Introduction to NumPy and Matplotlib for Scientific Computing

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (28)

Page 1 of 3

Introduction to NumPy: Basics of Scientific Computing

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

Numerical Methods: Stopping Criteria, SciPy, and Matplotlib

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

Numerical Methods: Bisection and Multidimensional Arrays

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

File Management and Exception Handling in Python

Focuses on file management and exception handling in Python programming.

Air Pollution Data Analysis

Covers the analysis of air pollution data, focusing on R basics, visualizing time series, and creating summaries of pollutant concentrations.

Vectorization in Python: Efficient Computation with Numpy

Covers vectorization in Python using Numpy for efficient scientific computing, emphasizing the benefits of avoiding for loops and demonstrating practical applications.

Python Programming: Lists and Functions Overview

Introduces Python programming concepts, focusing on lists, functions, and their applications in problem-solving.

Python Programming: Data Structures and Functions

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

Data Science for Engineers: Part 2

Explores data manipulation, exploration, and visualization in data science projects using Python.