1. Programming Languages
Programming languages are the foundation of computer science, used to write and execute software programs.
- C: Fundamental language for system-level programming, focusing on memory management and performance.
- C++: Extension of C with object-oriented features like classes, inheritance, and polymorphism.
- Java: Platform-independent, widely used for enterprise applications and Android development.
- Python: High-level, easy-to-learn language with applications in web development, AI, and data science.
- JavaScript: Core technology for web development, enabling interactivity on websites.
- Other Languages: Ruby, Go, Rust, PHP, and R (used in statistical computing).
Data structures are methods to organize and store data for efficient access and modification.
- Linear Data Structures:
- Arrays: Fixed-size collections of elements.
- Linked Lists: Dynamic-size collections with nodes pointing to the next element.
- Stacks: LIFO (Last In, First Out) structure.
- Queues: FIFO (First In, First Out) structure.
- Non-linear Data Structures:
- Trees: Hierarchical data structures (e.g., Binary Trees, AVL Trees, Binary Search Trees).
- Graphs: Networks with nodes and edges (e.g., Directed, Undirected, Weighted Graphs).
- Hash Tables: Data stored using key-value pairs for fast lookup.
Algorithms define a step-by-step procedure to solve problems efficiently.
- Sorting Algorithms: Bubble Sort, Quick Sort, Merge Sort, Heap Sort.
- Searching Algorithms: Linear Search, Binary Search.
- Graph Algorithms: Dijkstra’s Algorithm, Prim’s Algorithm, Kruskal’s Algorithm, BFS, DFS.
- Dynamic Programming: Optimal Substructure and Overlapping Subproblems (e.g., Fibonacci, Knapsack).
- Greedy Algorithms: Finding solutions step-by-step by choosing the best local option (e.g., Activity Selection).
Mathematics plays a crucial role in algorithms, machine learning, and cryptography.
- Discrete Mathematics:
- Set Theory: Basics of sets, unions, intersections.
- Logic: Propositional logic, truth tables, Boolean algebra.
- Combinatorics: Permutations and combinations.
- Graph Theory: Understanding nodes, edges, paths, and circuits.
- Probability and Statistics:
- Random variables, probability distributions, Bayes’ theorem.
- Statistical inference, hypothesis testing.
- Linear Algebra:
- Vectors, matrices, eigenvalues, eigenvectors.
- Essential for graphics, machine learning, and data analysis.
- Calculus:
- Differentiation and integration.
- Applications in optimization, gradient descent, and signal processing.