Computer Science

Foundations of Computing

Computational Thinking and Problem Solving

• Understand the core principles of computational thinking, including decomposition, pattern recognition, abstraction, and algorithm design.
• Master problem breakdown techniques, transforming complex challenges into manageable sub-problems.
• Develop logical reasoning skills for constructing effective and efficient solutions to a wide range of computational problems.

Binary Representation and Logic

• Gain a deep understanding of binary, octal, hexadecimal number systems and their conversion.
• Explore Boolean algebra, logic gates (AND, OR, NOT, XOR, NAND, NOR), and their application in digital circuit design.
• Learn to design and analyze combinational and sequential logic circuits, forming the basis of computer hardware.

Programming Paradigms and Languages

Procedural Programming

• Master fundamental programming constructs such as variables, data types, operators, conditional statements (if-else, switch), and loops (for, while).
• Understand function declaration, definition, parameter passing mechanisms (by value, by reference), and recursion.
• Learn to organize code into modular, reusable components using structured programming techniques.

Object-Oriented Programming (OOP)

• Grasp core OOP concepts: encapsulation, inheritance, polymorphism, and abstraction.
• Design and implement classes, objects, interfaces, and abstract classes, applying principles like composition and aggregation.
• Utilize design patterns such as Singleton, Factory, and Observer to build robust, scalable, and maintainable software systems.

Functional Programming Concepts

• Understand the principles of immutability, pure functions, higher-order functions, and referential transparency.
• Explore concepts like lambda expressions, closures, and map/filter/reduce operations for concise and parallelizable code.
• Learn to reason about program correctness and avoid side effects in concurrent environments.

Data Structures and Algorithms

Fundamental Data Structures

• Master array-based structures: dynamic arrays, multi-dimensional arrays, and their memory layouts.
• Understand linked structures: singly, doubly, and circularly linked lists, and their manipulation for insertion, deletion, and traversal.
• Implement and apply abstract data types: stacks (LIFO), queues (FIFO), and deques using various underlying structures.
• Explore hash tables and hash functions, including collision resolution strategies like chaining and open addressing, for efficient data retrieval.

Advanced Data Structures

• Gain expertise in tree structures: binary trees, binary search trees (BSTs), AVL trees, red-black trees, and B-trees for balanced data storage and retrieval.
• Implement graph representations: adjacency matrices and adjacency lists, and understand their suitability for different problem types.
• Work with heaps (min-heap, max-heap) for priority queue implementations and efficient selection algorithms.

Algorithm Design and Analysis

• Understand time and space complexity analysis using Big O notation, Big Omega, and Big Theta for evaluating algorithm efficiency.
• Master searching algorithms: linear search, binary search, and hash-based searching.
• Implement and analyze sorting algorithms: bubble sort, selection sort, insertion sort, merge sort, quicksort, heap sort, and radix sort.
• Learn graph algorithms: Breadth-First Search (BFS), Depth-First Search (DFS), Dijkstra's algorithm for shortest paths, Prim's and Kruskal's algorithms for minimum spanning trees.
• Explore dynamic programming and greedy algorithms for solving optimization problems, recognizing their applicability and limitations.

Operating Systems

Operating System Concepts

• Understand the role and fundamental components of an operating system, including the kernel, shell, and system calls.
• Grasp process management: process states, Process Control Blocks (PCBs), process scheduling algorithms (FCFS, SJF, Priority, Round Robin), and context switching.
• Learn about inter-process communication (IPC) mechanisms: pipes, message queues, shared memory, and semaphores.

Memory Management

• Master concepts of virtual memory, paging, segmentation, and page replacement algorithms (LRU, FIFO, Optimal).
• Understand memory allocation strategies: contiguous allocation, buddy system, and slab allocation.
• Address memory protection mechanisms and memory-mapped files.

File Systems and I/O Management

• Explore file system structure: directories, file allocation methods (contiguous, linked, indexed), and free space management.
• Understand I/O hardware, polling, interrupts, Direct Memory Access (DMA), and buffering.
• Examine disk scheduling algorithms (FCFS, SSTF, SCAN, C-SCAN) for optimizing disk access.

