Webinars

What:  A guided walkthrough and Q&A about how to migrate from MATLAB® to Python with Enthought’s Lead Instructor, Dr. Alexandre Chabot-Leclerc.

Who Should Attend: MATLAB® users who are considering migrating to Python, either partially or completely.

You and your organization might be thinking about migrating from MATLAB to Python to get access to the ecosystem and increase your productivity, but you might also have some outstanding questions and concerns, such as: How do I get started? Will any of my knowledge transfer? How different are Python and MATLAB? How long will it take me to become proficient? Is it too big a of a shift? Can I transition gradually or do I have to do it all at once? These are all excellent questions.

What:  A guided walkthrough and Q&A about Enthought’s technical training course Python for Scientists & Engineers with Enthought’s VP of Training Solutions, Dr. Michael Connell

Who Should Attend: individuals, team leaders, and learning & development coordinators who are looking to better understand the options to increase professional capabilities in Python for scientific and engineering applications

What: A comprehensive overview and live demonstration of Enthought’s latest tools for Python for the enterprise with Enthought’s Chief Technical & Engineering Officer, Didrik Pinte

Who Should Attend: Python users (or those supporting Python users) who are looking for a universal solution set that is reliable and “just works”; scientists, engineers, and data science teams trying to answer the question “how can I more easily build and deploy my applications”; organizations looking for an alternative to MATLAB that is cost-effective, robust, and powerful

What: A guided walkthrough and Q&A about Enthought’s technical training course “Python for Data Science and Machine Learning” with VP of Training Solutions, Dr. Michael Connell

Who Should Watch: individuals, team leaders, and learning & development coordinators who are looking to better understand the options to increase professional capabilities in Python for data science and machine learning applications

Enthought’s Python for Data Science training course is designed to accelerate the development of skill and confidence in using Python’s core data science tools — including the standard Python language, the fast array programming package NumPy, and the Pandas data analysis package, as well as tools for database access (DBAPI2, SQLAlchemy), machine learning (scikit-learn), and visual exploration (Matplotlib, Seaborn).

What: Presentation, demo, and Q&A with Brendon Hall, Geoscience Product Manager, Enthought

Who Should Watch:

  • Oil and gas industry professionals who are looking for ways to extract more value from expensive science wells
  • Those interested in learning how artificial intelligence and machine learning techniques can be applied to core analysis

Virtual Core automates aspects of core description for geologists, drastically reducing the time and effort required for core description, and its unified visualization interface displays cleansed whole-core CT data alongside core photographs and well logs. It provides tools for geoscientists to analyze core data and extract features from sub-millimeter scale to the entire core.

What: Presentation, demo, and Q&A with Collin Draughon, Software Product Manager, National Instruments, and Andrew Collette, Scientific Software Developer, Enthought

Who Should Watch:

  • Engineers and managers interested in extending LabVIEW with Python’s ecosystem
  • People who need to easily share and deploy software within their organization
  • Current LabVIEW users who are curious what Python brings to the table
  • Current Python users in organizations where LabVIEW is used

Engineers and scientists all over the world are using Python and LabVIEW to solve hard problems in manufacturing and test automation, by taking advantage of the vast ecosystem of Python software.  But going from an engineer’s proof-of-concept to a stable, production-ready version of Python, smoothly integrated with LabVIEW, has long been elusive.

What: Presentation and Q&A with Dr. Michael Connell, VP, Enthought Training Solutions

Who Should Watch: Anyone who wants to develop proficiency in Python for scientific, engineering, analytic, quantitative, or data science applications, including team leaders considering Python training for a group, learning and development coordinators supporting technical teams, or individuals who want to develop their Python skills for professional applications

Python is an uniquely flexible language – it can be used for everything from software engineering (writing applications) to web app development, system administration to “scientific computing” — which includes scientific analysis, engineering, modeling, data analysis, data science, and the like.

In this webinar, we’ll give you the information you need to decide whether Enthought’s Python training is the right solution for your or your team’s unique situation, helping answer questions.

Enthought’s Pandas Mastery Workshop is designed to accelerate the development of skill and confidence with Python’s Pandas data analysis package — in just three days, you’ll look like an old pro! This course was created ground up by our training experts based on insights from the science of human learning, as well as what we’ve learned from over a decade of extensive practical experience of teaching thousands of scientists, engineers, and analysts to use Python effectively in their everyday work.

