We all must have learned in school that a program needs instructions to perform any dedicated task such as sequencing a number, sorting tables, etc. However, these tasks are required to store, retrieve, and perform to form a symmetric structure in the virtual system. In other words, a data structure is used to perform all these actions (store-retrieve-organize) data in a structured way with the help of algorithms in an efficient manner.
Now, to learn DSA, you need to learn at least one programming language and python is the best choice nowadays. As per reports, Python was voted as the #1 Programming Language among Java, C/C++, etc. Not only this, currently there are more than 32,000 jobs available for Python Developers (in India) & 134,000+ (worldwide), and that’s why people are inclined towards Python Programming Language.
Why DSA with Python?
As we know that Python is a beginner’s language and is used in machine learning, game development, artificial intelligence, web development, etc. Python is a simple, high-level, general-purpose programming language that offers easy syntax and was introduced with the concept of providing ease of learning (just like English).
Built-in Data Structures in Python
Built-in Data Structures are the data types that cover almost every part of real-world DS and are directly used by developers (for both storing and declaring) different variables within a program. There are 2 major categories ie Primary (Primitive) and Non-Primitive (Lists, Dictionaries, Tuples, and Sets) Data Types.
If you’re looking to master DSA using Python Programming Language and prepare for big tech giants, we recommend you enroll yourself in Data Structure & Algorithms in Python – Self-Paced Coursedesigned to offer video tutorials, and a lot of practice sets and also with course completion certificate & 24×7 doubt support assistance.
User-Defined Data Structures in Python
Since data management (including organizing and sorting) in a logical manner is being handled into Data Structure, the stored data supports two kinds of data ie Primitive & Non-Primitive Data Types. However, it is also abbreviated as UDT which helps in extending built-in data types so that customized data types can be created.
Besides this, there are six major methods of implementing the same, those are:
- Linked List
This DSA with Python course has been curated to take you forward from the basics of data structure to the advanced level with concepts like Lists, Strings, Graphs, Hash, etc., and is suitable for both beginners and advanced-level programmers. This self-paced course will also drive through effective methods of appearing for top-notch Product based companies (such as Amazon, Netflix, Microsoft, etc.)
- Exclusive video content & lectures by Mr. Sandeep Jain(10+ Yrs. Exp.), Founder, and CEO of GeeksforGeeks
- Get doubt-support assistance.
- Get a Course Completion Certificate trusted by top-tier companies
- Purchase once – Access Forever (Get Lifetime access to the course)
- Engaging content with Practice videos, Live Classes, Videos for Project work, etc. that suits well for the beginner’s level.
- This course also offers Community Engagement Live Sessions to discuss all the major topics.
The content that will be provided in this Data Structures & Algorithms in Python – Self-Paced Course is as follows:
Data Structure (Basics)
1. Analysis of Algorithms – Analysis of Algorithms (Background), Asymptotic Analysis, Big O Notation, Omega Notation, Theta Notation, Analysis of Common Loops, and much more.
2. Mathematics – Python DSA – Count Digits, Palindrome Number, Factorial of a Number, Trailing Zeros in Factorial, GCD, and HCF of two numbers, LCM of two numbers, and much more.
3. List – List (Dynamic Sized Array) Introduction, Working on List in Python, Average or Mean of a List, Separate Even and Odd, Get Similar Elements, Slicing (List, Tuple, and String), and much more.
4. Recursion – Application of Recursion, Writing Base Cases in Recursion, Tail Recursion, and much more.
5. Searching – Binary Search in Python, Recursive Binary Search in Python, Analysis of Binary Search, Index of First Occurrence in a sorted array, Index of Last Occurrence, and much more.
6. Sorting – Sorting in Python, List Sort in Python, Sorted in Python, Stability in Sorting Algorithm, Bubble Sort, Selection Sort, and much more.
7. Hashing – Introduction to Hashing, Hashing Application, Direct Address Table, Hashing Functions, Collision Handling, Chaining, Implementation of Chaining in Python, and much more.
8. String – Strings in Python, Escape Sequences and Raw Strings, Formatted String in Python, String Comparison in Python, and much more.
9. Linked List – Video – Problems with Array Data Structure, Linked List Introduction in Python, Simple Linked List Implementation in Python, Applications of Linked List, and much more.
10. Circular Linked List – Circular Linked List in Python, Circular Linked List (Advantages & Disadvantages), Circular Linked List Traversal, Insert at the Beginning of Circular Linked List, and much more.
11. Doubly Linked List – Doubly Linked List in Python, Singly Vs Doubly Linked List (Advantages & Disadvantages), Insert at the Beginning of DLL in Python, and much more.
12. Stack – Stack Data Structure, Stack in Python, Linked List Implementation of Stack in Python, Stack Applications, and much more.
13. Queue – Queue in Python, Queue in Data Structure, Application of Queue Data Structure, Implementation of Queue using Array, and much more.
14. Dequeue – Introduction, Deque Applications, Deque in Python, List Implementation of Deque in Python, and much more.
15. Tree – Tree Data Structure, Application of Tree, Binary Tree in Python, Tree Traversal, Inorder Traversal in Python, Preorder Traversal in Python, Postorder Traversal in Python, and much more.
