Teach on Udemy

Turn what you know into an opportunity and reach millions around the world.

Learn More

Your cart is empty.

Keep shopping

Data Structures and Algorithms in Python: DSA Course

Name: Data Structures and Algorithms in Python: DSA Course
Rating: 4.2 (1476 reviews)

Master Python DSA for LEETCODE & Technical Interviews | 50-Day Structured Learning Path with 117 Coding Exercises

Role Play

Created byJackson Kailath

Last updated 5/2026

English

What you'll learn

Master data structures in Python - Arrays, LinkedLists, Stacks, Queues, Trees, Graphs, Hash Tables
Solve DSA problems for LEETCODE and technical interviews at Google, Amazon, Microsoft
Dynamic Programming, Backtracking, and advanced algorithm techniques
Time and Space Complexity analysis (Big O notation) for optimization
Real coding interview question patterns from FAANG companies

Coding Exercises

This course includes our updated coding exercises so you can practice your skills as you learn.

Course content

63 sections • 656 lectures • 56h 57m total length

What you're going to get from this course7:48
Welcome! How to make best use of this course (Please Watch)4:02
Day 1 Goals0:35
Introduction to Data Structures4:34
Introduction to Big O, Time Complexity14:00
Asymptotic Analysis and Big O16:36
Master asymptotic analysis and big o notation to see how time complexity grows with input size, neglect constants, and compare algorithms using O(1), O(log n), O(n), O(n log n), O(n^2).
Big O Space Complexity3:49
Big O Logarithm5:12
Arrays: Data Structures Crash Course13:20
Quiz: Arrays
CODING EXERCISES2:11
How to log output to debug code in Udemy Editor0:17
CODING INTERVIEW Q1 (Easy): Sorted Squared Array4:45
Coding Exercise: Sorted Squared Array
Method 1, Big O Analysis3:05
Master the brute force approach to square each element of a sorted array, then sort the results, achieving O(n log n) time and O(n) space.
Python Code - Method 11:42
Quiz - Method 1 (Sorted squarred array)
Method 27:29
Use the sorted input and a two-pointer approach to square extremes and fill a new array from the end, achieving O(n) time and O(n) space.
Python Code - Method 24:11
Quiz - Method 2 (Sorted squarred array)
CODING INTERVIEW Q2 (Easy): Monotonic Array5:14
Coding Exercise: Monotonic Array
Method and Big O analysis7:17
Determine if an array is monotonic by evaluating non increasing and non decreasing patterns, analyzing three cases, and reporting time complexity O(n) and space complexity O(1).
Python Code - Monotonic Array5:20
Develop a monotonic array checker by comparing first and last elements, then scanning adjacent pairs to confirm increasing or decreasing order, while treating an empty array as monotonic.
Quiz - Monotonic Array
Role Play( Beta) : Sorted Squarred Array
Celebrating Milestones0:43

For Quick Reference : Latest Additions to the Course0:54
Best Study Technique to prepare for Coding Interviews0:55
Day 2 Goals1:11
Recursion Basics9:21
Recursive Leap of Faith12:49
Visualising Recursion8:53
Recursion vs Iteration4:20
Ways to write Base condition3:31
Recurrence relation1:59
How to Solve Recursion Questions5:17
Recursion Approaches - 0 to N and N to 06:45
Recursion is everywhere2:24
Complexity Analysis of Recursive Solutions2:19
Analyze the time and space complexity of recursive solutions by counting nodes in the recursion tree and multiplying by per-node work, and assess stack usage via maximum depth.
Quiz: Recursion
CODING INTERVIEW QUESTION (Medium): k-th symbol in Grammar7:00
Coding Exercise (k-th symbol in Grammar)
Approach(k-th symbol in Grammar)12:45
Pseudocode (k-th symbol in Grammar)3:10
Complexity Analysis(k-th symbol in Grammar)4:22
Python Solution (k-th symbol in Grammar)4:00
Quiz - k-th symbol in Grammar
CODING INTERVIEW QUESTION (Medium): Josephus problem6:46
Coding Exercise: Josephus problem
Approach 17:52
Pseudocode3:24
Complexity Analysis1:42
Python Solution 1: Josephus problem Method 19:23
Quiz - Josephus problem Method 1
Approach 210:04
Pseudocode6:20
Complexity Analysis1:38
Analyze the time and space complexity of the Josephus problem's second approach, showing each call does constant work and the recursion stack yields O(n) space.
Python Solution 2 : Josephus problem Method 24:38
Quiz - Josephus problem Method 2
Approach 35:10
Complexity Analysis1:12
Python Solution 3 : Josephus problem Method 35:41
Code the iterative approach for the Josephus problem in Python, converting the recursive solution to a loop with k, handling cases, and computing the survivor via modulo and plus one.
Quiz - Josephus problem Method 3
Celebrating Milestones0:33
Celebrate milestones as you practice recursion and problem solving, reinforcing skills that edge you closer to acing tough interview questions.

