Time and space complexity analysis (big-O notation)
Time and space complexity analysis (big-O notation), available at $74.99, has an average rating of 4.33, with 58 lectures, 16 quizzes, based on 210 reviews, and has 3740 subscribers.
You will learn about Analyze the time and space complexity of an algorithm Compare the complexity of two algorithms Complexity of searching and sorting algorithms Complexity of data structures main operations This course is ideal for individuals who are Programmers or Computer science students or Engineering students or Competitive programmers or Self-taught developers It is particularly useful for Programmers or Computer science students or Engineering students or Competitive programmers or Self-taught developers.
Enroll now: Time and space complexity analysis (big-O notation)
Summary
Title: Time and space complexity analysis (big-O notation)
Price: $74.99
Average Rating: 4.33
Number of Lectures: 58
Number of Quizzes: 16
Number of Published Lectures: 57
Number of Published Quizzes: 16
Number of Curriculum Items: 74
Number of Published Curriculum Objects: 73
Original Price: $19.99
Quality Status: approved
Status: Live
What You Will Learn
- Analyze the time and space complexity of an algorithm
- Compare the complexity of two algorithms
- Complexity of searching and sorting algorithms
- Complexity of data structures main operations
Who Should Attend
- Programmers
- Computer science students
- Engineering students
- Competitive programmers
- Self-taught developers
Target Audiences
- Programmers
- Computer science students
- Engineering students
- Competitive programmers
- Self-taught developers
You have issues with time and space complexity analysis? No worries, get ready to take a detailed course on time and space complexity analysis that will teach you how to analyze the time and space complexity of an algorithm, an important skill to have in computer science and competitive programming!
The course contains both theory and practice, theory to get all the knowledge you need to know about complexity analysis (notations, input cases, amortized complexity, complexity analysis of data structures…) and practice to apply that knowledge to analyze the time and space complexity of different algorithms!
And to make your learning experience better, the course will have quizzes, extra resources, captions, animations, slides, good audio/video quality…et cetera. And most importantly, the ability to ask the instructor when you don’t understand something!
Hours and hours of researching, writing, animating, and recording, to provide you with this amazing course, don’t miss it out!
The course will cover:
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Complexity analysis basics
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Big-O, big-Omega, and big-Theta notations
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Best, average, and worst case
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Complexities hierarchy
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Complexity classes (P vs NP problem)
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How to analyze the time and space complexity of an algorithm
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How to compare algorithms efficiency
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Amortized complexity analysis
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Complexity analysis of searching algorithms
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Complexity analysis of sorting algorithms
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Complexity analysis of recursive functions
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Complexity analysis of data structures main operations
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Common mistakes and misconceptions
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Complexity analysis of some popular interview coding problems
Hope to see you in the course!
Course Curriculum
Chapter 1: Complexity analysis basics
Lecture 1: Introduction
Lecture 2: Examples
Lecture 3: Big-O, big-Ω, and big-θ notations
Lecture 4: Extra resource: Notations mathematical definition
Lecture 5: Best, average, and worst case
Lecture 6: Complexity classes
Chapter 2: How to analyze the complexity of an algorithm
Lecture 1: Complexities hierarchy
Lecture 2: How to analyze the complexity of an algorithm
Lecture 3: Amortized complexity
Lecture 4: How to compare two algorithms
Lecture 5: "Find pair that sums up to k" problem analysis
Chapter 3: Searching algorithms and complexity analysis
Lecture 1: Linear search algorithm
Lecture 2: Binary search algorithm
Lecture 3: Knuth-Morris-Pratt (KMP) algorithm
Chapter 4: Sorting algorithms and complexity analysis
Lecture 1: Introduction
Lecture 2: Insertion sort
Lecture 3: Bubble sort
Lecture 4: Selection sort
Lecture 5: Merge sort
Lecture 6: Heapsort
Lecture 7: Quicksort
Lecture 8: Counting sort
Lecture 9: Radix sort
Lecture 10: Bucket sort
Lecture 11: Shell sort
Lecture 12: Extra resource: Summary
Chapter 5: Recursive functions and complexity analysis
Lecture 1: Introduction
Lecture 2: Recursion tree method
Lecture 3: Recurrence relation method
Lecture 4: Master theorem method
Lecture 5: Space complexity analysis of recursive functions
Lecture 6: Memoization
Lecture 7: Dynamic programming
Chapter 6: Data structures and complexity analysis
Lecture 1: Arrays
Lecture 2: Linked lists, stacks, and queues
Lecture 3: Hash tables and sets
Lecture 4: Trees
Lecture 5: Mathematical proof of time complexity of building a binary heap
Lecture 6: Graphs
Lecture 7: Extra resource: Summary
Chapter 7: Common mistakes and misconceptions
Lecture 1: Nested loops == O(n²)?
Lecture 2: Common mistakes
Lecture 3: Do we always need to optimize?
Chapter 8: Practice: complexity analysis of popular coding interview problems solutions
Lecture 1: First repeating character
Lecture 2: Maximum subarray
Lecture 3: Peak finding
Lecture 4: Palindrome linked list
Lecture 5: String subsequences
Lecture 6: Minimum cost path
Lecture 7: Longest consecutive sequence
Lecture 8: Longest common subsequence
Lecture 9: Subsets that sum up to k
Lecture 10: Balanced binary tree
Lecture 11: Array permutations
Lecture 12: Word search
Lecture 13: N-queens
Lecture 14: Bonus: space-optimized dynamic programming
Instructors
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Inside Code
Your algorithms and data structures content provider
Rating Distribution
- 1 stars: 5 votes
- 2 stars: 6 votes
- 3 stars: 25 votes
- 4 stars: 49 votes
- 5 stars: 125 votes
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