Review Technical Foundation: Big O Notation
As you go through the topics covered in this unit, you will see the importance of Big O Notation. For example, you will be able to assess and choose sorting algorithms based on efficiency according to some predefined inputs.
Watch this video to learn more about Big O Notation. This video will cover:
- How complex algorithms are in time and space
- How Big O notation translates this complexity and how it can be used to rank algorithms regarding their efficiency
- Why knowing about Big O Notation is important for job interviews and the GCA
Additional Resources
Understanding Big O Notation is crucial for algorithm analysis and performance optimization. Here are some additional resources to help solidify your understanding:
Key Concepts
- Time Complexity: How runtime grows as input size increases
- Space Complexity: How memory usage grows as input size increases
- Common Complexities: O(1), O(log n), O(n), O(n log n), O(n²), O(2ⁿ), O(n!)
- Worst Case vs. Average Case: Understanding the difference and when each matters
Reference Materials
Practice Problems
Work on these practice problems to improve your ability to analyze algorithm complexity:
- Determine the time complexity of different search and sorting algorithms
- Analyze the complexity of recursive functions
- Optimize an algorithm to improve its Big O complexity
- Compare multiple solutions to the same problem based on complexity
Remember that understanding Big O Notation will help you make informed decisions about algorithm selection in your projects and prepare you for technical interviews.