Algorithms and Data Structures in Python
Overview The Algorithms and Data Structures in Python course focuses on how to efficiently store, organize, and process data using...
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About This Course
Overview
The Algorithms and Data Structures in Python course focuses on how to efficiently store, organize, and process data using Python. It teaches both foundational data structures and essential algorithms used in problem-solving, software development, and technical interviews.
The goal is to build strong problem-solving skills and learn how to write optimized code using Python.
2. Understanding Algorithms and Data Structures
Data Structures
Data structures are ways to organize and store data efficiently.
Algorithms
Algorithms are step-by-step procedures used to solve problems or perform tasks.
Together, they help build fast and scalable programs.
3. Python as a Problem-Solving Language
Python is widely used for algorithms because:
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Simple syntax
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Powerful built-in data structures
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Fast prototyping
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Strong library support
4. Big-O Notation (Time & Space Complexity)
Big-O measures algorithm efficiency.
Common Complexities
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O(1) → constant time
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O(log n) → logarithmic
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O(n) → linear
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O(n²) → quadratic
Why It Matters
Helps compare algorithm performance.
5. Arrays and Lists
Python lists are dynamic arrays.
Operations
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Access elements
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Insert/delete elements
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Iterate through data
Use Cases
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Storing collections
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Basic data manipulation
6. Stacks
A stack follows LIFO (Last In First Out).
Operations
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push
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pop
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peek
Use Cases
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Undo operations
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Expression evaluation
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Backtracking
7. Queues
A queue follows FIFO (First In First Out).
Operations
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enqueue
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dequeue
Use Cases
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Task scheduling
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Processing requests
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BFS algorithms
8. Linked Lists
A linked list stores data in nodes.
Types
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Singly linked list
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Doubly linked list
Advantages
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Dynamic size
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Efficient insertions/deletions
9. Trees
Trees represent hierarchical data.
Types
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Binary trees
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Binary search trees (BST)
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Balanced trees
Use Cases
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File systems
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Searching and sorting
10. Graphs
Graphs represent networks of connected nodes.
Types
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Directed graphs
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Undirected graphs
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Weighted graphs
Algorithms Used
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BFS (Breadth-First Search)
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DFS (Depth-First Search)
11. Hash Tables (Dictionaries in Python)
Hash tables store key-value pairs.
Features
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Fast lookup (O(1))
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Efficient data mapping
Use Cases
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Caching
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Counting problems
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Fast search operations
12. Sorting Algorithms
Sorting organizes data in order.
Common Algorithms
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Bubble Sort
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Selection Sort
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Insertion Sort
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Merge Sort
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Quick Sort
Goal
Efficient data organization.
13. Searching Algorithms
Used to find elements in data structures.
Types
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Linear Search
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Binary Search
Efficiency
Binary search is much faster (O(log n)).
14. Recursion
A function that calls itself.
Use Cases
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Tree traversal
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Divide-and-conquer algorithms
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Backtracking problems
15. Dynamic Programming
A method for solving complex problems by breaking them into subproblems.
Key Idea
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Store results of subproblems
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Avoid repeated calculations
16. Greedy Algorithms
Greedy algorithms make the best choice at each step.
Use Cases
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Optimization problems
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Scheduling
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Path selection
17. Backtracking
Backtracking explores all possible solutions.
Use Cases
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Puzzle solving
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Combinatorics
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Constraint problems
18. Real-World Applications
Algorithms and data structures are used in:
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Software development
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Artificial intelligence
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Search engines
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Game development
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Financial systems
19. Who This Course Is For
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Python programmers
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Computer science students
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Interview preparation candidates
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Software developers
20. Requirements
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Basic Python knowledge
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Understanding of variables, loops, and functions
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Logical thinking skills
21. Conclusion
The Algorithms and Data Structures in Python course builds strong foundations in problem-solving, data structures, and algorithm design. It prepares learners to write efficient code and solve complex programming challenges used in real-world software and technical interviews.
