Python for Engineers and Scientists / basic to advanced

Python for Engineers and Scientists / basic to advanced, available at $54.99, has an average rating of 4.94, with 120 lectures, based on 9 reviews, and has 143 subscribers.

You will learn about Python Language (from basic to advanced) + a complete package of Python scientific libraries: Sympy, Numpy, Pandas, Matplotlib, Scipy. The student has access to ALL the CODES from the class. Both the language and the libraries are FREE! Useful topics for day-to-day tasks such as reading and writing files Basic Python topics such as installation, variables, methods, and loops Advanced topics such as object creation. Sympy: solving linear systems, nonlinear systems, differential equations. Exercises and challenges. Numpy: for data manipulation in multidimensional arrays. Pandas: for creating tables; pivot tables; filters; data visualization, and much more. Matplotlib: for creating charts and dashboards. Scipy: for mathematics and numerical methods This course is ideal for individuals who are Engineers or Data Analysts or Math or Physics students or Other active students: geologists, journalists, doctors, biomedical scientists, statisticians, data scientists, financial analysts. It is particularly useful for Engineers or Data Analysts or Math or Physics students or Other active students: geologists, journalists, doctors, biomedical scientists, statisticians, data scientists, financial analysts.

Enroll now: Python for Engineers and Scientists / basic to advanced

Summary

Title: Python for Engineers and Scientists / basic to advanced

Price: $54.99

Average Rating: 4.94

Number of Lectures: 120

Number of Published Lectures: 120

Number of Curriculum Items: 120

Number of Published Curriculum Objects: 120

Original Price: $29.99

Quality Status: approved

Status: Live

What You Will Learn

Python Language (from basic to advanced) + a complete package of Python scientific libraries: Sympy, Numpy, Pandas, Matplotlib, Scipy.
The student has access to ALL the CODES from the class.
Both the language and the libraries are FREE!
Useful topics for day-to-day tasks such as reading and writing files
Basic Python topics such as installation, variables, methods, and loops
Advanced topics such as object creation.
Sympy: solving linear systems, nonlinear systems, differential equations. Exercises and challenges.
Numpy: for data manipulation in multidimensional arrays.
Pandas: for creating tables; pivot tables; filters; data visualization, and much more.
Matplotlib: for creating charts and dashboards.
Scipy: for mathematics and numerical methods

Who Should Attend

Engineers
Data Analysts
Math or Physics students
Other active students: geologists, journalists, doctors, biomedical scientists, statisticians, data scientists, financial analysts.

Target Audiences

Engineers
Data Analysts
Math or Physics students
Other active students: geologists, journalists, doctors, biomedical scientists, statisticians, data scientists, financial analysts.

The goal of “Python for Engineers and Scientists” is to provide programming, mathematical, and graphical tools for professionals across various fields.

Why should you take this course?

Both Python and the scientific ecosystem libraries taught here are FREE and open-source tools. This makes it easier to adopt these tools in both workplace and academic settings.

Moreover, the language and its libraries have been growing worldwide with a super active community. I’ve observed this since 2015 when I did R&D internships at a nuclear energy company.

Don’t fall behind, my friend!

What do you gain by enrolling in this course?

This is the most comprehensive course with the best cost/benefit ratio on Python and its scientific ecosystem. In addition to around 15 hours of content, students have access to the Q&A forum, where we already have constructive interactions with all students and many questions and answers already addressed. You’ll also have access to all the materials/codes created during the class, all structured and organized!

What will I learn?

In general, the course content includes:

– Python Fundamentals: You’ll learn everything from installation to more advanced topics like object-oriented programming. Also, you’ll cover useful day-to-day topics like task automation.

– Sympy: You’ll master symbolic algebra manipulation, solving systems of equations, differential equations, and calculus functions. Additionally, there are plenty of exercises and challenges (proposed and solved). Sympy is a great substitute for Matlab.

– Numpy: You’ll dive deep into the powerful array structure of Numpy.

– Pandas: You’ll learn the best Excel replacement we have today. We’ll work on filters, pivot tables, graphs, and real data handling with Pandas.

– Matplotlib: You’ll gain an in-depth understanding of Matplotlib’s objects for creating charts and dashboards.

– Scipy: You’ll explore the “big boy” of computational mathematics in Python. We’ll cover linear algebra, integrals, and numerical solutions to ODEs, with exercises (proposed and solved).

