Udemy - Time and space complexity analysis (big-O notation)

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Time and space complexity analysis (big-O notation) [TutsNode.com] - Time and space complexity analysis (big-O notation) 1. Complexity analysis basics
  • 3. Big-O, big-Ω, and big-θ notations.mp4 (171.9 MB)
  • 3. Big-O, big-Ω, and big-θ notations.srt (39.4 KB)
  • 2. Examples.srt (28.2 KB)
  • 4. Extra resource Notations mathematical definition.html (2.5 KB)
  • 5. Quiz Big-O, big-Ω, and big-θ notations.html (0.1 KB)
  • 1. Introduction.srt (15.4 KB)
  • 6. Best, average, and worst case.srt (13.3 KB)
  • 7. Complexity classes.srt (10.3 KB)
  • 2. Examples.mp4 (79.4 MB)
  • 1. Introduction.mp4 (44.0 MB)
  • 6. Best, average, and worst case.mp4 (39.6 MB)
  • 7. Complexity classes.mp4 (35.4 MB)
  • 3.1 big O, big Omega, big Theta.pptx (5.3 MB)
  • 2.1 examples.pptx (2.1 MB)
  • 1.1 what is complexity analysis.pptx (1.3 MB)
  • 6.1 best, average, and worst case.pptx (1.0 MB)
  • 7.1 complexity classes.pptx (660.8 KB)
2. How to analyze the complexity of an algorithm
  • 3. How to analyze the complexity of an algorithm.srt (32.5 KB)
  • 7. Find pair that sums up to k problem analysis.srt (16.4 KB)
  • 2. Quiz Analyze the complexity.html (0.1 KB)
  • 4. Quiz Deduce the complexity from T(n).html (0.1 KB)
  • 5. Amortized complexity.srt (12.1 KB)
  • 1. Complexities hierarchy.srt (12.1 KB)
  • 7.1 find_pair.py (0.9 KB)
  • 6. How to compare two algorithms.srt (7.9 KB)
  • 3. How to analyze the complexity of an algorithm.mp4 (92.0 MB)
  • 7. Find pair that sums up to k problem analysis.mp4 (50.6 MB)
  • 1. Complexities hierarchy.mp4 (46.9 MB)
  • 6. How to compare two algorithms.mp4 (29.8 MB)
  • 5. Amortized complexity.mp4 (28.8 MB)
  • 3.1 how to analyse complexity.pptx (3.0 MB)
  • 7.2 find pair that sums up to k.pptx (1.7 MB)
  • 1.1 complexities.pptx (1.2 MB)
  • 6.1 how to compare algorithms.pptx (979.4 KB)
  • 5.1 amortized complexity.pptx (797.4 KB)
6. Data structures and complexity analysis
  • 4. Trees.srt (28.7 KB)
  • 4. Trees.mp4 (96.2 MB)
  • 2. Linked lists, stacks, and queues.srt (13.7 KB)
  • 3. Hash tables and sets.srt (12.6 KB)
  • 6. Graphs.srt (11.7 KB)
  • 1. Arrays.srt (10.1 KB)
  • 7. Extra resource Summary.html (4.9 KB)
  • 5. Mathematical proof of time complexity of building a binary heap.html (0.2 KB)
  • 6. Graphs.mp4 (40.8 MB)
  • 2. Linked lists, stacks, and queues.mp4 (37.2 MB)
  • 3. Hash tables and sets.mp4 (34.1 MB)
  • 1. Arrays.mp4 (27.7 MB)
  • 4.1 trees.pptx (2.2 MB)
  • 2.1 linked lists stacks queues.pptx (1.3 MB)
  • 6.1 graphs.pptx (1.0 MB)
  • 1.1 arrays.pptx (902.5 KB)
  • 3.1 hash tables and sets.pptx (533.2 KB)
3. Searching algorithms and complexity analysis
  • 3. Knuth-Morris-Pratt (KMP) algorithm.srt (19.4 KB)
  • 3.2 kmp.py (0.6 KB)
  • 2. Binary search algorithm.srt (15.6 KB)
  • 1.2 linear_search.py (0.1 KB)
  • 2.2 binary_search.py (0.3 KB)
  • 1. Linear search algorithm.srt (8.3 KB)
  • 3. Knuth-Morris-Pratt (KMP) algorithm.mp4 (52.7 MB)
  • 2. Binary search algorithm.mp4 (48.0 MB)
  • 1. Linear search algorithm.mp4 (26.2 MB)
  • 2.1 binary search.pptx (1.4 MB)
  • 3.1 kmp algorithm.pptx (1.2 MB)