Day 3 Goals1:01
CODING INTERVIEW QUESTION (Medium): Tower of Hanoi4:11
Coding Exercise(Tower of Hanoi)
Identifying that wew can use Recursion2:59
Approach6:09
Learn how to solve the Tower of Hanoi with recursion by analyzing one, two, and three-disk cases, then generalizing to n disks through a smaller-disk subproblem, largest-disk move, and recursion.
Recursion Tree8:40
Python Solution : Tower of Hanoi6:59
Complexity Analysis : Tower of Hanoi8:21
Analyze the Tower of Hanoi algorithm's time and space complexity, showing that space is O(n) due to the recursion stack and time is O(2^n) from the recurrence t(n)=2 t(n-1)+1.
CODING INTERVIEW QUESTION(Medium): Power Sum6:34
Coding Exercise: Power Sum
Method and Big O Analysis11:01
Python Solution: Power Sum5:48
Explore a Python implementation of the power sum function, traversing arrays and nested lists recursively to accumulate sums and handle powers; learn about time complexity O(n) and space complexity O(d).
Celebrating Milestones0:31

Day 4 Goals0:16
What is Backtracking12:21
How is it different from Recursion ?1:04
How does Backtracking work ?3:37
Pass by reference / change inplace4:19
Blueprint to solve questions using Backtracking7:16
Identify when to use Backtracking2:04
Quiz: Backtracking
CODING INTERVIEW QUESTION (Medium): Permutations2:56
Coding Exercise ( Permutations)
Approach8:33
Pseudocode6:47
Python Solution : Permutations6:23
Complexity Analysis3:37
Evaluate the time and space complexity of generating all permutations. Show that recursion yields space complexity O(n), while producing n! permutations drives time complexity to O(n * n!).
CODING INTERVIEW QUESTION(Medium): Permutations 20:46
Permutations 2
Approach5:54
Pseudocode2:14
Python Code: Permutations 26:57
Write a Python backtracking solution for permutations with duplicates by pruning repeated branches using a hash map, swapping elements, and storing unique results in a list via a helper function.
Complexity Analysis : Permutations 20:50
Explore complexity analysis for permutations with duplicates, showing why time is n multiplied by n factorial and space equals n due to the recursion stack.
Celebrating Milestones0:31

Day 5 Goals0:21
CODING INTERVIEW QUESTION(Medium): Subsets2:39
Subsets
Method16:16
Learn how to generate all subsets (power set) of an array, using recursive backtracking and an iterative approach, with two to the power n possibilities for each element.
Subsets - Comparison with Backtracking Blueprint2:40
Subsets - Complexity Analysis3:39
Python Code - Subsets8:40
CODING INTERVIEW QUESTION(Medium): Subsets 20:43
Learn how to generate all unique subsets from an array that may contain duplicates, ensuring no duplicates in the power set, with the sample input 1, 2, 2.
Coding Exercise: Subsets 2
Approach8:47
Python Code: Subsets 27:14
Subsets 2: Complexity Analysis1:45
Analyze the time and space complexity of the subset generation approach, showing worst-case time O(n 2^n) and space O(n) due to recursion, with sorting contributing O(n log n) but ignored.
Celebrating Milestones0:32