In this webinar, we give you the key information and insight you need to evaluate whether the Pandas Mastery Workshop is the right solution to advance your data analysis skills in Python, including:

  • Who will benefit most from the course
  • A guided tour through the course topics
  • What skills you’ll take away from the course, how the instructional design supports that
  • What the experience is like, and why it is different from other training alternatives (with a sneak peek at actual course materials)
  • What previous workshop attendees say about the course

Built on 15 years of experience of Python packaging and deployment for Fortune 500 companies, the NEW Enthought Deployment Server provides enterprise-grade tools groups and organizations using Python need, including:

  1. Secure, onsite access to a private copy of the proven 450+ package Enthought Python Distribution
  2. Centralized management and control of packages and Python installations
  3. Private repositories for sharing and deployment of proprietary Python packages
  4. Support for the software development workflow with Continuous Integration and development, testing, and production repositories

In this webinar, Enthought’s product team demonstrates the key features of the Enthought Deployment Server and how it can take the pain out of Python deployment and management at your organization.

LabVIEW is a software platform made by National Instruments, used widely in industries such as semiconductors, telecommunications, aerospace, manufacturing, electronics, and automotive for test and measurement applications. In August 2016, Enthought released the Python Integration Toolkit for LabVIEW, which is a “bridge” between the LabVIEW and Python environment.

In this webinar, we’ll demonstrate:

  1. How the new Python Integration Toolkit for LabVIEW from Enthought seamlessly brings the power of the Python ecosystem of scientific and engineering tools to LabVIEW
  2. Examples of how you can extend LabVIEW with Python, including using Python for signal and image processing, cloud computing, web dashboards, machine learning, and more

Whether you are a data scientist, quantitative analyst, or an engineer, or if you are evaluating consumer purchase behavior, stock portfolios, or design simulation results, your data analysis workflow probably looks a lot like this:

Acquire > Wrangle > Analyze and Model > Share and Refine > Publish

The problem is that often 50 to 80 percent of time is spent wading through the tedium of the first two steps – acquiring and wrangling data – before even getting to the real work of analysis and insight. (See The New York Times, For Big-Data Scientists, ‘Janitor Work’ Is Key Hurdle to Insights)

In this webinar we’ll demonstrate how the new Canopy Data Import Tool can significantly reduce the time you spend on data analysis “dirty work,” by helping you:

  • Load various data file types and URLs containing embedded tables into Pandas DataFrames
  • Perform common data munging tasks that improve raw data
  • Handle complicated and/or messy data
  • Extend the work done with the tool to other data files

See how the Python for Excel (PyXLL) add-in helps solve data analysis challenges with advanced Python tools and analytic engines.

Extending the native analytic capabilities of Excel or implementing Excel-backend analytics on a cluster or in the cloud with VBA, C++, and other legacy languages is challenging and time-consuming. Python’s elegant syntax and extensive ecosystem of numeric and analytic packages can greatly simplify the development of advanced analytic tools and cluster/cloud-based backend capabilities in Excel.

In this webinar we demonstrate how to:

  1. Implement Excel backend parallel computations on local clusters or cloud-based platforms such as Microsoft Azure with Python
  2. Use Python functions to implement advanced, interactive graphical analytic tools in Excel
  3. Reduce risk by storing code in GitHub version control instead of embedded VBA in Excel files

See how PyXLL makes it easy to write Excel add-ins to leverage the capabilities of Python and the simplicity of the presentation of Excel.

PyXLL allows organizations to seamlessly deploy Python algorithms and models to all their Excel users. No more uncontrolled proliferation of VBA-based spreadsheet versions, no more tossing functions over the wall to get re-implemented, no more need to deploy multiple specialized applications in place of Excel-based computations. Technical teams and developers can use the full power of Python, and end users can access the results in their familiar Excel environment.

In this webinar we demonstrate how to:

  • Use Python functions to implement financial models in Excel
  • Reduce risk by storing code in version control instead of embedded in Excel files
  • Develop functions with PyXLL and Python simply, and its comparison to VBA

What: Presentation, demo, and Q&A with Brendon Hall, Geoscience Product Manager, Enthought

Who Should Watch:

  • Oil and gas industry professionals who are looking for ways to extract more value from expensive science wells
  • Those interested in learning how artificial intelligence and machine learning techniques can be applied to core analysis

Virtual Core automates aspects of core description for geologists, drastically reducing the time and effort required for core description, and its unified visualization interface displays cleansed whole-core CT data alongside core photographs and well logs. It provides tools for geoscientists to analyze core data and extract features from sub-millimeter scale to the entire core.