Course Content
001 Introduction
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001 Introduction
00:00
002 Why To Use Data Structures
003 Data Structures And Abstract Data Types
005 Installing Python
006 Arrays Introduction – Basics
007 Arrays Introduction – Operations
008 Arrays In Python
009 Linked List Introduction – Basics
010 Linked List Introduction – Operations
011 Linked List Theory – Doubly Linked List
012 Linked List Introduction – Linked Lists Versus Arrays
013 Linked List Implementation I – Insert
014 Linked List Implementation Ii – Traverse
015 Linked List Implementation Iii – Remove
016 Linked List Implementation Iv – Testing
017 Doubly Linked List Introduction
018 Stack Introduction
019 Stacks In Memory Management ( Stacks Heaps )
020 Stacks And Recursive Method Calls
021 Stack Implementation
022 Queue Introduction
023 Queue Implementation
024 Binary Search Trees Theory – Basics
025 Binary Search Trees Theory – Search Insert
026 Binary Search Trees Theory – Delete
027 Binary Search Trees Theory – In-Order Traversal
028 Binary Search Trees Theory – Running Times
029 Binary Search Tree Implementation I – Node Class
030 Binary Search Tree Implementation Ii – Insert
031 Binary Search Tree Implementation Iii – Traverse Min Max
032 Binary Search Tree Implementation Iv – Testing Insertion
033 Binary Search Tree Implementation V – Deletion
034 Binary Search Tree Implementation Vi – Testing Deletion
035 Avl Trees Introduction – Motivation
036 Avl Trees Introduction – Basics
037 Avl Trees Introduction – Height
038 Avl Trees Introduction – Rotations Cases
039 Avl Trees Introduction – Illustration
040 Avl Trees Introduction – Applications
041 Avl Tree Implementation I – Node
042 Avl Tree Implementation Ii – Height Balance
043 Avl Tree Implementation Iii – Rotations
044 Avl Tree Implementation Iv – Insertion
045 Avl Tree Implementation V – Violations
046 Avl Tree Implementation Vi – Testing Insertion
047 Avl Tree Implementation Vii – Remove
048 Red-Black Trees Introduction – Basics
049 The Logic Behind Red-Black Trees
050 Red-Black Trees Rotations- Cases I
051 Red-Black Trees Rotations- Cases Ii
052 Red-Black Trees Rotations- Cases Iii
053 Red-Black Trees Rotations- Cases Iv
054 Red-Black Trees Introduction – Example I
055 Red-Black Trees Introduction – Example Ii
056 Red-Black Tree Versus Avl Tree
057 Priority Queues Introduction
058 Heap Introduction – Basics
059 Heap Introduction – Array Representation
060 Heap Introduction – Remove Operation
061 Heap Introduction – Heapsort
062 Heap Introduction – Operations Complexities
063 Other Types Of Heaps Binomial And Fibonacci Heap
064 Heap Implementation I – Insert
065 Heap Implementation Ii – Heapsort
066 Heap Implementation Iii – Fixing Heap Properties
067 Heaps In Python
068 Associative Array Adt
069 Hashtable Introduction – Basics
070 Hashtable Introduction – Collisions
071 Hashtable Introduction – Dynamic Resizing
072 Linear Probing Implementation I – Hashfunction
073 Linear Probing Implementation Ii – Insert
074 Linear Probing Implementation Iii – Retrieve
075 Dictionaires In Python
076 Tries Introduction
077 Ternary Search Trees Introduction – Insert
078 Ternary Search Trees Introduction – Get
079 Ternary Search Trees Introduction – Applications
080 Ternary Search Trees Implementation I
081 Ternary Search Trees Implementation Ii
082 Graph Theory
083 Breadth-First Search Introduction
084 Breadth-First Search Implementation
085 Depth-First Search Introduction
086 Depth-First Search Implementation
087 Memory Management Bfs Vs Dfs
088 Dijkstra Algorithm Introduction – Basics
089 Dijkstra Algorithm Introduction – Algorithm
090 Dijkstra Algorithm Introduction – Example
091 Dijkstra Algorithm Implementation I – Edge Node
092 Dijkstra Algorithm Implementation Ii – Algorithm
093 Dijkstra Algorithm Implementation Iii – Testing
094 Bellman-Ford Algorithm Introduction
095 Bellman-Ford Algorithm Implementation I – Node Edge
096 Bellman-Ford Algorithm Implementation Ii – The Algorithm
097 Bellman-Ford Algorithm Implementation Iii – Testing
098 Shortest Path Algorithms Applications
099 Union Find Data Structure Introduction
100 Spanning Trees Introduction – Kruskal Algorithm
101 Kruskal Algorithm Implementation I – Basic Classes
102 Kruskal Algorithm Implementation Ii – Disjoint Set
103 Kruskal Algorithm Implementation Iii – Algorithm
104 Kruskal Algorithm Implementation Vi – Testing
105 Spanning Trees Introduction – Prim’s Algorithm
106 Prims-Jarnik Algorithm Implementation I – Basic Classes
107 Prims-Jarnik Algorithm Implementation Ii – Algorithm
108 Prims-Jarnik Algorithm Implementation Iii – Testing
109 Applications Of Spanning Trees
110 Sorting Introduction
111 Adaptive Sorting Algorithms
112 Bubble Sort Introduction
113 Bubble Sort Implementation
114 Selection Sort Introduction
115 Selection Sort Implementation
116 Insertion Sort Introduction
117 Insertion Sort Implementation
118 Quicksort Introduction I
119 Quicksort Introduction Ii
120 Quicksort Implementation
121 Merge Sort Introduction – Divide
123 Merge Sort Implementation
124 Hybrid Algorithms Introduction
125 Non-Comparison Based Algorithms
126 Counting Sort Introduction
127 Radix Sort Introduction
Frequently Asked Questions
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