Introduction to Algorithms and Data Structures

Introduction

Course provided by Treehouse, available on Free Code Camp YouTube channel.
Facilitator: Passan, a Treehouse instructor.
Audience: High school/college students, developers, or anyone learning to code.
Goal: Familiarize with the basics of algorithms, evaluating their performance and utility.
Course Focus: Tools needed to evaluate algorithms, not specifically on any single algorithm.
Programming Language: Python.

An algorithm is a set of steps or instructions for completing a task (e.g., recipe, tasks in the morning).
In computer science, it means steps a program takes to finish a task.
If you've written code, you have written an algorithm.
Algorithms solve problems effectively and efficiently.
Recognizing properly when to apply an algorithm is crucial.
Algorithmic Thinking: Break a problem into distinct steps and identify which algorithm/data structure to use.
Interviews in tech often test this skill to see if a candidate can break down problems into solvable components.

Example Game: Guess my number between 1 and 10.
Strategy: Establish problem bounds, adjust guesses based on feedback to reduce search space by half each time.
Efficiency: Quick for smaller ranges, performs notably better with larger ranges.
Result: Demonstrates efficiency and speed of algorithmic approach.

An algorithm must:
1. Have a clear problem statement with defined input and output.
2. Follow specific, distinct steps.
3. Produce a result.
4. Complete in a finite amount of time.

Time Complexity: Measure of how long it takes the algorithm to complete (e.g., best, average, worst case scenarios).
Space Complexity: Measure of the amount of memory an algorithm takes.
Algorithms are represented using Big-O notation to reflect their complexity and efficiency.
- Common complexities: O(1) (Constant), O(log n) (Logarithmic), O(n) (Linear), O(n^2) (Quadratic), etc.

Recursion: Functions calling themselves, includes base case to stop recursion and avoid infinite loops.
Example: Recursively summing numbers in a list.
Handling challenges like stack overflow by using base cases and optimizing algorithms is crucial.

Contiguous data structures, easy to access values with constant time complexity O(1).
Common operations: access, search, insert, and delete.
Insertion and deletion involve shifting elements, leading to O(n) complexity for those operations.

Consists of nodes with data and reference to the next node.
Advantages for insertion and deletion at the cost of linear search O(n).
Implementing common operations like adding nodes, searching, and deleting with linear time complexity.

Concept: Divide list into smaller parts, sort, and then merge them back.
Implementation:
- Recursive splitting of lists until single-element lists.
- Merging and sorting those lists step by step.
Complexity: O(n log n) for true implementation but can be affected by list slicing (O(n log n)).

Concept: Select a pivot, partition elements around it, recursively apply sort.
Faster than merge sort due to fewer operations per partition but has worst-case scenarios (O(n^2)). Average case: O(n log n).

Select smallest element, place it at the beginning. Repeat for remaining elements.
Time complexity: O(n^2). Less efficient than divide-and-conquer methods.

Learning algorithms involves recognizing when and how to use them effectively.
Algorithmic thinking is critical for tackling complex problems.
Recursion and divide-and-conquer are powerful strategies within algorithm design.
Practical usage often requires balancing time and space complexity based on context and specific requirements.

Modify implementations to improve efficiency.
Implement other sorting algorithms (e.g., Bubble Sort, Insertion sort) for comparison.
Explore more complex data structures (e.g., trees, graphs) and algorithms.