Python for Engineers and Scientists / basic to advanced
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 14h 41m | 12.6 GB
Instructor: Rafael Pereira da Silva, MSc.

Replace Excel and Matlab with Sympy, Numpy, Pandas, Matplotlib, and Scipy; Task automation; From basic to advanced.

[b]What you'll learn[/b]

• 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
  

[b]Requirements[/b]

The student should be familiar with exact sciences, but no prior knowledge of Python is required.

[b]Description[/b]

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 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.

Who this course is for:

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

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