Python for Scientists and Engineers

Gain insights into Python for scientific computing. Explore arrays, plotting, linear equations, and algorithms using NumPy, Matplotlib, SciPy. Delve into applying tools with practical exercises.

Intermediate

98 Lessons

13h

Certificate of Completion

Gain insights into Python for scientific computing. Explore arrays, plotting, linear equations, and algorithms using NumPy, Matplotlib, SciPy. Delve into applying tools with practical exercises.

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Explanations

AI-POWERED

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This course includes

1 Assessment
252 Playgrounds
11 Quizzes

This course includes

1 Assessment
252 Playgrounds
11 Quizzes

Course Overview

If you're a scientist or an engineer interested in learning scientific computing, this is the place to start. In this course, you'll learn to write your own useful code to perform impactful scientific computations. Along the way, your understanding will be tested with periodic quizzes and exercises. Topics covered in this course include arrays, plotting, linear equations, symbolic computation, scientific algorithms, and random variables. You’ll also be exposed to popular Python packages like NumPy, Matplo...Show More

TAKEAWAY SKILLS

Python Basics

Arrays

Plotting

Systems Of Linear Equations

Symbolic Computation

Scientific Algorithms

Random Variables

Course Content

1.

Introduction

Get familiar with Python's advantages in scientific computing and essential programming libraries.
2.

Python Refresher

Get started with Python essentials, including variables, operators, loops, functions, and packages.
3.

Arrays

Examine NumPy arrays, multidimensional arrays, array operations, indexing, data processing, and smart programming techniques.
4.

Plotting

Apply your skills to create and customize 2-D and 3-D plots using matplotlib.
5.

Systems of Linear Equations

Solve problems in systems of linear equations, eigenvalues, matrix operations, and sparse matrices.
6.

Symbolic Computation

17 Lessons

Follow the process of using SymPy for symbolic computation, including algebra, calculus, and equation solving.
7.

Scientific Algorithms

13 Lessons

Master scientific algorithms using SciPy for integration, interpolation, optimization, and Fourier transforms.
8.

Random Variables

9 Lessons

Learn how to use random variables, distributions, histograms, percentiles, and prediction simulations.
9.

Applications

10 Lessons

Get started with hands-on applications of Python in optical systems, transfer functions, and harmonographs.
10.

Conclusion

2 Lessons

Go hands-on with future learning in data science and machine learning skills.
11.

Appendix

2 Lessons

Grasp the fundamentals of efficient file I/O with NumPy and LaTeX formatting in matplotlib.

Python for Scientists and Engineers Exam

Assessment

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