Training
Specially-Designed Courses For A Digital Future
Public Classes
Optimized classes for learning scientific computing, data analysis, and machine learning
Ideal for individuals looking for professional growth
Duration: 3-5 Day courses
Corporate Training
Group sessions designed for teams co-working innovative solutions
Develop a shared language that fosters adoption of new solutions, collaborative design and problem-solving
Duration: 3-5 Day Courses
Applied Computing Program
Comprehensive, integrated program designed to transform your workflows
Partner closely with Enthought experts to develop new, lab-ready skills
Duration: 10-week part-time consulting & apprenticeship hybrid program
Working with the world's best companies
Why Enthought?
Transformative Results Depends on Digital Skills
Creative disruption in digital transformation depends on digital skills. There is significant benefit from implementing technology, for example in how data is organized, accessed and shared, or specific tools for specific workflows. However, these advances, while accelerating workflows, do not fundamentally change anything in the organization or how it runs. The effects are local.
Upcoming Courses
Python for Scientists and Engineers
This 40 hour class combines our Python Foundations with content relevant to scientists and engineers interested in using Python for their day-to-day computational tasks.
Python Foundations
This 20 hour intensive Python training class provides people with prior coding experience practical, hands-on experience and foundational working knowledge of Python for data analysis, science, engineering, and other technical applications.
Pandas Mastery Workshop
Course Details Syllabus Course Details Course Details This intensive 22.5 hours class is designed for students to gain proficiency using the Python Pandas library for…
Course Comparison
| Course | Day 1 | Day 2 | Day 3 | Day 4 | Day 5 |
|---|---|---|---|---|---|
| Python Foundations | Python & OOP | NumPy & Matplotlib |
Pandas Essentials | ||
| Python for Scientists and Engineers | Python Foundations | Python Foundations | Python Foundations | Software Craftsmanship | GUIS |
| Python for Machine Learning | Python Foundations | Python Foundations | Python Foundations | Machine Learning & Visualization | Machine Learning & Visualization |
| Python for Data Science | Python Foundations | Python Foundations | Python Foundations | DBs | Machine Learning & Visualization |
| Python for Data Analysis | Python Foundations | Python Foundations | Python Foundations | Advanced Pandas | Pandas Projects |
| Machine Learning Master Workshop | Machine Learning & Visualization | ML Projects | |||
| Pandas Mastery Workshop | Pandas Essentials | Advanced Pandas | Pandas Projects | ||
| Practical Deep Learning | Intro to Neural Networks | Training Neural Networks | Evaluating Neural Networks | ||
Training Resources
This document will guide you through the transition from MATLAB® to Python. The guide is specifically designed for long-time MATLAB® users who want to migrate to Python, either partially or entirely.
A comprehensive, visual guide to manipulating data with Pandas, from understanding usage patterns to combining and reshaping dataframes. Take your Pandas skills to the next level.
Scikit-learn is a simple and efficient tool for predictive data analysis—accessible to everybody, and reusable in various contexts. Hone your scikit-learn skills with our cheat sheets, specially designed by our in-house experts to help you grow your machine learning skills.
Explore All Resources
New Application Transforms Chip Communication Subsystem Testing
Freescale Semiconductor needed to transform their testing processes. Freescale’s High Speed Signal Integrity (HSSI) group is responsible for testing the electrical characteristics of the communications…
Active Learning Improves Polymer Formulation Scale-Up Efficiency
A Specialty Chemical Company Wanted to Remove the Bottleneck from their Polymer Scale-Up Process Many of the high tech products we interact with on a…
10X Efficiency Gain and Improved Classification Using Deep Learning
Use AI techniques to efficiently extract mineralogy and grain size statistics from thin sections Thin sections provide the closest examination of in situ rock properties,…
Accelerating Consumer Products Reformulation with Machine Learning
Consumer products need to be reformulated continually in order to adapt to new market forces, government regulations, and supply chain limitations. In some markets, sustainability…
The Journey to Digital-centric Chemicals and Materials Laboratories
Digital technologies and the innovation they enable are steadily transforming chemicals and materials labs. This webinar proposes five ‘levels’ of lab digital capability, providing managers…
3 ways to identify digital transformation opportunities in your R&D lab
Is your R&D lab digitally mature? Do you use data and code to create value at every step of your R&D program? This webinar will…
Integrating Weather and Renewable Energy Sources Data for 2.5 Million Viewers
Educating and developing a culture of responsible energy consumption The EnergizAIR project is presented both to show the innovative technology and methodology to solve the…
Manual Processes for CSEM Replaced by Computational Tools and Strategies
Shell needed a way to effectively visualize a new scientific measurement Controlled-Source Electromagnetic sounding (CSEM) is a new tool for marine oil exploration. Sensitive electric…
Predicting Pore Pressure From Very Limited Data Sets
ConocoPhilips wanted to make pore pressure predictions with limited data sets Logs from two wells and a limited set of 2D and 3D seismic were…
AI/Machine Learning Techniques Quantify AVA Seismic Analysis Uncertainty
Help ConocoPhillips apply advanced AI techniques to Amplitude Versus Angle (AVA), seismic analysis Amplitude Versus Angle (AVA), seismic analysis is a well-established oil exploration tool….
Our Experts
Alexandre Chabot-Leclerc
Vice President, Digital Transformation Solutions
Tim Diller
Director, Digital Transformation Services
Mark Dickinson
Director, Software Architecture
Eric Olsen
Director, Training Solutions
Glen Granzow
Scientific Software Technical Trainer
Sogo Shiozawa
Scientific Software Developer
Kuya Takami
Senior DTX Services Consultant and Instructor