What:  A guided walkthrough and Q&A about how to migrate from MATLAB® to Python with Enthought’s Lead Instructor, Dr. Alexandre Chabot-Leclerc.

Who Should Attend: MATLAB® users who are considering migrating to Python, either partially or completely.

You and your organization might be thinking about migrating from MATLAB to Python to get access to the ecosystem and increase your productivity, but you might also have some outstanding questions and concerns, such as: How do I get started? Will any of my knowledge transfer? How different are Python and MATLAB? How long will it take me to become proficient? Is it too big a of a shift? Can I transition gradually or do I have to do it all at once? These are all excellent questions.

What:  A guided walkthrough and Q&A about Enthought’s technical training course Python for Scientists & Engineers with Enthought’s VP of Training Solutions, Dr. Michael Connell

Who Should Watch: individuals, team leaders, and learning & development coordinators who are looking to better understand the options to increase professional capabilities in Python for scientific and engineering applications

Enthought’s Python for Scientists & Engineers training course is designed to accelerate the development of skill and confidence in addressing the technical challenges faced in scientific and analytic computing using some of Python’s core capabilities and tools, including: the standard Python language, core tools for science, engineering, and analysis, and tools for crafting well-organized and robust code, debugging, profiling performance, interfacing with other languages like C and C++, and adding graphical user interfaces (GUIs) to your applications.

What: A guided walkthrough and Q&A about Enthought’s technical training course Python for Data Science and Machine Learning” with VP of Training Solutions, Dr. Michael Connell

Who Should Watch: individuals, team leaders, and learning & development coordinators who are looking to better understand the options to increase professional capabilities in Python for data science and machine learning applications

Enthought’s Python for Data Science training course is designed to accelerate the development of skill and confidence in using Python’s core data science tools — including the standard Python language, the fast array programming package NumPy, and the Pandas data analysis package, as well as tools for database access (DBAPI2, SQLAlchemy), machine learning (scikit-learn), and visual exploration (Matplotlib, Seaborn).

Enthought’s Pandas Mastery Workshop is designed to accelerate the development of skill and confidence with Python’s Pandas data analysis package — in just three days, you’ll look like an old pro! This course was created ground up by our training experts based on insights from the science of human learning, as well as what we’ve learned from over a decade of extensive practical experience of teaching thousands of scientists, engineers, and analysts to use Python effectively in their everyday work.

In this webinar, we give you the key information and insight you need to evaluate whether the Pandas Mastery Workshop is the right solution to advance your data analysis skills in Python, including:

  • Who will benefit most from the course
  • A guided tour through the course topics
  • What skills you’ll take away from the course, how the instructional design supports that
  • What the experience is like, and why it is different from other training alternatives (with a sneak peek at actual course materials)
  • What previous workshop attendees say about the course

What: Presentation and Q&A with Dr. Michael Connell, VP, Enthought Training Solutions

Who Should Watch: Anyone who wants to develop proficiency in Python for scientific, engineering, analytic, quantitative, or data science applications, including team leaders considering Python training for a group, learning and development coordinators supporting technical teams, or individuals who want to develop their Python skills for professional applications

Python is an uniquely flexible language – it can be used for everything from software engineering (writing applications) to web app development, system administration to “scientific computing” — which includes scientific analysis, engineering, modeling, data analysis, data science, and the like.

In this webinar, we’ll give you the information you need to decide whether Enthought’s Python training is the right solution for your or your team’s unique situation, helping answer questions.