Concurrency and Synchronization

• Identify and resolve race conditions, deadlocks, and starvation issues in concurrent programming.
• Implement synchronization mechanisms: mutexes, semaphores, monitors, and condition variables to ensure data integrity.
• Understand classic synchronization problems like the Producer-Consumer problem and Dining Philosophers.

Computer Architecture

Digital Logic and Representation

• Understand the building blocks of digital systems: gates, flip-flops, registers, and counters.
• Master data representation formats: signed and unsigned integers, floating-point numbers (IEEE 754 standard).
• Design and analyze basic arithmetic logic units (ALUs) for addition, subtraction, and logical operations.

CPU Organization and Design

• Explore the Von Neumann and Harvard architectures.
• Understand instruction set architectures (ISAs): RISC vs. CISC principles, instruction formats, and addressing modes.
• Grasp CPU components: control unit, arithmetic logic unit (ALU), registers, and program counter.
• Learn about pipelining: stages, hazards (structural, data, control), and techniques for hazard resolution.

Memory Hierarchy and Caching

• Understand the concept of a memory hierarchy: registers, caches (L1, L2, L3), main memory, and secondary storage.
• Master cache memory principles: cache lines, associativity (direct-mapped, set-associative, fully associative), write-back vs. write-through policies, and cache coherency protocols.
• Analyze cache performance metrics: hit rate, miss rate, and average memory access time.

I/O and Peripherals

• Understand I/O organization: programmed I/O, interrupt-driven I/O, and Direct Memory Access (DMA).
• Explore bus architectures: system bus, data bus, address bus, and control bus.
• Learn about various peripheral devices and their interfaces with the CPU.

Databases

Relational Database Management Systems (RDBMS)

• Master relational model concepts: tables, rows, columns, keys (primary, foreign, candidate), and referential integrity.
• Design relational schemas using Entity-Relationship (ER) diagrams and transform them into relational tables.
• Understand normalization forms (1NF, 2NF, 3NF, BCNF) to eliminate data redundancy and anomalies.

Structured Query Language (SQL)

• Gain expert proficiency in SQL for Data Definition Language (DDL): CREATE, ALTER, DROP.
• Master SQL for Data Manipulation Language (DML): SELECT, INSERT, UPDATE, DELETE statements, including complex queries with JOINs, subqueries, and aggregate functions.
• Understand SQL for Data Control Language (DCL): GRANT, REVOKE for managing user permissions.
• Implement transaction control using COMMIT, ROLLBACK, and SAVEPOINT.

Database Concurrency and Recovery

• Understand ACID properties (Atomicity, Consistency, Isolation, Durability) of database transactions.
• Explore concurrency control mechanisms: locking (two-phase locking), timestamp ordering, and optimistic concurrency control.
• Learn about database recovery techniques: logging, checkpoints, and shadow paging.

NoSQL Databases (Introduction)

• Understand the motivations and characteristics of NoSQL databases, including their scalability and flexibility.
• Explore different NoSQL categories: key-value stores, document stores, column-family stores, and graph databases.
• Grasp the CAP theorem (Consistency, Availability, Partition Tolerance) and its implications for distributed systems.

Computer Networks

Network Models and Protocols

• Understand the OSI (Open Systems Interconnection) and TCP/IP (Transmission Control Protocol/Internet Protocol) reference models and their respective layers.
• Gain expertise in the functionality and protocols at each layer, from the physical layer to the application layer.
• Analyze network packet structures and flow at different layers.

Network Devices and Topologies

• Identify and understand the roles of network devices: hubs, switches, routers, firewalls, and access points.
• Explore network topologies: bus, star, ring, mesh, and tree, and their advantages and disadvantages.
• Learn about different network types: Local Area Networks (LANs), Wide Area Networks (WANs), and their characteristics.

