Introduction
For many people, open source has been a popular term for a while. The term "open source" refers to a variety of ideas, from the creation of software to its accessibility online. Open-source development encourages software freedom, sharing, and teamwork to create better products and services. In this article we will see these concepts in the context of Data Science, we’ll discuss why you should contribute to open-source projects as a data scientist, as well as some tools, packages, and frameworks that you can start contributing to right away.
Data science is a way of thinking about, collecting, organizing, and analyzing data to uncover new knowledge and insights. This field moves at lightning speed, and old systems can be replaced in minutes with new ones that are completely different. Some people think open source can’t apply to data science because it doesn’t have a clear definition, but looking past all this, you see it’s everywhere — including in projects like pandas, TensorFlow, R language etc. Working within open-source communities can give you an opportunity to contribute significantly to the data science world. As a Data Scientist, contributing to open-source is an excellent way to gain experience while working with large teams and codebases, interact with the developer community, add value to your resume, and, most importantly, make genuine contributions to software that you use or engage in as a data scientist.
Why you should contribute to open source
Open-source communities can be valuable resources, and the more people who contribute to them, the faster they will evolve. As a data scientist, I believe that contributing to open source is critical to my future career. Contributing to open source not only helps you advance your skill set and community but also allows you to connect with other developers in similar fields. Sharing your knowledge with others on GitHub creates opportunities that would not have otherwise existed. Google, as one of the world's largest employers of data scientists, also supports open-source projects through its Open Source Program (GOOGLE-OSS). As another GOOGLE-OSS user, I found this section of the GOOGLE codebase to be extremely useful when developing DeepLearning Frameworks such as Keras.
As a data scientist, investing in open source is a great way to leverage your skills and expertise while also benefiting the community around you. It's also one of the quickest ways to demonstrate your skills to potential employers and write new code that could solve a big problem.
Ways to contribute to open source projects
There are numerous ways to contribute if you want to. Some projects are simple and encourage general collaboration, while others are more focused on specific tasks critical to your work as a data scientist. There are numerous ways to help open-source data science projects. for example, you can help by writing code, reviewing documentation, testing new features, and running program tests.
To contribute to open-source data science, you must first learn more about the software and how to use it. Then, on top of that, you must publish your observations and findings on social media platforms such as GitHub in order to receive feedback from other people who are also interested in assisting in the solution of the same problem and improving their own skills.
Some open source packages, frameworks, and how to start contributing.
Open-source software (OSS) is a collaborative form of development in which code and many related resources are made freely available to the general public to use and build upon, with the usual caveat that original programmers retain the right to distribute their work. When you use open-source software, you have access to thousands of programmers who each built and contributed using their own time, energy, and effort. It's like joining a wonderful global community where you can share the information you've learned from your classes and gain valuable experience by helping others!
As a data scientist and machine learning engineer, you've probably used or come across one of the following packages before, making them excellent starting points.
Tensorflow: TensorFlow is a complete open-source machine learning platform. It has a comprehensive, adaptable ecosystem of tools, libraries, and community resources that enable researchers to push the boundaries of ML and developers to easily build and deploy ML-powered applications. Their website contains very detailed contribution guidelines. The GitHub page for their "good first issues" tag here.
pandas: This flexible and powerful data manipulation and analysis library for Python has an active community, with news updates on its blog. You can find the GitHub page for their “good first issue” tag here.
Numpy: Numpy provides strong numerical computing tools that frequently go hand-in-hand with Python data science work, despite not really being an ML-specific library. According to their website, they appear to value their community, which is encouraging for new contributors. The GitHub page for their "good first issue" tag is located here.
Scikit-Learn: Regression analysis, clustering, classification, and other tasks are all made possible by the popular Python library Sklearn. They are growing quickly and welcoming additional help. The GitHub page for their "good first issues" tag is located here.
How to start
Read the guideline: It's crucial to read the project's contribution guidelines once you've found the one you want to work on. Such a document should be available on every well-known open-source program's website or GitHub. Remember that while these communities are welcoming and open, they are also working hard. To avoid wasting anyone's time, including your own, you should familiarize yourself with what is expected of you as a contributor.
Find open issues: Next, browse the open issues they've identified, and when you see one you'd like to fix, ask if you can work on it or just find a solution and submit a pull request (PR) if that is permitted by their guidelines.
Fix the issue: Naturally, solving the issue is the last step. To find the problematic code, fork the repository, clone it to a local copy on your computer, do what needs to be done, create a new branch if necessary, submit your PR, and presto. You've contributed to a real piece of software.
The video below provides a clearer explanation of each of the above steps.
Conclusion
Contributing to open-source projects adds value to your resume and also increases your knowledge and skills. It demonstrates that you are capable, involved in the community, enthusiastic, and a team player. Not to mention how satisfying it is! You don't have to stick to the software I mentioned above, but if you want to get started with data science projects, those are definitely good options due to their widespread use and welcoming communities.
Your contributions to open-source projects are greatly appreciated. Your knowledge, ideas, and time can help millions of users around the world. Help us continue to create great tools that help people all over the world achieve their goals. Thanks for reading!