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https://github.com/thaikoz/data-science
This repository contains various data science projects and exercises implemented in Python using Jupyter Notebooks. It is intended for educational purposes and to document my learning journey in data science.
https://github.com/thaikoz/data-science
numpy pandas python
Last synced: about 2 months ago
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This repository contains various data science projects and exercises implemented in Python using Jupyter Notebooks. It is intended for educational purposes and to document my learning journey in data science.
- Host: GitHub
- URL: https://github.com/thaikoz/data-science
- Owner: ThaikoZ
- Created: 2023-12-28T21:15:32.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-06-26T14:33:53.000Z (7 months ago)
- Last Synced: 2024-06-26T17:48:11.951Z (7 months ago)
- Topics: numpy, pandas, python
- Language: Jupyter Notebook
- Homepage:
- Size: 9.77 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Data Science Learning Repository
This repository contains various data science projects and exercises implemented in Python using Jupyter Notebooks. It is intended for educational purposes and to document my learning journey in data science.
## Table of Contents
1. [Introduction](#introduction)
2. [Setup and Installation](#setup-and-installation)
3. [Projects and Exercises](#projects-and-exercises)
4. [Technologies Used](#technologies-used)
## Introduction
This repository is a collection of data science projects and exercises that I have worked on to enhance my skills in data analysis, machine learning, and data visualization. Each project is documented in a Jupyter Notebook, showcasing the problem-solving approach, methodology, and results.
## Setup and Installation
To run the notebooks in this repository, you need to have Python and Jupyter Notebook installed on your system. You can follow the steps below to set up the environment:
1. **Clone the repository**:
```bash
git clone https://github.com/ThaikoZ/data-science.git
cd data-science
```
2. **Create a virtual environment** (optional but recommended):
```bash
python -m venv venv
source venv/bin/activate # On Windows, use `venv\Scripts\activate`
```
3. **Install the required packages**:
```bash
pip install -r requirements.txt
```
4. **Launch Jupyter Notebook**:
```bash
jupyter notebook
```
## Projects and Exercises
1. **Data Analysis Projects**:
- Exploratory Data Analysis (EDA)
- Statistical Analysis
- Data Cleaning and Preprocessing
2. **Machine Learning Projects**: (Future)
- Supervised Learning (Regression, Classification)
- Unsupervised Learning (Clustering, Dimensionality Reduction)
- Model Evaluation and Tuning
3. **Data Visualization Projects**: (Future)
- Visualizing Data with Matplotlib and Seaborn
- Interactive Visualizations with Plotly
## Technologies Used
- **Python**: The main programming language used.
- **Jupyter Notebook**: For interactive computing and data exploration.
- **Pandas**: For data manipulation and analysis.
- **NumPy**: For numerical computing.
- **Matplotlib**: For static data visualizations.
- **Seaborn**: For statistical data visualizations.
- **Scikit-Learn**: For machine learning algorithms and tools.
- **Plotly**: For interactive visualizations.
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Feel free to contribute by submitting issues or pull requests if you find any improvements or additional projects to include. Happy learning!