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✅ Here’s what you’ll learn:
The labs on this website are designed to accompany the Foundations of Applied Mathematics textbook series. They provide hands-on experience with key mathematical and computational concepts, connecting theory to practical, real-world applications.
Note for public users: If you are not an enrolled ACME student, please see the Public Use page for information on setup, access, and usage guidelines.
What You’ll Learn
Core Technical Skills
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Python libraries: NumPy, SciPy, Matplotlib, pandas, and other scientific packages
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Core mathematical algorithms, numerical methods, and modeling techniques
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Coding best practices, including unit testing and code optimization
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Array broadcasting and efficient numerical computation
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Data visualization and analysis techniques
Real-World Applications
In these labs, you will solve problems such as:
Site Structure
At the top of each page, you’ll find several useful tools:
Fig. 1 Create an issue on GitHub if you find an error in the labs.
Fig. 2 Download page source, PDF, lab spec/test/data files, and requirements.
Fig. 3 Toggle fullscreen mode.
Fig. 4 Toggle light/dark mode.
The labs are organized by volume, accessible via the tabs on the left. Each lab volume corresponds to a theory volume in the textbook series. Other key sections include:
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Getting Started — A roadmap for beginning work on the labs and setting up your computer.
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Environment Setup — Step-by-step instructions to configure your system for the ACME labs.
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Student Resources — Tips and guides for ACME students, from code quality to plotting with Matplotlib.
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Supplemental Labs — Optional labs introducing additional concepts.
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References — Sources and references used in lab development.
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ACME Contributors — Individuals who have contributed to the development of these materials.
Lab Structure
Each lab is self-contained and includes:
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Clear learning objectives
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Theoretical background and practical examples
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Exercises to apply what you’ve learned
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Additional materials for deeper exploration
These labs are designed to bridge the gap between theory and implementation, strengthening both mathematical intuition and programming skills.
Personal Projects
The labs are also an excellent springboard for personal projects, which can be valuable for internships or job applications.
Projects showcase your initiative, creativity, and technical ability beyond coursework.
For example, one student expanded the Markov Chains lab into a Twitter bot called TSwizzlebot.
Your project can be as creative as you like, just ensure you extend or modify the lab in a way that makes it uniquely yours. It should no longer look like a homework submission, but rather an original project.
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