Algorithm analysis deals with the execution or running time of various operations involved. The running time of an operation can be defined as the number of computer instructions executed per operation.

In this “Algorithms Analysis – Data Structure and Algorithms” you will learn about the following topics:

1. Mathematical Background
2. Interval Notation
3. Ranges and Mid-Points
4. Sum and Product Notation
5. Elementary Functions and the Relative Growth of Such Functions
6. Two Special Useful Functions - The Floor and Ceiling Functions
7. Floor Function, Ceiling Function
8. Methods of Proof
9. Direct Proof, Indirect Proof (also called Proof by Contrapositive), Proof by Contradiction, Proof by Cases, Proof By Special Technique
10. Elementary Combinatorics and Factorial Notation
11. Addition Principle, Multiplication Principle, Factorial Notation, Number of Permutations, Number of Combinations
12. Elementary Discrete Probability
13. Equally Likely Outcomes, Certain Probabilities Sum to 1, Joint Occurrence of Independent Events, Joint Occurrence of Dependent Events, Probability Of The Occurrence Of One, Or The Other, Or Possibly Both, Of Two Events, Sample Space, Random Variable, Expectation of a Random Variable, Miscellaneous Useful Formulas
14. Algorithm
15. Characteristics of an Algorithm
16. How to Write an Algorithm?
17. Algorithm Analysis
18. Algorithm Complexity, Space Complexity, Time Complexity
19. Asymptotic Notations
20. Big Oh Notation (Ο), Omega Notation (Ω), Theta Notation (θ)
21. Common Asymptotic Notations

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