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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.
4.9
98 Lessons
13h
Join 2.9 million developers at
Join 2.9 million developers at
Learning Roadmap
1.
Introduction
Introduction
Get familiar with Python's advantages in scientific computing and essential programming libraries.
2.
Python Refresher
Python Refresher
Get started with Python essentials, including variables, operators, loops, functions, and packages.
3.
Arrays
Arrays
13 Lessons
13 Lessons
Examine NumPy arrays, multidimensional arrays, array operations, indexing, data processing, and smart programming techniques.
4.
Plotting
Plotting
11 Lessons
11 Lessons
Apply your skills to create and customize 2-D and 3-D plots using matplotlib.
5.
Systems of Linear Equations
Systems of Linear Equations
7 Lessons
7 Lessons
Solve problems in systems of linear equations, eigenvalues, matrix operations, and sparse matrices.
6.
Symbolic Computation
Symbolic Computation
17 Lessons
17 Lessons
Follow the process of using SymPy for symbolic computation, including algebra, calculus, and equation solving.
7.
Scientific Algorithms
Scientific Algorithms
13 Lessons
13 Lessons
Master scientific algorithms using SciPy for integration, interpolation, optimization, and Fourier transforms.
8.
Random Variables
Random Variables
9 Lessons
9 Lessons
Learn how to use random variables, distributions, histograms, percentiles, and prediction simulations.
9.
Applications
Applications
10 Lessons
10 Lessons
Get started with hands-on applications of Python in optical systems, transfer functions, and harmonographs.
10.
Conclusion
Conclusion
2 Lessons
2 Lessons
Go hands-on with future learning in data science and machine learning skills.
11.
Appendix
Appendix
2 Lessons
2 Lessons
Grasp the fundamentals of efficient file I/O with NumPy and LaTeX formatting in matplotlib.
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Developed by MAANG Engineers
ABOUT THIS COURSE
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, Matplotlib, SciPy, and others. In the last part of the course, the application section will test your ability to recall and apply the tools you have studied into newly learned scientific concepts.
At the end of this course, you'll be equipped with the tools necessary for everyday scientific computation.
ABOUT THE AUTHOR
Khayyam Hashmi
Computer scientist and Generative AI and Machine Learning specialist. VP of Technical Content @ educative.io.
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Anthony Walker
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Evan Dunbar
ML Engineer
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Software Developer
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Front-end Developer
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Software Developer
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