This project is currently in the preliminary stages of planning, implementation, and testing, and is subject to continuous advancements and modifications.
Astroinformatics is dedicated to advancing astronomical research using data-driven methodologies. Astronomy generates petabytes of data each night in today's data-intensive era, requiring robust computational approaches for collection, analysis, and simulation. Traditional methods often fall short in managing such vast datasets, making advanced computational strategies essential.
The repository focuses on improving data management, facilitating pattern detection with machine learning, and fostering algorithmic thinking through practical examples. It includes coding best practices, effective data format management, and hands-on examples to support these goals. By using these resources, learners will gain the skills needed to manage and analyze large astronomical datasets with modern computational methods. Special thanks to Prof. Dr. Tara Murphy for her course on Data-Driven Astronomy, which inspired the development of this repository.
This repository fosters the development of computational thinking and data-centric problem-solving skills for astronomy enthusiasts. With technological advancements, astronomy now generates high-dimensional datasets, requiring tools like Python, R, and specialized libraries such as Astropy, Scikit-learn, and TensorFlow. It's designed for astronomers, programmers, and students interested in data analysis and coding, focusing on computational methods in astronomy. Here, you'll gain hands-on experience in applying advanced coding and data processing techniques to modern astronomical challenges.
- Utilize Data Mining Techniques: Extract valuable insights from astronomical datasets using data mining and machine learning algorithms.
- Analyze Real Astronomical Data: Work with publicly available data from sources like the Sloan Digital Sky Survey (SDSS) and NASA Exoplanet Archive.
- Emphasize Interdisciplinary Collaboration: Explore the intersection of astronomy, computer science, and statistics to derive meaningful insights from the cosmos.
- **Notebooks**: Contains Jupyter notebooks that guide you through various examples and exercises related to astrophysics and computational astronomy.
- **Data**: Datasets for hands-on exercises. Publicly available datasets have been curated here for convenience.
- **Scripts**: Useful Python scripts that automate data processing tasks, visualize astronomical phenomena, and create simulations.
- **Papers and References**: A collection of papers, articles, and books for extended reading, providing a theoretical background to complement the practical exercises.- Python (3.7 or above): Most examples and tools are written in Python, which is commonly used in scientific computing.
- Jupyter Notebook: To view and interact with provided notebooks.
- Libraries: Install using
requirements.txt, which includes essential libraries like Numpy, Pandas, Astropy, Matplotlib, Scikit-learn, and TensorFlow.
To install the dependencies, run:
pip install -r requirements.txt- Clone the Repository:
git clone https://github.com/Adrita-Khan/Astroinformatics.git cd Astroinformatics - Run Jupyter Notebook:
Navigate through the notebooks to get started on data analysis and machine learning exercises.
jupyter notebook
- Special thanks to Prof. Dr. Tara Murphy for her course on Data-Driven Astronomy, which inspired many aspects of this project.
- Appreciation for the astronomical community whose open datasets have made this work possible.
This project is licensed under the MIT License - see the LICENSE file for details.
Can be found here
We welcome contributions from fellow astronomy enthusiasts, programmers, and students. Contributions can include bug fixes, new features, additional examples, or enhancements to existing content. For any inquiries or feedback, please contact:
For any inquiries or feedback, please contact:
Adrita Khan
📧 Email | 🔗 LinkedIn | 🐦 Twitter
Thank you for your interest in Astroinformatics!