Python For Calculus And Exact Sciences

Learn Python Fundamentals, Numpy and Sympy. Applied to mathematics, engineering, sciences.

What you'll learn
Watch the very first lesson of the course in other to see our content
Python native data structures
Repetitions loops and conditionals
Create functions and modules
Symbolic maht with Sympy
Matrices and vectors with Sympy
Solve linear and non-linear systems, differential equations with Sympy
Exercises solved in lessons
Numpy array data structures
Numpy array operations
Numpy applied to math (eigenvalues, eigenvectors, linear systems)
Requirements
The student don't need to know Python. But the familiarity with exact sciences is necessary.
Description
In this course, you will learn the Python fundamentals and Python applied to math and exact sciences.-Learn Python native data structures;-Learn repetitions loops and conditionals-Learn create functions and modules;-Work with symbolic maht;-Work with matrices and vectors;-Solve linear and non-linear systems, differential equations;-Exercises solved in lessons;-Learn Numpy array data structures;-Numpy array operations;-Numpy applied to math (eigenvalues, eigenvectors, linear systems);-Watch the very first lesson of the course in other to see our content.= = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = =

Overview

Section 1: Introduction

Lecture 1 0.1 - Course learning path

Lecture 2 0.2 Instalation

Lecture 3 0.3 Jupyter notebook IDE

Lecture 4 1.1 Breaf Introduction to Python

Lecture 5 1.2 Numbers

Lecture 6 1.3 String

Lecture 7 1.4 Lists

Lecture 8 1.5 Similarities between lists and strings

Lecture 9 1.6 Boolean Values

Lecture 10 2.1 If

Lecture 11 2.2 Loop for

Lecture 12 2.3 Loop while

Lecture 13 3.1 Methods to lists

Lecture 14 3.2 Tuples, sets and dictionaries

Lecture 15 3.3 Methods to dictionaries

Lecture 16 4.1 Functions

Lecture 17 4.2 Modules

Lecture 18 4.3 Creating a module

Section 2: Mathematics with Sympy

Lecture 19 5.2 Symbols

Lecture 20 5.3 Matrix

Lecture 21 5.4 Calculus functions

Lecture 22 5.5 Solvers

Lecture 23 5.6 Exercise 1

Lecture 24 5.7 Exercise 2

Lecture 25 5.8 Exercise 3

Lecture 26 5.9 Exercise 4

Lecture 27 5.10 Exercise 5

Lecture 28 5.11 Exercise 6

Section 3: Mathematics and data manipulation with Numpy

Lecture 29 6.1 Arrays

Lecture 30 6.2 Array operations

Lecture 31 6.3 Array creation functions

Lecture 32 6.4 Copy method

Lecture 33 6.5 Mathematical methods for arrays

Lecture 34 6.6 Indexing and slicing

Lecture 35 6.7 Operation with vectors and matrices in Numpy

Lecture 36 6.8 Eigenvalue and eigenvector

Lecture 37 6.9 Solver for linear systems

Section 4: Bonus Section (Download the JUPYTER NOTEBOOKS)

Lecture 38 Bonus Lecture (Download)

Engineers, physicists, mathematicians, chemists, students of exact sciences, economists, etc.