Data Structures and Algorithms (DSA) Explained

• Data Structures concern how data can be stored in different structures
• Algorithms involve solving different problems through searching and data structure manipulation
• Data Structures and Algorithms (DSA) theory aids in using large data amounts to efficiently resolve problems

Data Structures

• Data structures are a way to store data, structured depending on the data and its intended use

• Family trees are a data structure example, chosen due to existing relationships

• Family trees help organize relationships, with links showing connections between individuals

• Data structures enable efficient large data management for tasks like databases and internet indexing

• Data structures are essential for creating efficient algorithms

• They aid in managing/organizing data, reducing complexity, and improving efficiency

Computer Science Data Structures

• Primitive Data Structures are basic structures from programming languages that represent single values like integers, characters, and booleans
• Abstract Data Structures are advanced structures using primitive types, with complex operations
• Arrays, linked lists, stacks, queues, trees, and graphs are examples of abstract data structures

Algorithms

• Algorithms are step-by-step instructions for problem-solving or goal achievement
• A cooking recipe serves as an algorithm example, the goal is a certain meal and the steps describe how to make it
• Computer Science algorithms use programming languages, and use data structures instead of food ingredients
• Algorithms are critical for programming, providing step-by-step execution instructions
• Efficient algorithms help arrive a solution faster

Algorithm Examples

• Finding the fastest route in GPS is an example of algorithms
• Navigating an airplane or car (cruise control) is an application of algorithms
• Algorithms also help in finding what users search for (search engine) and sorting movies by rating

Data Structures with Algorithms

• Data structures and algorithms (DSA) are interconnected
• DSA focuses on efficient data storage, data retrieval, data operations, and problem-solving
• Understanding DSA allows one to decide the best data structure/algorithm for a situation

DSA enables

• Faster programs that use less memory
• Systematic approaches to complex problems
• Data Structures and Algorithms (DSA) are used in a vast range of software systems

Applications of DSA

• Operating Systems
• Database Systems
• Web Applications
• Machine Learning
• Video Games
• Cryptographic Systems
• Data Analysis
• Search Engines

Key Terminology

• Algorithm: Step-by-step instructions to solve a specific problem
• Complexity: Depends on the amount of data the algorithm is processing
• Space Complexity: Measures the memory an algorithm uses based on the amount of data it's working on
• Big O Notation: Mathematical notation describing a function's behavior toward a particular value or infinity, used to describe the time complexity of an algorithm
• Recursion: Programming technique where a function calls itself
• Divide and Conquer: Breaking complex problems into smaller, solvable sub-problems, used with recursion
• Brute Force: Directly trying all possible solutions to find the best one