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Enthought to Present at the 2022 American Institute of Chemical Engineers (AIChE) Spring Meeting and Global Congress on Process Safety Company’s presentation explores current challenges facing R&D labs and how its digital transformation framework can drive new innovations and business value Austin, TX – April 04, 2022 – Enthought, a company powering digital transformation for…
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Austin, TX – June 15, 2021 – Enthought, the leading provider of technologies and services that deliver digital innovation to science-driven companies, is experiencing rapid growth as companies look to accelerate their adoption of new technologies, such as artificial intelligence and machine learning, in response to COVID-19. In support of Enthought’s growth, strategic vision and…
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As in 2020, this year’s SciPy Conference will be virtual, offering increased opportunities for attendance. 2020 set an attendance record of over 1,500, almost double the 2019 Austin, Texas conference. The event brings together attendees from industry, academia, national labs and more – showcasing projects, sharing knowledge and collaborating on code development. Summary The…
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Author: Alexandre Chabot-Leclerc, Ph.D., Director, Training Solutions Download the guide here. If you are not yet a user of Python, and its move to become the most common programming language has piqued your interest, the Enthought training team has updated its popular reference guide, ‘Transitioning from MATLAB® to Python’. The guide highlights some of the…
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Author: Brendon Hall PhD., Director Energy Solutions In 2016, a joint IOPD-ICDP Expedition no. 364 (International Ocean Discovery Program and International Continental Scientific Drilling Program) collected core samples from the peak ring of the Chicxulub crater. The impact crater was formed 66 million years ago when an estimated 15-kilometer diameter asteroid’s impact triggered a series…
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Author: Alexandre Chabot-Leclerc, Ph.D., Vice President, Digital Transformation Solutions The Enthought training team has prepared a series of 8 quick-reference guides for Pandas (the Python Data Analysis library) and 3 quick-reference guides for scikit-learn (machine learning for Python). The topics were selected based on the idea that 20% of the functionality provides 80% of the…
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