A Tour of Enthought’s Latest Enterprise Python Solutions

When: Thursday, July 20, 2017, 11-11:45 AM CT (Live webcast)

What: A comprehensive overview and live demonstration of Enthought’s latest tools for Python for the enterprise with Enthought’s Chief Technical & Engineering Officer, Didrik Pinte

Who Should Attend: Python users (or those supporting Python users) who are looking for a universal solution set that is reliable and “just works”; scientists, engineers, and data science teams trying to answer the question “how can I more easily build and deploy my applications”; organizations looking for an alternative to MATLAB that is cost-effective, robust, and powerful

Built on 15 years of experience of Python packaging and deployment for Fortune 500 companies, the NEW Enthought Deployment Server provides enterprise-grade tools groups and organizations using Python need, including:

  1. Secure, onsite access to a private copy of the proven 450+ package Enthought Python Distribution
  2. Centralized management and control of packages and Python installations
  3. Private repositories for sharing and deployment of proprietary Python packages
  4. Support for the software development workflow with Continuous Integration and development, testing, and production repositories

In this webinar, Enthought’s product team demonstrates the key features of the Enthought Deployment Server and how it can take the pain out of Python deployment and management at your organization.

Whether you are a data scientist, quantitative analyst, or an engineer, or if you are evaluating consumer purchase behavior, stock portfolios, or design simulation results, your data analysis workflow probably looks a lot like this:

Acquire > Wrangle > Analyze and Model > Share and Refine > Publish

The problem is that often 50 to 80 percent of time is spent wading through the tedium of the first two steps – acquiring and wrangling data – before even getting to the real work of analysis and insight. (See The New York Times, For Big-Data Scientists, ‘Janitor Work’ Is Key Hurdle to Insights)

In this webinar we’ll demonstrate how the new Canopy Data Import Tool can significantly reduce the time you spend on data analysis “dirty work,” by helping you:

  • Load various data file types and URLs containing embedded tables into Pandas DataFrames
  • Perform common data munging tasks that improve raw data
  • Handle complicated and/or messy data
  • Extend the work done with the tool to other data files

See how PyXLL makes it easy to write Excel add-ins to leverage the capabilities of Python and the simplicity of the presentation of Excel.

PyXLL allows organizations to seamlessly deploy Python algorithms and models to all their Excel users. No more uncontrolled proliferation of VBA-based spreadsheet versions, no more tossing functions over the wall to get re-implemented, no more need to deploy multiple specialized applications in place of Excel-based computations. Technical teams and developers can use the full power of Python, and end users can access the results in their familiar Excel environment.

In this webinar we demonstrate how to:

  • Use Python functions to implement financial models in Excel
  • Reduce risk by storing code in version control instead of embedded in Excel files
  • Develop functions with PyXLL and Python simply, and its comparison to VBA

See how the Python for Excel (PyXLL) add-in helps solve data analysis challenges with advanced Python tools and analytic engines.

Extending the native analytic capabilities of Excel or implementing Excel-backend analytics on a cluster or in the cloud with VBA, C++, and other legacy languages is challenging and time-consuming. Python’s elegant syntax and extensive ecosystem of numeric and analytic packages can greatly simplify the development of advanced analytic tools and cluster/cloud-based backend capabilities in Excel.

In this webinar we demonstrate how to:

  1. Implement Excel backend parallel computations on local clusters or cloud-based platforms such as Microsoft Azure with Python
  2. Use Python functions to implement advanced, interactive graphical analytic tools in Excel
  3. Reduce risk by storing code in GitHub version control instead of embedded VBA in Excel files

What: Presentation, demo, and Q&A with Collin Draughon, Software Product Manager, National Instruments, and Andrew Collette, Scientific Software Developer, Enthought

Who Should Watch:

  • Engineers and managers interested in extending LabVIEW with Python’s ecosystem
  • People who need to easily share and deploy software within their organization
  • Current LabVIEW users who are curious what Python brings to the table
  • Current Python users in organizations where LabVIEW is used

Engineers and scientists all over the world are using Python and LabVIEW to solve hard problems in manufacturing and test automation, by taking advantage of the vast ecosystem of Python software.  But going from an engineer’s proof-of-concept to a stable, production-ready version of Python, smoothly integrated with LabVIEW, has long been elusive.

LabVIEW is a software platform made by National Instruments, used widely in industries such as semiconductors, telecommunications, aerospace, manufacturing, electronics, and automotive for test and measurement applications. In August 2016, Enthought released the Python Integration Toolkit for LabVIEW, which is a “bridge” between the LabVIEW and Python environment.

In this webinar, we’ll demonstrate:

  1. How the new Python Integration Toolkit for LabVIEW from Enthought seamlessly brings the power of the Python ecosystem of scientific and engineering tools to LabVIEW
  2. Examples of how you can extend LabVIEW with Python, including using Python for signal and image processing, cloud computing, web dashboards, machine learning, and more