Day 6 Goals0:29
CODING INTERVIEW QUESTION(Medium): Combinations1:52
Coding Exercise: Combinations
Approach4:51
Apply a backtracking recursive approach to generate all k-combinations from 1 to n using two pointers i and j, building sequences and backtracking to explore all options.
Combinations : Complexity Analysis5:34
Python Code : Combinations4:47
Code a Python backtracking solution to generate all combinations of size k from 1 to n, using a helper function, a current list, and a results list.
Combinations: Optimisation3:55
Python Code: Combinations with Optimisation0:42
CODING INTERVIEW QUESTION ( Medium) : Combinations Sum 11:31
Combinations Sum 1
Approach7:21
Explore a backtracking approach to find all combinations of candidate numbers that sum to a target, using recursion, pruning, unlimited repetitions, and starting from the current index to avoid duplicates.
Complexity Analysis part 12:45
Analyze the space and time complexity of the combination approach by examining the recursion tree, depth t/m, pruning when sums reach or exceed T, given n candidates and M.
Sidenote: Max number of nodes5:23
derive upper bound on number of nodes in a recursive k-ary tree with a geometric progression, showing nodes grow as k^(height+1) and informing space time complexity for combination sum one.
Complexity Analysis part 23:10
Python Code: Combinations Sum 14:23
Implement a recursive backtracking solution for combination sum one using a helper with start index, current combination, and sum included; explore candidates and backtrack to build all target-summing combinations.
Celebrating Milestones0:31

Day 7 Goals0:32
CODING INTERVIEW QUESTION (Medium): Combinations Sum 22:18
Coding Exercise: Combinations Sum 2
Approach8:02
Master the differences between combination sum one and two, use each candidate at most once, prune duplicate combinations with a hash map, and sort the input to ensure unique results.
Complexity Analysis1:50
Python Code: Combination Sum 26:54
Implement combination sum II with backtracking: sort candidates, use a recursive helper with index and current sum, prune when sum exceeds target, skip duplicates, and collect all valid combinations.
CODING INTERVIEW QUESTION ( Medium) : Combinations Sum 30:16
Coding Exercise: Combinations Sum 3
Combination Sum 3 - Short explanation0:15
Celebrating Milestones0:29

Day 8 Goals0:41
CODING INTERVIEW QUESTION(Hard) : Sudoku Solver4:27
Sudoku Solver
Approach9:08
Pseudocode5:52
Master sudoku backtracking pseudocode: identify the next empty cell, try 1–9 with validity checks, prune on failure, backtrack, and stop when the board is solved.
isValid check for Sudoku Solver6:50
Python Code : Sudoku Solver13:02
Complexity Analysis2:30
Another approach - Sudoku Solver ( Python Code)0:49
CODING INTERVIEW QUESTION(Hard): N Queen3:41
Solve the hard N-queens puzzle with backtracking, placing n queens on an n by n board so none attack, and return all distinct solutions, illustrated with n=4.
Coding Exercise: N Queen
Approach6:55
Solve the n queens problem on an n by n board with backtracking, placing one queen per row, checking column and diagonal conflicts, pruning branches, and generating all valid solutions.
Pseudocode3:50
Python Code: N Queen12:53
Learn to solve the n-queens problem in Python using recursive backtracking, placing queens on an n-by-n board with is_valid checks and board-to-string conversion.
Complexity Analysis3:52

Day 9 Goals0:35
Introduction to Dynamic Programming (DP)12:56
Dynamic Programming - Patterns2:39
Master dynamic programming by recognizing patterns and variations across interview questions, focusing on patterns like Fibonacci, knapsack, longest common subsequence, and Kadane's algorithm to tackle new problems.
Approach to solve DP(Dynamic Programming) Questions7:51
Develop dynamic programming expertise by first solving recursively, then memoizing (top-down), and finally constructing a bottom-up tabulation with space optimization.
Why writing the Recursive solution helps to write the Bottom up approach3:35
Identifying Dynamic Programming Questions1:37
Quiz: Dynamic Programming
CODING INTERVIEW QUESTION(Easy): Fibonacci4:59
Master the Fibonacci sequence from zero and one, and implement a function to compute f(n) using f(n-1)+f(n-2); explore multiple solutions and their time and space complexity.
Coding Exercise: Fibonacci
Approaches0:58
Approach 1: Recursion2:35
Complexity Analysis: Approach 1 - Recursion9:16
Python Code - Recursion4:52
Demonstrates a recursive Python solution for computing Fibonacci numbers, detailing base cases, f(n-1) and f(n-2) calls, and analyzes time complexity O(2^n) and space complexity O(n).
Approach 2: Memoization6:15
Learn the memoization (top-down) approach to Fibonacci by turning recursion into stored results using a hash table, enabling constant-time lookups and faster computation.
Complexity Analysis : Approach 2 - Memoization1:47
Python Code: Approach 2 - Memoization4:13
Learn how memoization speeds up Fibonacci calculations by storing computed values in a hash table and avoiding redundant recursion, with time and space complexity O(n).
Approach 3: Tabulation3:29
Apply the tabulation (bottom-up) approach to Fibonacci by building a one dimensional table with base cases f(0)=0 and f(1)=1, filling up to f(5)=5 and relating it to recursion.
Complexity Analysis: Approach 3 - Tabulation0:40
Python Code: Approach 3 - Tabulation2:05
Approach 4: Space Optimised Tabulation + Complexity Analysis5:58
Python Code: Approach 4 -Space Optimised Tabulation + Complexity Analysis3:23
CODING INTERVIEW QUESTION(Easy): Climbing Stairs4:13
Solve the climbing stairs problem by modeling it as a dynamic programming task, recognizing overlapping subproblems and a Fibonacci pattern to count distinct ways, an easy coding interview question.
Coding Exercise: Climbing Stairs
Approach3:56

