About this ebook
The second edition of this introductory text includes an expanded treatment of collisions, agent-based models, and insight into underlying system dynamics. Lab assignments are accessible and carefully sequenced for maximum impact. Students are able to write their own code in building solutions and Python is used to minimize any language barrier for beginners.
Problems involving visualization are emphasized throughout with interactive graphics, image files, and plots of generated data. This text aims to establish a core learning experience around which any number of other learning objectives could be included. The text is presented in eight chapters where each chapter contains three problems and each problem develops five specific lab assignments, plus additional questions and discussion. This approach seeks to leverage the immediate feedback provided by the computer to help students as they work toward writing code creatively.
All labs will scale to available hardware and free software could be used for the entire course, if desired. Lab assignments have been used since 2011 at the #1 ranked U.S. high school. It is an ideal textbook for high school courses that prepare students for advanced placement tests.
Problems involving visualization are emphasized throughout with interactive graphics, image files, and plots of generated data. This text aims to establish a core learning experience around which any number of other learning objectives could be included. The text is presented in eight chapters where each chapter contains three problems and each problem develops five specific lab assignments, plus additional questions and discussion. This approach seeks to leverage the immediate feedback provided by the computer to help students as they work toward writing code creatively.
All labs will scale to available hardware and free software could be used for the entire course, if desired. Lab assignments have been used since 2011 at the #1 ranked U.S. high school. It is an ideal textbook for high school courses that prepare students for advanced placement tests.
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About the author
Dr. Shane Torbert holds a B.A. in Mathematics from the University of Virginia, and an M.Ed., M.S., and Ph.D. from George Mason University where he conducted research in the Center for Computational Fluid Dynamics. He has been at Thomas Jefferson High School for Science and Technology since the summer of 1999 and full-time since 2001, having taught all levels of Math and Computer Science. Since 2004 he has been lab director of TJ's Computer Systems Lab. He was a consultant for the College Board in Computer Science, served as a grant review panelist for the National Science Foundation, and has given presentations at the local, regional, state, and national levels including JOSTI, NCTM, NCSSS, and CSTA. He has run workshops for Computer Science teachers sponsored by both Google and ACM. Introductory materials developed by Dr. Torbert are used continually by beginning students each year.
At the advanced level, his post-AP elective courses attract talented high school Computer Science students with a research interest in algorithms, optimization, agent-based models, search, parallel programming, computer vision, and machine learning. Students from the lab have consistently placed well in Science Fair and contests including Intel, Siemens, and ISEF. In addition, each fall Dr. Torbert coordinates the research efforts of TJ students working at external sites for a substantial portion of their school day, and directly alongside professional mentors from academia and industry.
At the advanced level, his post-AP elective courses attract talented high school Computer Science students with a research interest in algorithms, optimization, agent-based models, search, parallel programming, computer vision, and machine learning. Students from the lab have consistently placed well in Science Fair and contests including Intel, Siemens, and ISEF. In addition, each fall Dr. Torbert coordinates the research efforts of TJ students working at external sites for a substantial portion of their school day, and directly alongside professional mentors from academia and industry.
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