Lectures related to Matplotlib

Data Science Essentials: Pandas, Numpy, Matplotlib

Introduces Pandas, Numpy, and Matplotlib for data analysis and visualization in Python.

Offers a comprehensive introduction to Data Science, covering Python, Numpy, Pandas, Matplotlib, and Scikit-learn, with a focus on practical exercises and collaborative work.

Wind Power Production Analysis

Covers wind power production analysis using Python and matplotlib.

Python Complement: Numpy, Scipy, Matplotlib

Covers advanced Python topics like numpy operations, scipy linear algebra, and matplotlib for creating figures.

Matplotlib: Basic Graph Creation and Practical Exercise

Introduces the basics of creating graphs with Matplotlib and includes practical exercises for students.

Data Science: Python for Engineers - Part II

Explores data wrangling, numerical data handling, and scientific visualization using Python for engineers.

Introduction to Jupyter Notebook

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

Numerical Methods: Stopping Criteria, SciPy, and Matplotlib

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

Transition Python to Matlab/Octave

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

Introduction to Python Programming

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

Introduction to Matplotlib

Introduces matplotlib for 2D graphic representation and data visualization with numpy.

NumPy Arrays and Graphical Representations: Introduction

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

Matplotlib: Overview

Covers a wide range of topics in Matplotlib, including simple plots, subplots, and various types of charts.

Introduction to NumPy: Basics of Scientific Computing

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

Introduction to Numerical Methods in Scientific Computing

Covers elementary numerical methods and algorithmic thinking using Python for scientific computing.

Introduction to NumPy and Matplotlib for Scientific Computing

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

Numerical Methods: Bisection and Multidimensional Arrays

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

Nonlinear Equation Resolution: Introduction to Bisection Method

Introduces the bisection method for resolving nonlinear equations using numerical techniques and Python programming.

Numerical Integration: Introduction to SciPy and Matplotlib

Covers numerical integration techniques using SciPy and Matplotlib for visualizing functions and approximating integrals.

Algorithmic Complexity: Visualization and Analysis

Explores algorithmic complexity, visualization of functions, and algorithm efficiency analysis using Python.