indexing, slicing, mutation, and tuple immutability. These sequence types underpin array-based data structures covered later.

Lesson 3 • Dictionaries and Sets

Covers hash-based storage, key-value operations, and set algebra. Directly supports hash table and graph adjacency implementations.

Lesson 4 • List and Dictionary Comprehensions

Teaches concise comprehension syntax for filtering and transforming collections. Reduces boilerplate in algorithm implementations throughout the course.

Lesson 1 • Introduction to Algorithm Analysis

Defines algorithm correctness, efficiency, and the need for formal analysis. Sets the analytical mindset applied to every data structure and algorithm ahead.

Lesson 2 • Big-O, Big-Omega, and Big-Theta

Formalises asymptotic notation and common complexity classes. Provides the vocabulary used to describe every algorithm in subsequent chapters.

Lesson 3 • Analysing Loops and Recursive Calls

Teaches step-counting for loops and recurrence relations for recursion. Directly prepares students to analyse sorting and tree algorithms.

Lesson 4 • Profiling Python Code

Uses timeit, cProfile, and memory_profiler to measure real performance. Connects theoretical complexity to measurable Python execution behaviour.

Lesson 1 • Complexity Comparison of Linear Structures

Compares time and space complexity across all linear structures studied. Enables informed structure selection for algorithm design problems.

Lesson 2 • Singly and Doubly Linked Lists

Builds Node classes and list operations including insertion, deletion, and traversal. Introduces pointer-based thinking essential for trees and graphs.

Lesson 3 • Stacks: Implementation and Applications

Implements stacks using lists and linked lists, then applies them to real problems. Demonstrates LIFO semantics used in recursion, parsing, and backtracking.

Lesson 4 • Queues, Deques, and Priority Queues

Covers FIFO queues, double-ended deques, and heap-backed priority queues. Prepares students for BFS, scheduling, and greedy algorithm problems.

Lesson 1 • Quadratic Sorting Algorithms

Implements bubble, selection, and insertion sort with step-by-step analysis. Provides the O(n²) baseline against which faster sorts are measured.

Lesson 2 • Searching Algorithms

Implements linear search and binary search with complexity proofs. Connects searching to sorted data requirements and real interview problems.

Lesson 3 • Recursion Fundamentals

Defines base cases, recursive cases, and the call stack. Builds the mental model required for tree traversal and divide-and-conquer algorithms.

Lesson 4 • Linear-Time and Hybrid Sorting

Covers counting sort, radix sort, and Timsort used in Python's built-in sort. Expands students' toolkit beyond comparison-based sorting.

Lesson 1 • Binary Search Trees

Implements BST insertion, search, and deletion with O(log n) average analysis. Demonstrates ordered data storage and retrieval using tree structure.

Lesson 2 • Tries and Prefix Trees

Constructs a trie for string insertion, search, and prefix matching. Enables efficient autocomplete and dictionary lookup solutions.

Lesson 3 • Binary Trees and Traversals

Defines tree terminology and implements in-order, pre-order, and post-order traversals. Establishes the recursive traversal pattern used in all tree algorithms.

Lesson 4 • Heaps and Heap Operations

Builds a max-heap and min-heap, implements heapify and heap sort. Directly supports priority queue and greedy algorithm implementations.

Lesson 1 • Advanced Graph Problems

Covers strongly connected components, bipartite checking, and network flow basics. Extends graph skills to complex real-world problem categories.

Lesson 2 • Graph Representations and Terminology

Defines directed, undirected, weighted, and cyclic graphs with adjacency list and matrix representations. Establishes the foundation for all graph algorithm implementations.

Lesson 3 • Shortest Path Algorithms

Implements Dijkstra's and Bellman-Ford algorithms for weighted graphs. Equips students to solve routing and cost-minimisation problems.

Lesson 4 • Minimum Spanning Trees

Implements Kruskal's and Prim's algorithms using union-find and priority queues. Solves network design problems requiring minimum-cost connectivity.

Lesson 1 • Greedy Algorithms and Backtracking

Implements greedy strategies for interval scheduling and Huffman coding, plus backtracking for N-Queens and subsets. Completes the core algorithm design toolkit.

Lesson 2 • Tabulation and Bottom-Up DP

Builds DP tables iteratively for coin change, climbing stairs, and grid paths. Eliminates recursion overhead and clarifies state transition logic.

Lesson 3 • Introduction to Dynamic Programming

Defines overlapping subproblems and optimal substructure as DP prerequisites. Contrasts DP with divide-and-conquer to clarify when each applies.

Lesson 4 • Memoisation and Top-Down DP

Converts naive recursion to memoised solutions using dictionaries and functools.lru_cache. Demonstrates dramatic complexity reduction on Fibonacci and similar problems.