Artificial intelligence to go faster and discover more.
For more than 15 years, Enthought has built AI solutions with science and engineering at the core. We accelerate digital transformation by enabling your people to leverage the benefits of Artificial Intelligence and Machine Learning.
From strategy to implementation to training, our science-first approach means we understand your unique market value at the deepest levels, while our leadership in the scientific Python community ensures your team stays ahead of the competition.
Digital Transformation and Leadership are mandatory and Enthought’s unique blend of scientists and developers build solutions that unite your teams and their data.
In-person training of scientists, engineers, data scientists, analysts, developers, and other technical professionals throughout the world in the Python programming language. Ask us about immersion training where we work side-by-side with your team until they can work on their own.
Have a team transitioning to Python from another language such as R or MATLAB? Need to level the Python skill set across a group for better productivity? We specialize in corporate Python training.
Pandas provides a powerful toolset for working with data – including reading and writing files, data cleaning, data exploration, manipulating and transforming data, data modeling and analysis, and visualization.
Enthought’s oil & gas software allows easy exploration of your data in 2D or 3D. Data is accessible from an embedded Python environment, and can be analyzed, modified, and immediately visualized using simple Python commands.
Virtual Core automates aspects of core description for geologists and petrophysicists, drastically reducing the time and effort required for core description. Its unified visualization interface displays cleansed whole-core CT data alongside core photographs and well logs. See the latest release details.
SciPy 2017, the 16th annual Scientific Computing with Python conference, was held on July 10-16, 2017 in Austin, Texas with participants from industry, academia, and government who showcased their latest projects, learned from skilled users and developers, and collaborated in code development.
Major topic tracks included: Machine Learning & AI and SciPy Tools. Mini-Symposia tracks included: Astronomy; Biology, Biophysics and Biostatistics; Computational Science and Numerical Techniques; Data Science; Earth, Ocean and Geoscience; Materials Science and Engineering; Neuroscience; and Open Data and Reproducibility.