Archive for the 'SciPy' category

Webinar: Work Better, Smarter, and Faster in Python with Enthought Training on Demand

Jul 22 2014 Published by under Enthought Training on Demand, NumPy, Python, SciPy, Training, Webinars


Join Us For a Webinar

We’ll demonstrate how Enthought Training on Demand can help both new Python users and experienced Python developers be better, smarter, and faster at the scientific and analytic computing tasks that directly impact their daily productivity and drive results.

Enthought Training on Demand Webinar

Space is limited! Click a webinar session link below to reserve your spot today:

  • Tues, August 5, 2014, 1:00-1:45 PM CDT (REGISTER)
  • Wed, August 6, 2014, 8:00-8:45 AM CDT (REGISTER)
  • Wed, August 13, 2014 11:00-11:45 AM CDT (REGISTER)


What You’ll Learn

Whether you’re new to the language or looking to expand your existing capabilities, you’ll see examples of how this innovative training can help you:

    • Work Better by using the most effective approaches to problems, reducing time spent on trial and error; we’ll show you not just the “what” but the “how” and “why”
    • Work Smarter by building and refining your use of the language and deepening your skillset with new tools and techniques proven to make you more efficient and deliver more insightful results
    • Work Faster by accelerating your learning through courses tailored to the particular tasks and needs of your role; you’ll focus your learning time on the 20% of topics you’ll put to use 80% of the time

See FREE course preview videos

See the course catalog / buy a course(s) here 


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Exploring NumPy/SciPy with the “House Location” Problem

Oct 16 2013 Published by under General, NumPy, Python, SciPy, Training

Author: Aaron Waters

I created a Notebook that describes how to examine, illustrate, and solve a geometric mathematical problem called “House Location” using Python mathematical and numeric libraries. The discussion uses symbolic computation, visualization, and numerical computations to solve the problem while exercising the NumPy, SymPy, Matplotlib, IPython and SciPy packages.

I hope that this discussion will be accessible to people with a minimal background in programming and a high-school level background in algebra and analytic geometry. There is a brief mention of complex numbers, but the use of complex numbers is not important here except as “values to be ignored”. I also hope that this discussion illustrates how to combine different mathematically oriented Python libraries and explains how to smooth out some of the rough edges between the library interfaces.

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