  • 1.1 linear search.pptx (989.1 KB)
4. Sorting algorithms and complexity analysis
  • 1.1 sorting algorithms intro.pptx (492.8 KB)
  • 4. Selection sort.srt (3.6 KB)
  • 12. Extra resource Summary.html (0.4 KB)
  • 7. Quicksort.srt (13.7 KB)
  • 11. Shell sort.srt (11.0 KB)
  • 5. Merge sort.srt (9.4 KB)
  • 6. Heapsort.srt (9.3 KB)
  • 8. Counting sort.srt (8.2 KB)
  • 9.1 radix_sort.py (0.6 KB)
  • 2.2 insertion_sort.py (0.2 KB)
  • 4.1 selection sort.pptx (477.2 KB)
  • 3.1 bubble_sort.py (0.3 KB)
  • 4.2 selection_sort.py (0.2 KB)
  • 6.2 heapsort.py (0.6 KB)
  • 10.2 bucket sort.pptx (463.0 KB)
  • 1. Introduction.srt (6.4 KB)
  • 7.2 quicksort.py (0.8 KB)
  • 2. Insertion sort.srt (5.6 KB)
  • 9. Radix sort.srt (5.1 KB)
  • 8.2 counting_sort.py (0.4 KB)
  • 3. Bubble sort.srt (4.7 KB)
  • 10. Bucket sort.srt (4.1 KB)
  • 10.1 bucket_sort.py (0.4 KB)
  • 11.1 shell_sort.py (0.3 KB)
  • 5.1 merge_sort.py (0.7 KB)
  • 9.2 radix sort.pptx (445.6 KB)
  • 7. Quicksort.mp4 (40.9 MB)
  • 8. Counting sort.mp4 (29.8 MB)
  • 11. Shell sort.mp4 (24.4 MB)
  • 6. Heapsort.mp4 (22.2 MB)
  • 5. Merge sort.mp4 (21.9 MB)
  • 1. Introduction.mp4 (17.6 MB)
  • 9. Radix sort.mp4 (15.5 MB)
  • 2. Insertion sort.mp4 (14.7 MB)
  • 10. Bucket sort.mp4 (12.0 MB)
  • 3. Bubble sort.mp4 (11.6 MB)
  • 4. Selection sort.mp4 (9.9 MB)
  • 11.2 shell sort.pptx (1.1 MB)
  • 7.1 quicksort.pptx (989.4 KB)
  • 7.3 quicksort rec tree.jpg (851.9 KB)
  • 8.1 counting sort.pptx (717.6 KB)
  • 2.1 insertion sort.pptx (630.1 KB)
  • 6.1 heapsort.pptx (547.5 KB)
  • 3.2 bubble sort.pptx (539.3 KB)
5. Recursive functions and complexity analysis
  • 1.1 recursion complexity intro.pptx (491.9 KB)
  • 3. Recurrence relation method.srt (12.2 KB)
  • 4. Master theorem method.srt (12.0 KB)
  • 6. Memoization.srt (11.5 KB)
  • 2. Recursion tree method.srt (11.5 KB)
  • 7. Dynamic programming.srt (10.4 KB)
  • 5. Space complexity analysis of recursive functions.srt (10.1 KB)

Description


Description

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:

Complexity analysis basics
Big-O, big-Omega, and big-Theta notations
Best, average, and worst case
Complexities hierarchy
Complexity classes (P vs NP problem)
How to analyze the time and space complexity of an algorithm
How to compare algorithms efficiency
Amortized complexity analysis
Complexity analysis of searching algorithms
Complexity analysis of sorting algorithms
Complexity analysis of recursive functions
Complexity analysis of data structures main operations
Common mistakes and misconceptions
Complexity analysis of some popular interview coding problems

Hope to see you in the course!
Who this course is for:

Programmers
Computer science students
Engineering students
Competitive programmers
Self-taught developers

Requirements

Basic programming knowledge

Last Updated 3/2021



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Udemy - Time and space complexity analysis (big-O notation)


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2 GB
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Udemy - Time and space complexity analysis (big-O notation)


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