Requirements

Basic knowledge of Python ( things like write a loop, function etc)
No experience with Data Structures or Algorithms required

Description

Looking for the best data structures and algorithms Python course? This structured DSA course is designed for anyone preparing for LEETCODE challenges and technical coding interviews. With 117 hands-on coding exercises spread across 50 structured days, you'll master every essential data structure in Python and algorithm needed to ace your next interview.

Student Testimonials:

"Amazing Course" - Erick Odhiambo Otieno
"I never seen the best course in this learning platform. It is the best course if you want to understand DSA to the core. you should try it guys. thanks a lot sir for this best course." - Nibru Kefyalew
"Great course!" - Shay Keren
"Very thorough and methodical" - Shahjamal Biswas
"Very intuitive and in-depth! so far" - Nikhil Valse
"A good explanation for this problem." - Bhuvan Akoju
"So far good explanation on DS ,recursion and quizzes." - Anuradha Yadavalli
"the instructor is very good at explaining and simplifying complex concept. this course cover all the DSA module in depth withs great examples" - RODRIGUE NGONGANG
"excellent" - Neha Nayak
"Awesomly attractive course!" - Dariusz Jenek
"Great one" - Wilson Edafe
"Excellent Teaching" - Ameeruddin Syed
"It is an excellent platform !!" - Subhajit Bera

About the Course:

Welcome to the Data Structures and Algorithms Coding Interview Bootcamp with Python!

The primary goal of this course is to prepare you for coding interviews at top tech companies. By tackling one problem at a time and understanding its solution, you'll accumulate a variety of tools and techniques for conquering any coding interview.

Daily Data Structures and Algorithms Coding Challenges:

The course is structured around daily coding challenges. Consistent practice will equip you with the skills required to ace coding interviews. For the next 40 days commit to yourself to practice atleast 2 coding interview questions everyday. You don't need any setup for this as the daily coding problem challenges can be solved in the coding environment provided by Udemy. The course will automatically track your progress and you just need to spend your time making actual progress everyday.

Topics Covered:

We start from the basics with Big O analysis, then move on to very important algorithmic techniques such as Recursion, Backtracking and Dynamic Programming Patters. After this we move to cover common data structures, and discuss real problems asked in interviews at tech giants such as Google, Meta, Amazon, Netflix, Apple, and Microsoft.

For each question, we will:

Discuss the optimal approach
Explain time and space complexity
Code the solution in Python (you can follow along in your preferred language)

Additional Resources :

The course includes downloadable resources, motivational trackers, and cheat sheets.

Course Outline:

