Introduction to GitHub
Data Science is on its way to becoming one of the twenty-first century’s most important pieces of technology. Individuals and organizations are astounded by Data Science’s powers in various disciplines, including education, healthcare, research, information technology, and many more. Numerous online resources are available if you’re interested in learning more about Data Science. One such incredible network is the community of software engineers’ hangouts, GitHub.
Github is a collaborative version control system used by millions of developers for working on projects together. Using GitHub, we can manage and track the history of the changes made to our code over time. Developers can roll back to an older project version if someone makes a mistake. So, GitHub helps developers publish open-source projects and collaborate with other developers, protecting them from making human errors in the source code.
It would be inaccurate to describe GitHub as only a code repository and collaboration tool because it is much more. Although few people are aware of it, GitHub is also one of the finest locations to learn about a wide collection of projects made utilizing the many programming languages accessible today for a variety of modern use cases. In this article, we’ve listed down 10 of the best GitHub repositories of 2022 for learning all about Data Science.
Why is Collaboration Required in Data Science?
Data science is an industry that has blown up over the past few decades or so. Many advancements and new technologies have been introduced during this time, like pandas, scikit-learn, TensorFlow, and many others. All these frameworks and libraries were shared with the public through GitHub, and many developers then worked together to improve these open-source frameworks. This is why it is important to stay up to date with the trending repositories being published on GitHub.
In this article, we’ll be taking a look at some of the trending Data Science GitHub Repositories of 2022.
Top 10 Data Science GitHub Repositories of 2022
1. Ray
Ray is an open-source framework designed to scale AI and python workloads. It consists of a distributed runtime and It has a wide set of libraries at its disposal which can be used for tasks like distributed data preprocessing and training, scalable hyperparameter tuning, scalable reinforcement learning, scalable and programmable serving, and much more. Ray can easily scale your python code from a laptop to a cluster without needing any other infrastructure.
- Here is a link to project ray’s repository: Github
- # of stars earned in 2022 – 5K
2. Streamlit
After you’ve created a machine learning model, you must be able to serve it so that you can use it later. There are many tools available for data scientists to serve their models, like Django and flask. But there is a pre-requisite of HTML and CSS with these frameworks. So to serve models, streamlit created an open-source solution. Streamlit allows us to turn our scripts into web applications that can be shared with anyone. No frontend knowledge is required with streamlit. With streamlit’s creative solution, we can create interactable web apps with just a few lines of code.
- Here is a link to Streamlit’s repository: https://github.com/streamlit/streamlit
- # of stars earned in 2022 – 5.5K
3. Lightning AI
With so many advancements, AI Systems and Machine learning workloads are becoming increasingly intensive. It is difficult to maintain an infrastructure for heavy AI systems. This is where Lightning AI comes into the picture. Lightning AI is a platform that we can use to build AI systems, train models, and deploy them on the cloud without having to worry about any infrastructure or scalability issues. With lightning AI, we can use its modularity to train and deploy our models.
- Here is a link to Lightning AI’s repo: https://github.com/Lightning-AI/lightning
- # of stars earned in 2022 – 4.5K
4. Excelize
There are a lot of languages data scientists use daily, like Python and R. Go is another language that is used for data science. It is a statically typed open-source language that can be used to build secure and scalable systems. Excelize is a Go Language library for reading and writing Microsoft Excel spreadsheets. It is a highly compatible library that allows you to interact with all types(extensions) of excel spreadsheets. It is also cross-platform compatible, which gives ease of access to its users.
- Here is a link to Excelize’s repository: https://github.com/qax-os/excelize
- # of stars earned in 2022 – 3.6K
5. Microsoft Neural Network Intelligence
AutoML has achieved a lot of success in recent years. AutoML provides tools to create Machine learning models without having to write much code to accelerate research time for machine learning. Microsoft’s open-source Neural Network Intelligence does just that with its very powerful toolkit. We can use it to automate processes like Hyperparameter optimization, Neural architecture search, model compression, and feature engineering.
- Here is a link to Microsoft NNI’s repository: https://github.com/microsoft/nni
- # of stars earned in 2022 – 1.7K
6. Gradio
Working in teams as data scientists, there will be situations where you’ll have to share your models with your teammates and also share demos with the stakeholders. When this situation arises, gradio is at your service. Gradio can be used to create interactive apps that can help you demonstrate your machine learning models. Not only that, Gradio has got your back when you need to deploy or even debug your python code. This is why gradio is a very useful tool for data scientists who often share their models as web applications.
- Here is a link to gradio’s repository: https://github.com/gradio-app/gradio
- # of stars earned in 2022 – 6.7K
7. DVC
Version control is a way to manage and track the changes that you make to your software. But when it comes to tracking changes made on a large dataset or machine learning model, it becomes a challenge. DVC, or Data Version Control, is an open-source tool that we can use to version large data sets and machine learning models. It also supports SSH, so you can access all file systems like AWS S3 and your local storage. DVC supports both structured and unstructured data for your projects.
- Here is a link to DVC’s repository: https://github.com/iterative/dvc
- # of stars earned in 2022 – 1.8K
8. Prefect
A big part of data science is getting the data from one location to another systematically while ensuring that no data is leaked or corrupted in the process. This can take a lot of time and effort. Prefect 2.0 is a framework that will help you with your data flow problems. Powered by the Orion engine, Prefect can be used to orchestrate and organize your data flow activities. It provides workflow functionalities like scheduling, caching, distributed computing, and a lot of other very useful features.
- Here is a link to Prefect’s repository: https://github.com/PrefectHQ/prefect
- # of stars earned in 2022 – 3.6K
9. Modin
Every organization, whether it is big or small, is trying to leverage data to grow its businesses. This has resulted in a data revolution which led to enormous amounts of data being generated. Handling so much data and gaining insights from it is a difficult task as it requires better computation techniques. Enter modin, modin is a library in python which is a replacement for pandas. It can upscale any pandas workflow so that we can work with large datasets. Pandas can run out of memory when dealing with large datasets, and it works only on a single thread. Modin, on the other hand, uses all the cores of your system for parallel computation and thus increases the efficiency of your code while allowing you to work with very large datasets.
- Here is a link to Modin’s repository: https://github.com/modin-project/modin
- # of stars earned in 2022 – 1.2K
10. Vaex
As we discussed earlier, pandas is a very good library with easy-to-understand APIs, but it falls short when handling large datasets, pandas is inefficient. Every data science professional should know the right tools to work with large data sets. An alternative to the pandas library is vaex. Vaex is an open-source library for python, which harnesses the power of lazy computation to visualize, explore, and calculate statistics for large datasets containing billions of rows. It can work with more than a billion rows per second. It also has the option to create interactive visualizations.
- Here is a link to Vaex’s repository: https://github.com/vaexio/vaex
- # of stars earned in 2022 – 0.6K
Conclusion
In this article, we got to explore GitHub and get an overview of some of the trending frameworks and Data Science GitHub Repositories of 2022 that are useful for a range of use cases. For working in the data science industry, we need to stay updated with the latest technologies being released for public use. The repositories mentioned in this article are only the tip of the iceberg; there are lots of more very powerful Data Science GitHub Repositories of 2022. I encourage you to explore other GitHub yourself based on your interest.
