BSC with Python

A collection of Python-based homework assignments focused on fundamental concepts in Statistics, Probability, Data Analysis, and Visualization.

This repository contains standalone scripts developed as part of coursework exercises. Each file typically demonstrates a specific statistical concept through simulation, computation, and graphical visualization.

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Project Overview

The repository includes multiple homework assignments covering topics such as:

• Data visualization (bar charts, pie charts, histograms)
• Effect of outliers on statistical measures
• Gaussian (Normal) distributions
• Probability Density Functions (PDF)
• Cumulative Distribution Functions (CDF)
• Data normalization and scaling
• Statistical testing and analysis

Each script is independent and can be executed individually.

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Repository Structure

Example files included in the project:

HW1-q1.py
HW1-q2.py
HW2.py
HW3-ch4q*.py
HW3-ch8q*.py
HW4-ch9q*.py
HW5-ch6-q*.py
HW3-reports.pdf

Naming convention:

• HW → Homework number\
• chX → Chapter number\
• qY → Question number

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Requirements

• Python 3.9+ (recommended)

Required libraries (depending on the script):

• numpy
• matplotlib
• scipy
• scikit-learn

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Installation

It is recommended to use a virtual environment:

python -m venv venv

Activate the environment:

Linux / macOS

source venv/bin/activate

Windows

venv\Scripts\activate

Install dependencies:

pip install numpy matplotlib scipy scikit-learn

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Running the Scripts

Each file can be executed independently:

python HW2.py
python HW3-ch8q6.py
python HW5-ch6-q1.py

Most scripts generate visual outputs using matplotlib. Some scripts also print numerical results in the terminal.

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Reproducibility

Several scripts rely on random number generation. To ensure reproducible results, you may add a random seed at the beginning of the script:

import numpy as np
np.random.seed(42)

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Author

Mahan Baneshi

Developed as part of academic coursework in statistics and data analysis using Python.