Core Networking Protocols

• Master IP addressing (IPv4 and IPv6), subnetting, and CIDR (Classless Inter-Domain Routing).
• Understand Address Resolution Protocol (ARP) and Internet Control Message Protocol (ICMP).
• Explore TCP (Transmission Control Protocol) for reliable, connection-oriented communication and UDP (User Datagram Protocol) for fast, connectionless communication.
• Learn about routing protocols: RIP, OSPF, BGP, and their operation.

Application Layer Protocols and Services

• Understand how common application protocols function: HTTP/HTTPS (web), DNS (domain name resolution), SMTP/POP3/IMAP (email), FTP/SFTP (file transfer).
• Explore client-server and peer-to-peer network architectures.
• Learn about socket programming for building network applications.

Software Engineering Principles

Software Development Life Cycle (SDLC)

• Understand various SDLC models: Waterfall, Agile (Scrum, Kanban), Spiral, and DevOps.
• Learn about requirements elicitation, analysis, specification, and validation techniques.
• Master software design principles: modularity, cohesion, coupling, and design patterns.

Testing and Quality Assurance

• Develop expertise in different testing levels: unit testing, integration testing, system testing, and acceptance testing.
• Implement various testing techniques: black-box testing, white-box testing, regression testing, and performance testing.
• Understand static analysis, code reviews, and debugging methodologies for identifying and resolving defects.

Version Control and Collaboration

• Master distributed version control systems like Git, including branching, merging, conflict resolution, and repository management.
• Learn collaborative development workflows: Gitflow, feature branching, and pull requests.
• Understand the importance of continuous integration and continuous delivery (CI/CD) pipelines.

Theoretical Computer Science

Automata Theory

• Understand formal languages and grammars: regular expressions, context-free grammars.
• Master finite automata (Deterministic Finite Automata - DFAs, Non-deterministic Finite Automata - NFAs) and pushdown automata.
• Grasp their application in language recognition, compiler design, and parsing.

Computability Theory

• Explore Turing machines as a universal model of computation.
• Understand the Church-Turing thesis and its implications for what can be computed.
• Learn about decidable and undecidable problems, including the Halting Problem, and their theoretical limitations.

Complexity Theory

• Differentiate between P, NP, NP-complete, and NP-hard complexity classes.
• Understand the significance of the P vs. NP problem and its impact on algorithm design and problem solvability.
• Analyze the intrinsic difficulty of computational problems.

Artificial Intelligence and Machine Learning Fundamentals

Introduction to AI Concepts

• Understand the core goals and historical development of Artificial Intelligence.
• Explore different types of AI: symbolic AI, connectionist AI, and hybrid approaches.
• Grasp concepts of intelligent agents, problem-solving agents, and their architectures.

Machine Learning Basics

• Differentiate between supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), and reinforcement learning.
• Understand fundamental concepts like training data, testing data, overfitting, underfitting, bias-variance trade-off, and cross-validation.
• Explore basic algorithms such as linear regression, k-Nearest Neighbors (k-NN), decision trees, and Naive Bayes classifiers.

Knowledge Representation and Search

• Learn about various knowledge representation techniques: propositional logic, first-order logic, semantic networks, and frames.
• Master informed and uninformed search algorithms: Breadth-First Search (BFS), Depth-First Search (DFS), Asearch, and minimax for game playing.

Cybersecurity Fundamentals

Principles of Information Security

• Understand the core principles of Confidentiality, Integrity, and Availability (CIA triad).
• Learn about common security threats: malware, phishing, denial-of-service, man-in-the-middle attacks.
• Explore defense-in-depth strategies and security policies.

Cryptography Basics

• Grasp symmetric-key cryptography (AES, DES) and asymmetric-key cryptography (RSA, ECC).
• Understand hashing functions (SHA-256, MD5) for data integrity and digital signatures for authentication and non-repudiation.
• Learn about Public Key Infrastructure (PKI) and X.509 certificates.

Network Security

• Understand firewall types (packet filtering, stateful inspection, application layer) and intrusion detection/prevention systems (IDS/IPS).
• Explore Virtual Private Networks (VPNs) and secure communication protocols like SSL/TLS.
• Learn about common network attack vectors and countermeasures.