16. Binary Search Tree – Binary Search Tree (Background), Binary Search Tree (Introduction), Search in BST in Python, BST Insert in Python, BST Delete in Python, Floor in BST (Problem and Solution Idea), and much more.
17. Heap – Binary Heap Introduction, Heap Python Implementation (Introduction), Binary Heap Insert, Binary Heap (Extract min and Heapify), Decrease Key and Delete Operations, and much more.
Data Structure (Advanced)
18. Bit Magic – Bitwise Operator in Python (Part 1,2), Check whether the Kth bit is set or not, Count Set Bits, Power of Two, and much more.
19. List – Left Rotate by d Places, Maximum difference, Stock Buy & Sell Part 2, Trapping Rainwater, Maximum Consecutive 1s, Longest even odd subarray, and much more.
20. Recursion – Subset of a given string, Printing all Permutations, Tower of Hanoi in Python, Josephus Problem in Python, Subset sum problem, and much more.
21. Searching – Search in Sorted Rotated Array, Median of two sorted arrays, Repeating Elements (Part 1,2), Allocate Minimum Pages (Naive Method), and much more.
22. Sorting – Tail Call Elimination in Quick Sort, Kth Smallest, Minimum Difference in an Array, Chocolate Distribution Problem, Sort an array with two types of elements, and much more.
23. Hashing – Intersection of two arrays, Union of two unsorted arrays, Pair with given sum in an unsorted array, Subarray with 0 sums in Python, Check for Palindrome Permutation, and much more.
24. String – Overview of Pattern Searching, Pattern Searching in Python, Naive Pattern Searching, Improved Naive Pattern Searching for Distinct, Rabin Karp Algorithm, and much more.
25. Linked List – Reverse a linked list in groups of size k, Detect loop using Floyd’s Cycle Detection Algorithm, Detect and remove loop in Linked List, and much more.
26. Stack – Two stacks in an array, K stacks in an array, Previous Greater Elements, Next Greater Elements, Stock span problem, and much more.
27. Queue – Queue and Deque, Queue implementation using circular list, Implementing stack using a queue, Reversing a queue, Generate numbers with given digits, Design a data structure with min/max operations, and much more.
28. Tree – Level Order Traversal by Line (Part 1,2), Check for Balanced Binary Tree, Maximum Width of Binary Tree, Convert Binary Tree to Doubly Linked List, Construct Binary Tree from Inorder and Preorder, and much more.
29. Binary Search Tree – Ceiling on the left side in an array, Find Kth Smallest in BST, Check for BST, Fix BST with Two Nodes Swapped, Pair Sum with given BST, and much more.
30. Heap – Sort K Sorted Array, Purchase Maximum Items, K Largest Elements, K Closest Elements, Merge K Sorted Arrays, and much more.
31. Graph – Introduction, Graph Representation (Adjacency Matrix), Graph Representation (Adjacency List), Graph Adjacency List Representation in Python, Adjacency Matrix and List Comparison, and much more.
32. Greedy – Introduction to Greedy Algorithm, Activity Selection Problem, Activity Selection, Fractional Knapsack, and much more.
33. Backtracking – Concept of backtracking, Rat in a Maze, N Queen Problem, and much more.
34. Dynamic Programming – Introduction to DP, Memoization, Tabulation, Longest Common Subsequence (Part 1,2), Variation of LCS, and much more.
35. Try – Introduction, Representation, Search Insert, Delete, and much more.
36. Segment and Binary Indexed Trees – Introduction, Segment Tree, Range Query, Update Query, Binary Indexed Tree (Introduction), and much more.
37. Disjoint Set – Introduction, Find and Union Operator, Union by Rank, Kruskal’s Algorithm, and much more.
Frequently Asked Questions (FAQs)
1. Is Python good for Data Structures?
Being a High-Level and #1 Programming Language, using Python for Data Structure is likely to be among the good choice due to its easy syntax and implementation. Python has an easy learning curve and provides ease for developers, and data scientists to work on different modules such as backend development, machine learning, artificial intelligence, etc.
2. Should I learn DSA in Python or C++?
The more you’ll code, the more you’ll make mistakes and the more you’ll learn. Well, Python is undoubtedly one of the best choices to start with the DSA, but unless you’ll make work on raw structures where inbuild features are not there, you’ll eventually groom up in your logic part.
3. What is the best way to learn DSA in Python?
Start learning the basics of python and then learn about the complexities of time and space. After that, you can jump on to the basics of DSA and start practicing the problems on particular topics. To learn more about this you can refer to the Complete Roadmap to DSA.
4. How long does it take to learn DSA in Python?
It would require consistent practice and dedication and solving as many problems as you can. It should take anywhere between 3 to 6 months to learn DSA right from scratch with the help of quality resources and proper guidance. Also, it totally depends on how much time you are giving per day and basics are cleared or not.
Being on a beginner’s level will definitely give a kick start while working with the Data Structure and Algorithm. The concepts, structure, and methods are so intuitive that you’ll soon be ready for advanced-level programming with Python. The best part is its Live Lecture, practice questions, Certificate, and Interview Preparation that will create a smooth journey in Data Structure with Python. All you need is to be consistent and practice as much as you can.