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Data Structures, AlgorithmsComplexity Analysis & Big-O Notation

65 items

1

Big-O Notation Basics

Slides / Video
2

Big-O Notation Basics

Flashcard
3

Big-O for Time and Space

Slides / Video
4

Big-O Time & Space Basics

Flashcard
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Big-Omega Notation

Flashcard
6

Linearithmic Time O(n log n)

Flashcard
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Linear Time O(n)

Quiz
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Logarithmic Time O(log n)

Quiz
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Linearithmic Time O(n log n)

Quiz
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Counting Basic Operations

Quiz
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Ordering Complexity Classes

Quiz
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Exponential Time O(2^n)

Quiz
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Quadratic Time O(n^2)

Quiz
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Sequential Code Blocks

Quiz
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Constant Time O(1)

Quiz
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How Runtime Scales

Quiz
17

Input Size Drives Complexity

Quiz
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Complexity of Nested Loops

Quiz
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Complexity of a Single Loop

Quiz
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Dropping Lower-Order Terms

Quiz
21

Dropping Constants

Quiz
22

What Complexity Analysis Is and Why It Matters

Slides / Video
23

Using Complexity to Compare Algorithms

Slides / Video
24

Best, Average, and Worst Case

Slides / Video
25

Space Complexity Basics

Slides / Video
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Analyzing Nested Loops for Time Complexity

Slides / Video
27

Analyzing Simple Loops for Time Complexity

Slides / Video
28

Dropping Constants and Lower-Order Terms

Slides / Video
29

Ranking Common Complexity Classes

Slides / Video
30

Sort Then Loop Complexity

Code Quiz
31

Dropping Constants In Big-O

Code Quiz
32

Summing Sequential Loops

Code Quiz
33

Worst Case Of Linear Search

Code Quiz
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Ordering Growth Rates

Code Quiz
35

Loop With Doubling Step

Code Quiz
36

Constant Time Array Access

Code Quiz
37

Single Loop Linear Time

Code Quiz
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Nested Loops Quadratic Time

Code Quiz
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Halving Loop Logarithmic Time

Code Quiz
40

Two Input Variables

Code Quiz
41

Space Complexity Of Copy

Code Quiz
42

Common Big-O Complexity Classes

Slides / Video
43

Big-O as an Upper Bound

Slides / Video
44

Definition and Intuition of Big-O Notation

Slides / Video
45

Best, Average, and Worst Case

Flashcard
46

Comparing Growth Rates

Flashcard
47

Sequential Statements (Addition)

Flashcard
48

Big-Theta Notation

Flashcard
49

Why We Focus on Worst Case

Flashcard
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Big-O Formal Notation and Meaning

Flashcard
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Linear Time O(n)

Flashcard
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Counting Operations

Flashcard
53

Exponential Time O(2^n)

Flashcard
54

Complexity of Single Loops

Flashcard
55

Constant Time O(1)

Flashcard
56

Logarithmic Time O(log n)

Flashcard
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Quadratic Time O(n^2)

Flashcard
58

Dropping Lower-Order Terms

Flashcard
59

Complexity of Nested Loops

Flashcard
60

Auxiliary Space vs Total Space

Flashcard
61

Factorial Time O(n!)

Flashcard
62

Dropping Constants in Big-O

Flashcard
63

How Runtime Grows as Input Grows

Slides / Video
64

Input Size n: The Basis of Analysis

Slides / Video
65

Counting Operations to Estimate Cost

Slides / Video
FlashcardAdvanced

Big-O Notation Basics

Big-O describes how an algorithm's time or space grows as input size increases.

Question

What is Big-O notation, and what does it measure?

Click to reveal answer