- From 17 March–7 April 2026 and 5 May 2026 (8 Classes, 32 Total Hours)
- Tuesdays and Thursdays from 12–4 p.m. Eastern Time (all sessions will be recorded and available for replay; course material will be available for download)
- The final eighth session on Tuesday, 5 May is for reviews, questions and interactive code development (following an intermission for material/code reflection).
- All students will receive an AIAA Certificate of Completion at the end of the course
OVERVIEW
Proficient programming takes more than familiarity with the syntax of any programming language. One needs awareness of existing standard and third-party libraries to avoid re-implementing the wheel. Another vital skill is the art of posing problems in ways to exploit the features and capabilities of the language. This last aspect of programming is rarely covered in courses or books.
In this popular course, taught for years at Boeing by the instructor, the Python programming language and its rich ecosystem are introduced. It will focus on engineering applications with practice examples of implementing elegant and efficient algorithms.
The course introduces machine learning concepts, capabilities, and limitations applied to engineering-type problems using the scikit-learn package.
The final eighth session is for reviews, questions and interactive code development. There will be a two-week intermission before the final eighth session to allow students to review the material and raise questions. Students are encouraged to bring up their own programming projects for class discussion and collaborative implementation during this session. The best exercises are those of students’ personal interests.
It is recommended that students use their personal computer for the class with Python and additional packages installed. Miniforge is one easy way to install the many packages and dependencies in single installer.
LEARNING OBJECTIVES
- Understand essential concepts to use Python effectively
- Understand the NumPy add-on library and its practical applications to numerical computations
- Introductions to libraries valuable for aerospace engineering applications
- Accessing documentation
- Software quality assurance and documentation generation
- Machine learning with the Scikit-Learn package
- Detailed outline below
AUDIENCE: This course is intended for any engineer, scientist, researcher, or manager with programming needs and/or interests. No prior programming experience is required for the course.
COURSE FEES (Sign-In To Register)
- AIAA Member Price: $945 USD
- AIAA Student Member Price: $595 USD
- Non-Member Price: $1,145 USD
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Fundamentals of Python
- Python native data types
- Python syntax
- Loops
- Branching
- Exceptions
- Packages and Modules
- Name spaces
- Examples and applications
NumPy and matplotlib Libraries
- NumbPy add-on
- matplotlib
- Examples and applications
Survey of other libraries useful for aerospace engineering applications such as:
- The Jupyter Notebook environment.
- The matplotlib plotting package.
- The standard libraries, in particular Regular expression.
- The scientific/engineering routines in SciPy.
- The Pandas data analytics package.
- Integration with other languages.
- Machine learning with scikit-learn.
INSTRUCTOR
Dr. Peng Ho has been involved in engineering software development and maintenance for his entire career and is a Boeing Designated Expert on engineering software development. Over fifteen years ago, he selected Python as his primary programming language and developed a dozen projects using Python; as well as using it for data analysis and machine learning. He actively coaches engineers at Boeing in Python and effective use of the language and ecosystem. He strongly advocates for software quality assurance and documentation best practices in engineering software.TESTIMONIALS
"This class is filled with extremely helpful information. The exceptionally well-prepared course materials were a huge benefit."
"I thought the class was great. I really liked the examples of Propulsion issues that were resolved using Python scripts, and the instructor had a very thorough knowledge of the subject matter."
"The class covered a wide range of content that went over just about everything someone would need to know to make a helpful python program."
"The example codes are very easy to follow and a fantastic resource to catch up on when needed. Perfect notes!"
"Being built on Jupyter was amazing to set up the examples as well as challenge the students with interpretations, mods, and spin-offs. It was a great way to learn. And the way Dr. Ho built up his lessons and examples built concepts layered as nominal, sufficient, and complex solutions. So that you could learn from each level. It was an overall great experience."
"Great professor, great course!"
Classroom hours / CEUs: 32 classroom hours / 3.2 CEU/PDH
- The course lectures will be delivered via Zoom. You can test your connection here: https://zoom.us/test
- Miniforge: It is recommended that students use their personal computer for the class with Python and additional packages installed. Miniforge is one easy way to install the many packages and dependencies in single installer. Software installation instructions are provided.
- All sessions will be available on demand within 1-2 days of the lecture. Once available, you can stream the replay video anytime, 24/7. All slides will be available for download after each lecture.
- No part of these materials may be reproduced, distributed, or transmitted, except for course participants. All rights reserved.
- Between lectures, the instructors will be available via email for technical questions and comments.
Cancellation Policy: A refund less a $50.00 cancellation fee will be assessed for all cancellations made in writing prior to 7 days before the start of the event. After that time, no refunds will be provided.
Contact: Please contact Lisa Le or Customer Service if you have questions about the course or group discounts (for 5+ participants).