Computer Science Course

Lesson 1 • Computer Architecture Basics

Explains CPU components, memory hierarchy, and the fetch-decode-execute cycle. Connects hardware structure to software execution behavior.

Lesson 2 • Introduction to Operating Systems

Describes OS roles in resource management, process scheduling, and user interaction. Sets the stage for understanding how programs run on hardware.

Lesson 3 • Boolean Logic and Logic Gates

Introduces Boolean algebra and its physical implementation in circuits. Provides the logical foundation for understanding processors and digital systems.

Lesson 4 • Number Systems and Data Representation

Covers binary, octal, and hexadecimal systems and how data is encoded. Directly supports understanding of memory, logic gates, and low-level programming.

Lesson 1 • Control Flow and Decision Making

Covers conditional statements and loop constructs for directing program execution. Enables students to write programs that respond to varying inputs.

Lesson 2 • Arrays and Basic Data Structures

Introduces arrays, strings, and simple collections for storing multiple values. Bridges procedural programming to the data structures chapter ahead.

Lesson 3 • Variables, Types, and Expressions

Introduces data types, variable declaration, and expression evaluation. Forms the syntactic and semantic base for all subsequent programming tasks.

Lesson 4 • Functions and Modular Design

Teaches function definition, parameters, return values, and scope. Promotes code reuse and prepares students for object-oriented and functional paradigms.

Lesson 1 • Queues and Priority Queues

Covers FIFO queues, circular queues, and heap-based priority queues. Prepares students for scheduling algorithms and graph traversal strategies.

Lesson 2 • Linked Lists and Stacks

Builds singly and doubly linked lists, then derives stack behavior from them. Establishes pointer-based thinking critical for trees and graphs later.

Lesson 3 • Trees and Binary Search Trees

Introduces tree terminology, traversals, and BST insertion and search. Lays groundwork for balanced trees, heaps, and graph algorithms.

Lesson 4 • Algorithm Complexity and Big-O Notation

Formalizes time and space complexity analysis using Big-O, Omega, and Theta. Enables students to evaluate and compare algorithm efficiency rigorously.

Lesson 5 • Hash Tables and Graphs

Covers hashing, collision resolution, and graph representations with BFS and DFS. Completes the core data structure toolkit for applied problem solving.

Lesson 1 • Abstraction and Interfaces

Teaches abstraction through abstract classes and interface contracts. Reinforces separation of interface from implementation for scalable design.

Lesson 2 • Inheritance and Polymorphism

Covers single and multiple inheritance, method overriding, and runtime polymorphism. Enables flexible, extensible class hierarchies in real applications.

Lesson 3 • Classes, Objects, and Encapsulation

Defines classes, instantiation, and access control through encapsulation. Establishes the structural unit of OOP used throughout the chapter.

Lesson 4 • SOLID Principles and Clean Code

Applies SOLID principles to evaluate and refactor class designs. Bridges OOP theory to professional software engineering standards.

Lesson 1 • Database Normalization

Applies first through third normal forms to eliminate redundancy and anomalies. Ensures students can design efficient, consistent database schemas.

Lesson 2 • NoSQL and Non-Relational Databases

Compares document, key-value, column-family, and graph databases to relational models. Enables informed selection of storage technology for diverse data needs.

Lesson 3 • SQL Querying and Manipulation

Covers SELECT, INSERT, UPDATE, DELETE, and JOIN operations in SQL. Develops practical query-writing skills applicable to any relational database system.

Lesson 4 • Transactions and Concurrency Control

Explains ACID properties, transaction management, and locking strategies. Prepares students to handle concurrent data access safely in production systems.

Lesson 1 • Network Security Fundamentals

Introduces firewalls, encryption, VPNs, and common attack vectors. Builds security awareness essential for designing safe networked applications.

Lesson 2 • IP Addressing and Routing

Covers IPv4 and IPv6 addressing, subnetting, and routing protocols. Enables students to design and analyze network addressing schemes.

Lesson 3 • Wireless and Cloud Networking

Covers wireless standards, cloud networking models, and virtual networks. Prepares students for modern distributed and cloud-based infrastructure.

Lesson 4 • Transport and Application Layer Protocols

Examines TCP, UDP, HTTP, DNS, and SMTP for end-to-end communication. Connects protocol behavior to real application performance and reliability.

Lesson 1 • Software Design and Architecture

Introduces architectural styles, UML diagrams, and component design. Connects high-level structure to implementation decisions made in later phases.

Lesson 2 • Version Control and Collaboration

Applies branching, merging, and pull request workflows using version control systems. Prepares students for collaborative development in professional team environments.

Lesson 3 • Software Development Life Cycle

Covers requirements, design, implementation, testing, and maintenance phases. Frames all engineering activities within a structured, repeatable process.

Lesson 4 • Requirements Engineering

Teaches elicitation, specification, and validation of functional and non-functional requirements. Ensures students can translate stakeholder needs into actionable specifications.

Lesson 1 • Compiler Design and Language Theory

Explains lexing, parsing, semantic analysis, and code generation phases. Deepens understanding of how high-level code is transformed into machine instructions.

Lesson 2 • Artificial Intelligence and Machine Learning

Introduces supervised, unsupervised learning, and neural network architectures. Connects AI fundamentals to practical applications in data-driven software systems.

Lesson 3 • Parallel and High-Performance Computing

Covers multithreading, GPU computing, and parallel algorithm design. Equips students to optimize computation-intensive applications for modern hardware.

Lesson 4 • Distributed Systems and Cloud Computing

Covers consistency models, distributed consensus, and cloud service tiers. Prepares students to architect scalable, fault-tolerant distributed applications.