I invite all of you to watch the introductory lesson where I showcase the learning structure of the course.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction – course overview

Lecture 2: Installation of Anaconda – common users

Lecture 3: Installation of pure Python – advanced users (pip install)

Lecture 4: Download the course lectures

Lecture 5: Execution – cmd and ipython basic execution

Lecture 6: Execution – Spyder

Lecture 7: Execution – Jupyter notebook and colab

Chapter 2: 2. Python Fundamentals

Lecture 1: 2.1 Built-in types

Lecture 2: 2.2 Basic math operations

Lecture 3: 2.3 Basic operations with strings (texts)

Lecture 4: 2.4 Boolean values (*bool*)

Lecture 5: 2.5 Collections

Chapter 3: 3. Control Flow

Lecture 1: 3.1 Conditional Structures

Lecture 2: 3.2 Loop Structures: **for**

Lecture 3: 3.3 Loop Structures: **while**

Lecture 4: 3.4 Boolean Operations

Lecture 5: 3.5 Other Ways to Generate Booleans: isinstance(), in, is

Lecture 6: 3.6 Iterables

Lecture 7: 3.7 enumerate()

Lecture 8: E3.1 – Exercise

Lecture 9: E3.2 – Exercise

Lecture 10: E3.3 – Exercise

Lecture 11: E3.4 – Exercise

Lecture 12: E3.5 – Exercise

Lecture 13: E3.6 – Exercise

Chapter 4: 4. Data Structures

Lecture 1: 4.1 Methods for Lists

Lecture 2: 4.2 List Comprehensions

Lecture 3: 4.3 List Indexing and Slicing

Lecture 4: 4.4 Methods for Tuples

Lecture 5: 4.5 Tuple Unpacking

Lecture 6: 4.6 Methods for Sets

Lecture 7: 4.7 Set Operations

Lecture 8: 4.8 Dictionary Methods

Lecture 9: 4.9 any and all

Lecture 10: 4.10 zip

Lecture 11: E4.1 – Exercise

Lecture 12: E4.2 – Exercise

Lecture 13: E4.3 – Exercise

Lecture 14: E4.4 – Exercise

Lecture 15: E4.5 – Exercise

Lecture 16: E4.6 – Exercise

Chapter 5: 5. Functions

Lecture 1: 5.1 Functions

Lecture 2: 5.2 Functions and Keyword (Named) Arguments

Lecture 3: 5.3 *args

Lecture 4: 5.4 **kwargs

Lecture 5: 5.5 Docstring

Lecture 6: 5.6 Lambda Functions

Lecture 7: E5.1 – Exercise

Lecture 8: E5.2 – Exercise

Lecture 9: E5.3 – Exercise

Chapter 6: 6. Object-Oriented Programming

Lecture 1: 6.1 Introduction to OOP (object-oriented programming)

Lecture 2: 6.2 Inheritance and Polymorphism

Lecture 3: The rest of the section is a placeholder

Chapter 7: 7. Texts and Files

Lecture 1: 7.1 String Operations

Lecture 2: 7.2 String Methods

Lecture 3: 7.3 String Formatting

Lecture 4: 7.4 Reading and Writing Files

Lecture 5: E7.1 – Exercise

Lecture 6: E7.2 – Exercise

Lecture 7: E7.3 – Exercise

Lecture 8: E7.4 – Exercise

Chapter 8: 8 Error handling

Lecture 1: 8.1 Try and Except Statements

Lecture 2: 8.2 Python's Built-in Exceptions

Lecture 3: 8.3 Try, Except Error

Lecture 4: 8.4 Custom Exceptions

Lecture 5: This section is a placeholder

Chapter 9: 10. Numpy

Lecture 1: 10.1 Arrays

Lecture 2: 10.2 Math functions

Lecture 3: 10.3 Array Creation

Lecture 4: 10.4 Basic Operations with Arrays

Lecture 5: 10.5 Numpy Memory Management

Lecture 6: 10.6 Statistical Methods for Arrays

Lecture 7: 10.7 Array Indexing and Slicing

Lecture 8: 10.8 Matrices in Numpy

Lecture 9: 10.9 Vectors

Lecture 10: E10.1 – Exercise

Lecture 11: E10.2 – Exercise

Lecture 12: E10.3 – Exercise

Lecture 13: E10.4 – Exercise

Chapter 10: 11. Pandas

Lecture 1: 11.1 Series

Lecture 2: 11.2 DataFrame

Lecture 3: 11.3 Basic Methods for DataFrames

Lecture 4: 11.4 Reading and Writing Files

Lecture 5: 11.5 Selecting Rows and Columns with loc and iloc

Lecture 6: 11.6 Filters

Lecture 7: 11.7 Data Cleaning – Preprocessing

Lecture 8: 11.8 Join Method

Lecture 9: 11.9 concat Function

Lecture 10: 11.10 Pivot Table

Lecture 11: E11.1 – Exercise

Instructors

Rafael Pereira da Silva, MSc.
Engenheiro de produto

Rating Distribution

1 stars: 0 votes
2 stars: 0 votes
3 stars: 0 votes
4 stars: 1 votes
5 stars: 8 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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