#### Overview of this book

Begin your journey into the fascinating world of algorithms with this comprehensive course. Starting with an introduction to the basics, you will learn about pseudocode and flowcharts, the fundamental tools for representing algorithms. As you progress, you'll delve into the efficiency of algorithms, understanding how to evaluate and optimize them for better performance. The course will also cover various basic algorithm types, providing a solid foundation for further exploration. You will explore specific categories of algorithms, including search and sort algorithms, which are crucial for managing and retrieving data efficiently. You will also learn about graph algorithms, which are essential for solving problems related to networks and relationships. Additionally, the course will introduce you to the data structures commonly used in algorithms. Towards the end, the focus shifts to algorithm design techniques and their real-world applications. You will discover various strategies for creating efficient and effective algorithms and see how these techniques are applied in real-world scenarios. By the end of the course, you will have a thorough understanding of algorithmic principles and be equipped with the skills to apply them in your technical career.
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Chapter 1: Introduction to Algorithms
Chapter 2: Pseudocode and Flowcharts
Chapter 3: Algorithm Efficiency
Chapter 4: Basic Algorithm Types
Chapter 5: Search Algorithms
Chapter 6: Sort Algorithms
Chapter 7: Graph Algorithms
Chapter 8: Data Structures Used in Algorithms
Chapter 9: Algorithm Design Techniques
Chapter 10: Real World Applications of Algorithms
Conclusion
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# 3.3 Introduction to Big O Notation

Welcome to one of the most important concepts in computer science - the Big O notation. The Big O notation is a tool that we use to describe how the efficiency of an algorithm changes as the size of the input grows.

This tool provides us with a high-level understanding of the algorithm and gives us an upper bound of the time or space complexity in the worst-case scenario. It helps us understand the scalability of the algorithm and how it will behave when the input size increases.

By using the Big O notation, we can predict how much time and space an algorithm will take and compare it with other algorithms to determine the best one for a particular task. Additionally, this notation is programming language agnostic and can be used to describe the efficiency of an algorithm on any platform.

In conclusion, the Big O notation is a powerful tool that helps computer scientists and engineers analyze and optimize algorithms to improve performance and efficiency...