DACOP: Scientific Computing and Data Analysis with Python

This repository contains coursework, programming exercises, and mini-projects completed during the Data Analysis and Computational Optimization (DACOP) module at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) as part of my M.Sc. Artificial Intelligence studies.

The course introduced core concepts in scientific programming, object-oriented software development, numerical computing, and data analysis using Python.

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Repository Structure

DACOP-Scientific-Computing-Python-Academic_Project/

├── oop/
│   ├── README.md
│   ├── gym-chaos-simulator/
│   └── tic-tac-toe/
│
├── numpy/
│   └── treasure-hunt/
│
├── pandas/
│   └── data-analysis-project/
│
├── assignments/
│   ├── exercise-1/
│   └── exercise-2/
│
├── docs/
│
├── assets/
│
├── requirements.txt
│
└── README.md

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Featured Projects

🏋️ Gym Chaos Simulator

An object-oriented simulation project that models interactions within a gym environment using Python classes and objects.

Skills Demonstrated

• Object-Oriented Programming
• Software Design
• State Management
• Simulation Development
• Problem Solving

Screenshot location:

assets/gym_chaos_simulator.png

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🎮 Tic-Tac-Toe

A command-line Tic-Tac-Toe game developed using object-oriented design principles.

Features

• Human vs Computer gameplay
• Win and draw detection
• Input validation
• Replay functionality

Skills Demonstrated

• OOP Design
• Game Logic
• Control Flow
• User Interaction

Screenshot location:

assets/tic_tac_toe_demo.png

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🔍 Treasure Hunt with NumPy

A grid-based treasure hunting game implemented using NumPy arrays and randomized map generation.

Skills Demonstrated

• NumPy Arrays
• Array Indexing
• Random Number Generation
• Algorithmic Thinking

Screenshot location:

assets/numpy_treasure_hunt.png

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📊 Data Analysis Project

A practical data analysis project using Pandas to explore, clean, analyze, and visualize data.

Skills Demonstrated

• Data Cleaning
• Exploratory Data Analysis
• Data Visualization
• Statistical Analysis

Screenshot location:

assets/pandas_project.png

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Technologies Used

• Python
• NumPy
• Pandas
• Matplotlib
• Jupyter Notebook

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Key Skills Developed

• Object-Oriented Programming
• Scientific Computing
• Data Analysis
• Numerical Computation
• Data Visualization
• Computational Problem Solving
• Software Development

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Learning Outcomes

Throughout this course I gained practical experience in:

• Writing clean and maintainable Python code
• Designing software using object-oriented programming principles
• Performing numerical computations with NumPy
• Working with real-world datasets using Pandas
• Creating visualizations for data interpretation
• Applying computational methods to solve technical problems

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About the Course

DACOP combines programming fundamentals with scientific computing and data analysis techniques commonly used in engineering, automation, artificial intelligence, and research applications.

The course emphasizes practical problem solving through hands-on programming exercises and mini-projects.

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Author

Adithya Narayanan

M.Sc. Artificial Intelligence

Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)

GitHub: https://github.com/n1x3on