Introduction to Computational Science: Course Overview and Objectives

Related lectures (26)

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Covers advanced Python programming concepts, including data structures and functions.

Covers programming concepts for engineers, including interactive quizzes and practical applications to enhance learning and engagement.

Discusses numerical methods, focusing on stopping criteria, SciPy for optimization, and data visualization with Matplotlib.

Covers LabVIEW programming for data acquisition and display, including LabVIEW sound and visualization.

Introduces NumPy, focusing on array creation, manipulation, and its advantages for scientific computing.

Discusses the bisection method for solving nonlinear equations and its implementation using Python with NumPy and Matplotlib.

Covers programming basics for engineers, emphasizing MATLAB, C, and LabVIEW tools for project development.

Introduces Python programming concepts, focusing on dictionaries and classes, including their definitions, usage, and practical examples.

Introduces NumPy and Matplotlib, essential tools for scientific computing and data visualization in Python.

Covers NumPy arrays and their graphical representations using Matplotlib, focusing on array creation, manipulation, and visualization techniques.