Day 1: Arrays, Big O, Sorted Squared Array, Monotonic Array
Day 2:Recursion,k-th symbol in Grammar,Josephus problem
Day 3:Recursion, Tower of Hanoi, Power Sum
Day 4:Backtracking, Permutations, Permutations 2
Day 5:Backtracking, Subsets, Subsets 2
Day 6:Backtracking, Combinations, Combinations Sum 1
Day 7:Backtracking,Combinations Sum 2,Combinations Sum 3
Day 8:Backtracking,Sudoku Solver, N Queens
Day 9:Dynamic Programming, Fibonacci, Climbing Stairs
Day 10:Dynamic Programming, Min Cost Climbing Stairs, Tribonacci
Day 11:Dynamic Programming, 01 Knapsack, Unbounded Knapsack
Day 12:Dynamic Programming, Target Sum, Partition Equal Subset Sum
Day 13:Dynamic Programming, LCS, Edit Distance
Day 14:Dynamic Programming, LIS, Max Length of Pair Chain, Russian Doll Envelopes
Day 15:Dynamic Programming, Palindromic Substrings, Longest Palindromic Substring, Longest Palindromic Subsequence
Day 16:Dynamic Programming, Palindrome Partitioning, Palindrome Partitioning 2
Day 17:Dynamic Programming, Word Break, Matrix Chain Multiplication
Day 18:Dynamic Programming, Kadane's algorithm - Max Subarray, Maximum Product Subarray
Day 19:Greedy Algorithms - Fractional Knpasack, Non overlapping Intervals
Day 20:Greedy Algorithms - Jump Game 1, Minimum # of arrows to burst baloons
Day 21:Greedy Algorithms - Two City Scheduling, Boats to Save people
Day 22:Greedy Algorithms - Task Scheduler, Largest Number
Day 23:Greedy Algorithms - Gas Stations, Jump Game 2
Day 24: Arrays, Rotate Array, Container with Most Water
Day 25: Hash Tables, Two Sum, Isomorphic Strings
Day 26: Strings, Non-Repeating Character, Palindrome
Day 27: Strings, Longest Unique Substring, Group Anagrams
Day 28: Searching, Binary Search, Search in Rotated Sorted Array
Day 29: Searching, Find First and Last Position, Search in 2D Array
Day 30: Sorting, Bubble Sort, Insertion Sort
Day 31: Sorting, Selection Sort, Merge Sort
Day 32: Sorting, Quick Sort, Radix Sort
Day 33: Singly Linked Lists, Construct SLL, Delete Duplicates
Day 34: Singly Linked Lists, Reverse SLL, Cycle Detection
Day 35: Singly Linked Lists, Find Duplicate, Add 2 Numbers
Day 36: Doubly Linked Lists, DLL Remove Insert, DLL Remove All
Day 37: Stacks, Construct Stack, Reverse Polish Notation
Day 38: Queues, Construct Queue, Implement Queue with Stack
Day 39: Binary Trees, Construct BST, Traversal Techniques
Day 40: Pre order and In order Traversal of Binary Tree - Iterative
Day 41: Post Order Traversal Iterative, Path Sum 2
Day 42: Construct Binary Tree from Pre and In order Traversal ^ In and Post order Traversal
Day 43: Binary Trees, Level Order Traversal, Left/Right View
Day 44: Level order Trav 2, ZigZag Traversal
Day 45: Vertical order Traversal, Sum root to leaf numbers
Day 46: Binary Trees, Invert Tree, Diameter of Tree
Day 47: Binary Trees, Convert Sorted Array to BST, Validate BST
Day 48: Lowest common Ancestor of BST, Unique BST 2
Day 49: Lowest common Ancestor of Binary Tree, Unique BST 1
Day 50: Serialize and Deserialize Binary Tree, N-ary Tree Level Order Traversal
Day 51: Heaps, Max Heap, Min Priority Queue
Day 52: Graphs, BFS, DFS
Day 53: Graphs, Number of Connected Components, Topological Sort
Day 54: Number of Provinces, Find if path exists in Graph
Day 55: Number of Islands, Numbers with same consecutive differences

My confidence in your satisfaction with this course is so high that we offer a complete money-back guarantee for 30 days! Thus, it's a totally risk-free opportunity. Register today, facing ZERO risk and standing to gain EVERYTHING.

So what are you waiting for? Join the best Python Data Structures & Algorithms Bootcamp on Udemy.

I'm eager to see you in the course.

Let's kick things off! :-)

Jackson

Who this course is for:

Folks looking to get into top Tech companies in Software Engineering roles
Folks looking to ace the DSA part in Data Science Interview
Self taught programmers looking for their first job
Experienced developers wanting to get into MAANG companies ( top tech firms)

Data Structures and Algorithms in Python: DSA Course

What you'll learn

Explore related topics

Coding Exercises

Course content

Day 1: Arrays Data Structures and Algorithms21 lectures • 1hr 52min

Day 2: Arrays Data Structures and Algorithms10 lectures • 1hr 6min

Day 3: Recursion31 lectures • 2hr 36min

Day 4: Recursion Continued11 lectures • 1hr 2min

Day 5: Backtracking18 lectures • 1hr 16min

Day 6: Backtracking11 lectures • 53min

Day 7: Backtracking14 lectures • 47min

Day 8: Backtracking8 lectures • 21min

Day 9: Backtracking13 lectures • 1hr 15min

Day 10: Dynamic Programming21 lectures • 1hr 28min

Requirements

Description

Who this course is for: