Open-source data science projects form excellent platforms to help you improve your skills while contributing to meaningful progress. This collaboration will put you in a position to engage with a global community of data scientists and developers. Open-source data science projects help in pushing the frontier in data analysis, machine learning, and artificial intelligence. Contributing to open-source projects is highly rewarding, whether one wants to apply and improve coding skills, learn from experts, or make a meaningful contribution to the area. In this post, we have described seven of the best open-source data science projects seeking contributors by explaining key features and how one can get involved.
TensorFlow is one of the most popular and versatile deep learning and neural network models, isolated within open-source machine learning libraries. TensorFlow is a loosely versatile, scalable framework developed by Google to build and train models. More generally, TensorFlow is at the core of the tools used in research and production environments to enable developers to build and deploy cutting-edge AI solutions for diverse industries. PyTorch vs TensorFlow has been an ongoing debate for Data Scientists with TensorFlow always coming on top. So, this is a great tool to apply your project skills and boost your career as a data scientist.
It leads to flexibility in architecture, and a wide range of machine learning models can be designed. Its performance is quite smooth when working with huge sets of data. It comes with seamless integration into Google Cloud. It also supports both CPU and GPU accelerations, which increases its speed in case of complicated computations.
Website: TensorFlow GitHub
The high-performance Python library is built for relatively easier and efficient data manipulation and analysis. Pandas provides very usefully extended data structures, notably the DataFrame and Series, to the capabilities of Python, which can enable simple handling and analysis of structured data. Pandas is one of the important packages for any data scientist or analyst since most of the time is spent on cleaning, transforming, and exploring the data.
It gives fast, flexible, and expressive data structures designed to easily adapt to data changes, and is stable and highly integrated with NumPy for numerical computation, supporting a large and growing list of file formats, including CSV, Excel, and SQL. Friendly API and great documentation enable both easy and complex data manipulations.
Website: Pandas GitHub
Sklearn is one of the most famous Python libraries in machine learning. It is one of the known, easy, and powerful tools for data mining and data analysis. Scikit- encompasses a bunch of algorithms on classification, regression, clustering, and reduction of dimensionality. Scikit-Learn has become a favorite of both beginners and experienced data scientists thanks to its user-friendly API and detailed documentation.
In that sense, Scikit-Learn offers one of the widest collections of algorithms and tools, classification, regression, clustering, and model selection. Some of the other positive features of this include its simple and consistent API, efficient performance, and good integration with other Python libraries such as NumPy SciPy, and Pandas. There is also support in the form of tutorials and extended documentation.
Website: Scikit-Learn GitHub
Apache Spark is an open-source analytics engine for large-scale data processing. It provides a powerful form of distributed computing that paces up big data workloads in an instant. Thus, it supports batch processing, streaming data processing, and machine learning. Hence, working efficiently with large datasets and analyzing them requires a powerful tool that should be equipped with the following key features.
Apache Spark’s data processing offers great performance when undertaking large-scale data tasks. Furthermore, it supports batch and stream processing, provides good integration with Hadoop and other Big Data technologies, and has a modular architecture that enables it to be used very easily by plugging into machine learning libraries and data processing tools.
Website: Apache Spark GitHub
Dask is a parallel computing library. It is general, with the property of scaling existing Python libraries for data analysis to work with big data. It has been designed to fit in seamlessly with both Pandas and Numpy, providing scalable solutions to tasks using one machine or a distributed computer.
Dask can execute computations in parallel and is designed to scale from machines that can fit on a single laptop to large distributed systems. Its architecture is flexible and allows users to have efficient data handling and to extend the capacity of Python in doing big data tasks.
Website: Dask Github
Keras is an extremely high-level library for Python that provides the facility of designing and training deep learning models in an extremely simplistic manner. It provides an extremely user-friendly API, so one can easily construct a model and even play around with the model, sometimes providing complete flexibility. For all levels ranging from beginners to extremely advanced users working in the industries of Artificial Intelligence and Machine Learning, Keras is the best tool.
Keras is a high-level neural network API, also allowing for modular model construction. It goes further to integrate and support multiple neural network architectures using TensorFlow as a backend. It contains innumerable tutorials and great documentation explaining how users can develop models or train them.
Website: Keras GitHub
XGBoost is a powerful gradient-boosting library. It is highly efficient, and its performance has proven to be quite convincing in problems related to predictive modeling. Owing to high accuracy and speed, it turns out to be very helpful in developing machine learning models for data science competitions and real-world applications.
XGBoost is capable of being used across many different domains to complete large-scale and high-dimensional tasks. It offers minimal hyper-parameters and is capable of very high performance while carrying out classification and regression problems. It is applied in most machine learning projects due to its high speed and accuracy in cases where strong modeling capability is needed.
Website: XGBoost GitHub
This is an open-source machine learning library developed by the Facebook AI Research Lab. However, Pytorch’s shift from Meta to Linux Foundation shows its rapid growth. It is the most preferred library due to dynamic computation graphs and intuitive design features, easing the development process associated with deep learning models.
PyTorch provides a dynamic computation graph that helps in the ease of building and debugging flexible models. Other key features include an intuitive API, amazing GPU support with acceleration, computer vision, natural language processing, and reinforcement learning libraries, making it versatile, and most fitting for any kind of AI application.
Website: PyTorch
H2O.ai is an open-source artificial intelligence and machine learning platform. It comprises state-of-the-art algorithms and has an interactive user front end for data scientists and business analysts alike.
H2O.ai contains scalable machine learning algorithms with capabilities of automatic machine learning and integration with most data sources and platforms. Performance and ease of use are natively supported, with distributed computing and an excellent, easy way to build AI solutions.
Website: H2O.ai
OpenML is an open-source project that offers a common platform for machine learning datasets, experiments, and workflows aiming at transparent and reproducible machine learning research.
OpenML offers a repository of datasets, experiment tracking, and sharing tools so anyone can contribute by sharing their results. Open and reproducible research fosters collaboration in the creation and sharing of your results.
Website: OpenML
Contributions to open-source data science projects involve a few steps. This encompasses an understanding of the project by studying project documentation and goals to know about their objectives and outstanding issues of the project.
1. Begin with Open Issues
Most definitely, look through issues that have a "good first issue" or a "help wanted" tag. This usually is fitting for a new contributor but can also lead to the commencement of your engagement.
2. Fork and Clone
Fork the repository to your own GitHub account and clone it into your local machine for modifications.
3. Development and Testing
Work according to the chosen issue, the change shall adhere to all guidelines and standards of coding used in the project. Thorough testing should be done to make sure the changes work according to requirements.
4. Create a Pull Request
Finally, when you are through, create a pull request highlighting the changes that you have made and what impact those changes will have.
5. Stay Engaged with the Community
Feel free to engage in the discussion, drop your feedback, and check out other contributors' pull requests to guarantee a friendly environment.
6. Stay Updated
Be updated on the status of the projects for changes and actively contribute to the new issues and discussions to remain involved.
This contributes to open-source projects in data science as a way to improve your skills while collaborating with others who are experts in the field on projects that are contributing to the technologies around data science. You get to experience new tools used by other people and add to them by being a part of the top open-source data science projects. This also gives you a chance to connect with peers. Thus, these projects help you grow as a data scientist too by helping you network and improve your skills.
Go into the GitHub of the project repository, read through the guidelines for contributions, and look into issues that are flagged as good for new contributors. Interact with the community for many more resources guiding and wisdom.
For instance, some of the core skills are programming proficiency using languages such as Python, knowledge about the basics of data science, experience with version control systems such as Git, and understanding the specific tools and frameworks used for a project.
Yes, contributing to open source helps in career advancement, showcases skills, and builds a portfolio. In addition, it puts one in direct contact with other professionals in the same field.
Every project has contribution guidelines, usually mentioned in repository README or CONTRIBUTING files. This would guide you on how you can send your code, raise issues, and observe the coding standards.
Choose a project most applicable to your interest and skill. Find active projects with friendly communities and some areas where you can be of assistance.