Python For Science & Engineering - The Bootcamp
Published 8/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 17.80 GB | Duration: 13h 7m

Master applied Python for Science and Engineering - Essential Skills and Hands-On Projects!

What you'll learn

Master Python from Fundamentals to Advanced Concepts.

Solve Engineering Problems through Analysis and Modelling.

Manipulate, Analyse, and Visualize data efficiently.

Excel in Critical Libraries for Scientifical Computation.

Unlock the Power of Symbolic Mathematics with SymPy.

Develop Professional Data Visualization Skills with Matplotlib.

Utilize Python for Efficient Numerical Analysis.

Requirements

No programming Experience needed. Everything will be shared along the way.

Open to learners from all backgrounds and Disciplines.

No paid software required, just a computer having access to internet (and a little dedication! )

Description

It is finally time you unlock your full professional potential with Python! •In this comprehensive bootcamp, The Python for Science and Engineering you'll embark on a structured and concise learning journey designed to equip you with practical Python skills tailored specifically for science and engineering applications.Who is this course thought by? •Guided by an instructor who has 5+ years experience in Python and Engineering that understands the challenges of being a student, the course covers fundamental and advanced Python concepts, presented in a clear, logical and intuitive manner. What will this course teach you? •You'll learn to navigate through powerful and crucial libraries in the scientifical realm:Handle Multi-dimensional Arrays and perform Numerical Computation with NumPy.Develop Professional Data Visualization Skills with Matplotlib.Unlock the Power of Symbolic Mathematics with SymPy.But also learn how to master Python when applied in Engineering, by learning how to:Master the fundamentals of python programming while adopting good writing habits.Define and Model, Solve and Simulate a Differential Equation that governs a physical phenomena.The course is crafted to avoid unnecessary complexity, offering focused content that is both current and relevant. With an optimal course length, you'll efficiently gain the skills needed to boost your resume and excel in your academic or professional pursuits.Enroll today to unlock your full potential and prepare yourself for success in the STEM field! •

Overview

Section 1: Welcome to the Python for Engineers Course!

Lecture 1 Course Introduction - An Overview!

Lecture 2 Installing Python and Visual Studio Code

Lecture 3 Installing Jupyter Notebook

Section 2: Fundamentals of Python Programming

Lecture 4 Variables - Numerical Variables (Int, Float, Complex)

Lecture 5 Variables - Textual Variables (Strings)

Lecture 6 Variables - Booleans

Lecture 7 Variables - Additional Features

Lecture 8 User I/O - User Output

Lecture 9 User I/O - Formated Output

Lecture 10 User I/O - User Input

Lecture 11 Operators & Expressions - Arithmetical Operations

Lecture 12 Operators & Expressions - Comparaison Operations

Lecture 13 Operators & Expressions - Logical Operations

Lecture 14 Operators & Expressions - Assignment Operations

Lecture 15 Conditional Statements (If, Elif, Else)

Lecture 16 Loop Statements - For Loop

Lecture 17 Loop Statements - While Loop

Lecture 18 Control Flow Statement (break, continue, pass)

Section 3: Functions & Modular Programming

Lecture 19 Functions - Overview

Lecture 20 Functions - Proper Documentation

Lecture 21 Modular programming - A practical example with a Planets!

Lecture 22 Modular Programming - Built-In Functions

Lecture 23 Create Mathematical functions in Python

Lecture 24 Additionnal Features on Functions

Section 4: Data Structures

Lecture 25 Lists - Overview

Lecture 26 Lists - A practical example with the BMI !

Lecture 27 Lists - Multi-Dimensional Lists (1D, 2D and n-D)

Lecture 28 Tuples - Overview

Lecture 29 Strings - Overview

Lecture 30 Dictionaries - Overview

Lecture 31 Dictionaries - A practical example with Sensors!

Lecture 32 Sets - Overview

Lecture 33 List Comprehension in Python

Section 5: Object Oriented Programming

Lecture 34 Classes & Objects - Overview

Lecture 35 Classes & Objects - A practical example with Car Accelerations!

Lecture 36 Classes & Objects - Class Inheritance

Section 6: Errors and Exceptions

Lecture 37 Errors and Exceptions in Python

Section 7: File Handling and I/O Operations

Lecture 38 File Operations - Overview & File Writing

Lecture 39 File Operations - File Reading

Lecture 40 File Operations - File Appending

Lecture 41 File Operations - Additional Informations & the 'with' keyword

Lecture 42 Storing & Retrieving Data - From a Text file (.txt)

Lecture 43 Storing & Retrieving Data - From a JSON file (.json)

Lecture 44 Storing & Retrieving Data - From a Pickle file (.pickle)

Lecture 45 Handling CSV files (.csv) in Python

Section 8: Handling Multi-Dimensional Arrays with NumPy

Lecture 46 Getting Started with NumPy!

Lecture 47 Creating your first NumPy-Arrays

Lecture 48 Indexing and Slicing NumPy-Arrays

Lecture 49 Operating NumPy-Arrays

Lecture 50 Managing NumPy-Arrays

Section 9: Data Visualization & Plotting with Matplotlib

Lecture 51 Getting Started with Matplotlib!

Lecture 52 Make Professional Looking Plots & Graphs

Lecture 53 Draw Multiple Plots & Layouts

Lecture 54 Plotting 3D Mathematical Functions

Section 10: Symbolic Mathematics & Expressions with SymPy

Lecture 55 Write your First Symbolic Expression with SymPy!

Lecture 56 Creating Symbolic Variables

Lecture 57 Solving Symbolic Equations

Lecture 58 Advanced Symbolic Expressions

Lecture 59 Creating & Handling Symbolic Matrices

Section 11: Solving Differential Equations (DE) with Python

Lecture 60 A Theoretical Overview on DE

Lecture 61 Solving Differential Equations with Sympy!

Lecture 62 Solving Differential Equations numerically using `solve_ivp`!

Beginners who have never programmed before and seek an engaging adventure.,Regular Programmers transitioning to Python.,Intermediate Python Programmers seeking to elevate their skills by applying them in the scientific field.,Career-Driven Engineers aiming to enhance their opportunities.,PhD students and researchers aiming to apply Python in their research field, bringing illumination to the unknown.



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