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Top 23 Data Science Open-Source Projects
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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Pandas
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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Ray
Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
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pytorch-lightning
Pretrain, finetune and deploy AI models on multiple GPUs, TPUs with zero code changes.
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data-science-ipython-notebooks
Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
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Probabilistic-Programming-and-Bayesian-Methods-for-Hackers
aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)
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applied-ml
📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.
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ML-From-Scratch
Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.
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awesome-datascience
:memo: An awesome Data Science repository to learn and apply for real world problems.
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d2l-en
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
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SaaSHub
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- https://github.com/microsoft/ML-For-Beginners
Also check out this list Pitt puts out every year:
As a beginner, I was looking for something simple and flexible for developing deep learning models and that is when I found Keras. Many AI/ML professionals appreciate Keras for its simplicity and efficiency in prototyping and developing deep learning models, making it a preferred choice, especially for beginners and for projects requiring rapid development.
Superset is absolutely phenomenal. I really hope Microsoft eventually releases all of their customizations they made to it internally to the OS community someday.
https://www.youtube.com/watch?v=RY0SSvSUkMA
https://github.com/apache/superset/discussions/20094
Project mention: AutoCodeRover resolves 22% of real-world GitHub in SWE-bench lite | news.ycombinator.com | 2024-04-09Thank you for your interest. There are some interesting examples in the SWE-bench-lite benchmark which are resolved by AutoCodeRover:
- From sympy: https://github.com/sympy/sympy/issues/13643. AutoCodeRover's patch for it: https://github.com/nus-apr/auto-code-rover/blob/main/results...
- Another one from scikit-learn: https://github.com/scikit-learn/scikit-learn/issues/13070. AutoCodeRover's patch (https://github.com/nus-apr/auto-code-rover/blob/main/results...) modified a few lines below (compared to the developer patch) and wrote a different comment.
There are more examples in the results directory (https://github.com/nus-apr/auto-code-rover/tree/main/results).
Dash is a Python framework that enables you to build interactive frontend applications without writing a single line of Javascript. Internally and in projects we like to use it in order to build a quick proof of concept for data driven applications because of the nice integration with Plotly and pandas. For this post, I'm going to assume that you're already familiar with Dash and won't explain that part in detail. Instead, we'll focus on what's necessary to make it run serverless.
Project mention: [D] How do you keep up to date on Machine Learning? | /r/learnmachinelearning | 2023-08-13Made With ML
Project mention: Building in Public: Leveraging Tublian's AI Copilot for My Open Source Contributions | dev.to | 2024-02-12Contributing to Apache Airflow's open-source project immersed me in collaborative coding. Experienced maintainers rigorously reviewed my contributions, providing constructive feedback. This ongoing dialogue refined the codebase and honed my understanding of best practices.
Project mention: Creating a Sales Analysis Application with Streamlit: A Practical Approach to Business Intelligence | dev.to | 2024-04-192.-Go to https://streamlit.io, log in, and create a new app from your GitHub repository.
22. Ray | Github | tutorial
Project mention: Show HN: Dropbase – Build internal web apps with just Python | news.ycombinator.com | 2023-12-05There's also that library all the AI models started using that gives you a public URL to share. After researching it: https://www.gradio.app/ is the link.
It's used specifically for making simple UIs for machine learning apps. But I guess technically you could use it for anything.
Project mention: Step by step guide to create customized chatbot by using spaCy (Python NLP library) | dev.to | 2024-03-23Hi Community, In this article, I will demonstrate below steps to create your own chatbot by using spaCy (spaCy is an open-source software library for advanced natural language processing, written in the programming languages Python and Cython):
**[I.am.ai AI Expert Roadmap](https://i.am.ai/roadmap)**: This roadmap focuses more on AI and includes various aspects of machine learning and deep learning. It's suitable for those who want to delve deeper into AI, particularly in cutting-edge research and applications.
Project mention: Lightning AI Studios – A persistent GPU cloud environment | news.ycombinator.com | 2023-12-14
Project mention: Probabilistic Programming and Bayesian Methods for Hackers (2013) | news.ycombinator.com | 2024-02-10
Get started with Data Science in the Data Science for Beginners curricula.
Project mention: About Data analyst, data scientist and data engineer, resources and experiences | dev.to | 2024-03-26Awesome Data Science by Academic
Project mention: The fastai book, published as Jupyter Notebooks | news.ycombinator.com | 2024-01-17
Project mention: How and where is matplotlib package making use of PySide? | /r/learnpython | 2023-12-07
NOTE:
The open source projects on this list are ordered by number of github stars.
The number of mentions indicates repo mentiontions in the last 12 Months or
since we started tracking (Dec 2020).
Data Science related posts
- My Favorite DevTools to Build AI/ML Applications!
- Release: Keras 3.3.0
- Runhouse
- Frawk: An efficient Awk-like programming language. (2021)
- Hierarchical Clustering
- Creating a Sales Analysis Application with Streamlit: A Practical Approach to Business Intelligence
- Orange Data Mining
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A note from our sponsor - SaaSHub
www.saashub.com | 25 Apr 2024
Index
What are some of the best open-source Data Science projects? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | ML-For-Beginners | 66,806 |
| 2 | Keras | 60,902 |
| 3 | superset | 58,737 |
| 4 | scikit-learn | 58,046 |
| 5 | Pandas | 41,923 |
| 6 | Made-With-ML | 35,610 |
| 7 | Airflow | 34,397 |
| 8 | streamlit | 31,506 |
| 9 | Ray | 30,988 |
| 10 | gradio | 28,730 |
| 11 | spaCy | 28,704 |
| 12 | AI-Expert-Roadmap | 28,388 |
| 13 | pytorch-lightning | 26,797 |
| 14 | data-science-ipython-notebooks | 26,459 |
| 15 | Probabilistic-Programming-and-Bayesian-Methods-for-Hackers | 26,341 |
| 16 | Data-Science-For-Beginners | 26,290 |
| 17 | applied-ml | 25,875 |
| 18 | ML-From-Scratch | 23,164 |
| 19 | awesome-datascience | 23,101 |
| 20 | d2l-en | 21,628 |
| 21 | fastbook | 20,711 |
| 22 | dash | 20,472 |
| 23 | matplotlib | 19,223 |
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