This is an introductory class for learning Python for students in science courses.
Find the lectures, labs, assignments and other teaching tools for this course. This class is taught in a college (cegep) context but may be used fairly in other contexts. This work is released under Creative Commons CC BY-NC-SA 4.0.
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This class is divided into two parts: computer science and physics. The Computer Science part of the course will introduce the student to the fundamentals of programming used to write applications that encompass building blocks (sequential, selection, and repetitive control structures) used in program construction. The student will learn how to analyze problems, and then design and implement both numerical and non-numerical (searching and sorting) algorithms to solve the problems.
The Physics part of the course will revisit material and problems that students will have already seen in previous science courses but will do so in a way that begins to consider their true complexity. Students will analyze complex problems, develop mathematical models and then solve the relevant equations using appropriate numerical methods with the aid of programming techniques developed in the Computer Science component. The Physics component focuses on modeling, and problems will be taken from a wide range of engineering and scientific fields.
This repository does not contain the physics aspects of this class.
VSCode, Jupyter Notebook, Python
Upon successful completion of this course, the student will be able to: • Solve problems using methods proper to science • Demonstrate consistency and rigor in problem-solving • Justify, orally and in writing, the approach used to solve the problem • Apply a software development method to solve engineering and scientific problems • Solve scientific problems that require the evaluation of mathematical equations • Evaluate and use packages from software libraries to solve engineering and scientific problems • Validate and assess the solution to the problem • Solve problems using a method proper to science. • Apply techniques of experimentation or validation specific to science.