The best network for Python data science.

We've built a large network of open source developers who have made significant contributions to the open source projects you are using - including NumPy, Pandas, Scikit-Learn, and Matplotlib. No one else is better suited to answer your questions.

Premium support from project authors

We provide high bandwidth support via live video conferences, as well as email support for simpler and less urgent issues. Support is our core business - so you'll never have to speak with a customer service agent. 15 minutes with our developers will save your data scientists and developers hours or days of time.

Prompt and fast service

You can always file github issues and ask questions on Stack Overflow but no on else will give you SLAs or help you work through your problem over a video conference. We target a same day response time on live support requests, and we guarantee a 48 hour SLA on email requests.

Be confident in your architecture.

Before you spend the next 6 months building out a project you have to make sure the architecture is sound. Our developers can review your design and make sure you are using the underlying libraries correctly. They can also identify potential pitfalls and tell you if there is an easier way to accomplish your goals.

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There are no contracts or commitments. You get your money back if we can't answer your question. Corporate subscriptions complete with SLAs and NDAs are also available upon request.

Email Support

Direct support from open source developers of the projects you're using within 48 hours. No customer service representatives to get in your way.

Video Conference

Set up a video call in minutes to easily share screens and walk through problems together with our open source developers.

Featured Developers

We have assembled a team of developers who are experts in NumPy, Pandas, Pytables, Blosc, Scikit-Learn, and Matplotlib. Here are a few of our more active developers. Our team is still growing. Contact us if you would like to join.

Brock Mendel

Brock is a member of the pandas core development team and a contributor to python-dateutil and statsmodels. He holds a PhD in Economics, doing research primarily on Sticky Prices (macro).

Matthew Roeschke

Matt is a Pandas core developer and has years of experience utilizing Scikit-Learn, NumPy, Statsmodels, and Matplotlib in various capacities. Matt holds a masters degree in Civil Engineering from the University of California, Berkeley and now works as a Data Scientist.

Ilan Schnell

Ilan was the primary Conda developer and created the Anaconda Distribution. For his first five years at Anaconda, Ilan managed every Anaconda Distribution release, and watched it become the de facto standard distribution for data scientists using Python. Ilan holds a PhD in theoretical solid state physics from the University of Bremen in Germany. After having worked as a postdoctoral researcher at Los Alamos National Lab and Georgetown University.

Satra Ghosh

Satra is a Principal Research Scientist at the McGovern Institute for Brain Research at MIT, an Assistant Professor in the Department of Otolaryngology at Harvard Medical School, and a faculty member in the Speech and Hearing Biosciences and Technology program in the Harvard Division of Medical Sciences. He has made significant contributions to Scikit-Learn, and can also provide support for Tensorflow, Keras, Pandas and NumPy.

Harsh Gupta

Harsh Gupta has been a core contributor to the symbolic computation library SymPy for several years. He has also contributed to Conda and Numba while working with Anaconda. Harsh holds a masters degree in Mathematics and Computing from Indian Institute of Technology Kharagpur.

Hugo Shi

Hugo has more than 12 years working in science and scientific computing, specializing in financial services. He has extensive experience building out trading systems as well as developing quantitiatve trading strategies. He is one of the original authors of Bokeh and also contributes to Pandas. Hugo has significant expertise in structuring tools and applications around Jupyter, NumPy, and Pandas for domain experts. Hugo holds a PhD in electrical engineering from the University of Michigan Ann Arbor.

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