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Related lectures (32)

Introduction to Python Programming

Introduces Python programming basics using Anaconda and JupyterLab, with practical exercises on syntax and libraries.

Python/NumPy Primer

Introduces Python basics and NumPy for scientific computing, covering data types, functions, arrays, indexing, and common operations.

Introduction to Jupyter Notebook

Introduces setting up the environment, Git basics, and Jupyter Notebook features with practical examples.

Numpy: Memory Comparison & Performance

Compares memory usage and performance between Python lists and Numpy Arrays, demonstrating Numpy's significant speed improvement.

Scientific Computing with SciPy Constants and Curve Fitting

Covers SciPy constants, curve fitting, and zero finding methods for scientific computing.

NumPy Arrays and Graphical Representations: Introduction

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

Transition Python to Matlab/Octave

Covers the transition from Python to Matlab and Octave for scientific computing.

Computational Modeling of Systems

Covers computational modeling, project rules, collaboration, and site remediation projects.

Digital Integration with SciPy: Quad Function

Covers digital integration using the quad function from SciPy for efficient calculation of definite integrals and resolution of first-order ordinary differential equations.

Numerical Methods: Bisection and Multidimensional Arrays

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

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Parametric Signal Models: Matlab Practice

Covers parametric signal models and practical Matlab applications for Markov chains and AutoRegressive processes.

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