This comprehensive repository contains programming assignments, lab exercises, and projects from a 6-semester Computer Science Engineering curriculum. The repository showcases progression through various domains of computer science, from basic programming to advanced topics like Machine Learning and DevOps.
- Total Semesters: 6
- Programming Languages: 10+ (Python, Java, C, JavaScript, HTML/CSS, SQL, Assembly, Verilog, MATLAB, TCL/AWK)
- Subjects Covered: 15+ core CS subjects
- Lab Exercises: 200+ individual programs and notebooks
- MATLAB - Mathematical computing and visualization
- Graph plotting and analysis
- L'Hospital's rule implementation
- Maclaurin series calculations
- Parametric and polar curves
- Partial derivatives and optimization
- Python - Programming fundamentals
- Basic syntax and data structures
- Control flow and functions
- File handling and basic algorithms
- MATLAB - Advanced mathematical computing
- Complex mathematical computations
- Engineering problem solving
- C Programming - System programming fundamentals
- Pointers and memory management
- Data structures implementation
- System-level programming concepts
- Digital Design and Computer Organization (DDCO) - Hardware design
- Verilog HDL programming
- Digital circuits (Multiplexers, Demultiplexers, Flip-flops)
- Combinational and sequential logic design
- Data Structures and Algorithms (DSA) - Algorithm design
- Linear and non-linear data structures
- Sorting and searching algorithms
- Graph algorithms and dynamic programming
- Java Programming - Object-oriented programming
- OOP concepts (Inheritance, Polymorphism, Abstraction)
- Multithreading and concurrency
- Package management and access modifiers
- Operating Systems (OS) - System programming
- Process management and system calls
- Memory management algorithms
- File systems and I/O operations
- Excel - Data analysis and visualization
- Microcontrollers (MC) - Embedded systems
- Assembly language programming
- Hardware interfacing and control
- Real-time programming concepts
- Analysis and Design of Algorithms (ADA) - Advanced algorithms
- Algorithm complexity analysis
- Advanced algorithmic techniques
- SQL & Database Systems - Database management
- Database design and normalization
- Complex queries and optimization
- User Interface and User Experience (UIUX) - Design principles
- User-centered design methodologies
- Prototyping and usability testing
- Computer Networks (CN) - Network programming
- Network simulation using NS2/NS3
- TCL scripting for network analysis
- Protocol implementation and analysis
- Web Development - Full-stack web development
- HTML5, CSS3, and responsive design
- JavaScript programming and DOM manipulation
- Modern web development practices
- Machine Learning (ML) - AI and data science
- Jupyter Notebooks: Interactive ML experiments and analysis
- Python Libraries: scikit-learn, pandas, numpy, matplotlib
- Topics Covered:
- Data preprocessing and visualization
- Principal Component Analysis (PCA)
- Find-S Algorithm implementation
- K-Nearest Neighbors (KNN) classification
- Linear and polynomial regression
- Support Vector Machines
- Naive Bayes classification
- Neural networks and deep learning
- DevOps - Software development lifecycle
- Build Tools: Gradle, Maven
- CI/CD: Jenkins pipelines
- Testing: JUnit, automated testing frameworks
- Containerization: Docker and deployment strategies
- Cloud Computing (CC) - Cloud technologies
- Cloud service models and deployment
- Distributed computing concepts
- Python - Data science, ML, automation scripts
- Java - Object-oriented programming, enterprise applications
- C - System programming, data structures
- JavaScript - Web development, DOM manipulation
- HTML/CSS - Web markup and styling
- SQL - Database queries and management
- Assembly - Low-level programming for microcontrollers
- Verilog - Hardware description language
- MATLAB - Mathematical computing and analysis
- TCL/AWK - Network scripting and text processing
- Machine Learning: scikit-learn, pandas, numpy, matplotlib, seaborn
- Web Development: Modern HTML5/CSS3, responsive design
- Testing: JUnit for Java applications
- Build Tools: Gradle, Maven for project management
- Version Control: Git for source code management
- IDEs: VS Code, IntelliJ IDEA
- Notebooks: Jupyter for interactive ML development
- Simulation: NS2/NS3 for network simulation
- CI/CD: Jenkins for automated builds and deployment
- Containerization: Docker for application deployment
- Data Visualization: Comprehensive data analysis and visualization techniques
- Dimensionality Reduction: PCA implementation for feature reduction
- Classification Algorithms: KNN, Naive Bayes, SVM implementations
- Regression Models: Linear and polynomial regression with real datasets
- Neural Networks: Deep learning applications and model optimization
- Operating System Concepts: Process management, memory allocation, file systems
- Data Structures: Complete implementation of linear and non-linear structures
- Network Programming: Socket programming and protocol implementations
- Responsive Design: Modern, mobile-first web applications
- Interactive UIs: Dynamic web pages with JavaScript
- User Experience: Design thinking and usability principles
- Digital Circuit Design: Complete FPGA programming in Verilog
- Microcontroller Programming: Assembly language for embedded systems
- Real-time Systems: Hardware interfacing and control applications
This repository demonstrates a structured learning path through computer science:
- Foundation (Sem 1-2): Mathematical foundations and basic programming
- Core CS (Sem 3-4): Data structures, algorithms, systems programming
- Specialization (Sem 5-6): Advanced topics in AI/ML, web tech, and DevOps
- Python 3.x with pip
- Java Development Kit (JDK) 8+
- Node.js for web development
- MATLAB (for .m files)
- C compiler (GCC recommended)
cd Sem6/ML/Lab1
jupyter notebook Lab1.ipynb
# or
python ../PythonFiles/Lab1.pycd Sem3/Java
javac Lab1.java
java Lab1cd Sem2/C
gcc -o program q1.c
./programcd Sem5/Web/Lab1
# Open index.html in a web browser
python -m http.server 8000 # For local serverThis repository represents coursework from a Computer Science Engineering program, covering:
- University: Cambridge Institute of Technology
- Duration: 6 Semesters
- Focus Areas: Software Development, Machine Learning, Systems Programming, Web Technologies
- Programming Proficiency: Multi-language development capabilities
- Algorithm Design: Efficient problem-solving approaches
- System Design: Understanding of computer systems and architecture
- Data Science: Machine learning and statistical analysis
- Web Development: Full-stack development capabilities
- DevOps: Modern software development lifecycle practices
- Problem Solving: Systematic approach to complex challenges
- Documentation: Clear code documentation and project explanations
- Project Management: Organized code structure and version control
- Continuous Learning: Adaptation to new technologies and frameworks
- 🤖 Machine Learning Pipeline: Complete ML workflow from data preprocessing to model deployment
- 🌐 Responsive Web Applications: Modern, accessible web interfaces
- ⚙️ System-level Programming: Low-level system interactions and optimizations
- 🔧 DevOps Automation: CI/CD pipelines and automated testing frameworks
- 🧮 Mathematical Computing: Complex algorithm implementations in MATLAB
- 🔌 Hardware Integration: Embedded systems programming and digital design
- University Website
- Course Curriculum Details - Interactive overview
- Individual lab manuals and documentation in respective directories
Note: This repository serves as an academic portfolio demonstrating progression through computer science fundamentals to advanced specialized topics. Each directory contains detailed implementations with comprehensive documentation and examples.