HomeCoursesPython for Scientists and Engineers

4.8

Intermediate

13h

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.

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Overview

Content

Reviews

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.

If you're a scientist or an engineer interested in learning scientific computing, this is the place to start. In this course, y...Show More

TAKEAWAY SKILLS

Python Basics

Arrays

Plotting

Systems of Linear Equations

Symbolic Computation

Scientific Algorithms

Random Variables

Content

98 Lessons11 Quizzes1 Assessment1 Challenge

Introduction

2 Lessons

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

Introduction

About This Course

Python Refresher

12 Lessons

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

Data Types and Variables

Operators

Conditional Statements

Tuples and Dictionaries

Using Python Packages

Quiz 1!

Exercise: Check Sum

Solution Review: Check Sum

Arrays

13 Lessons

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

Introduction

Vectors

Multidimensional Arrays

Smart Array Programming

Quiz 3!

Exercise: Accessing 2-Dimensional Arrays

Solution Review: Accessing 2-Dimensional Arrays

Exercise: Using Conditions on Arrays

Solution Review: Using Conditions on Arrays

Plotting

11 Lessons

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

Basic Plotting

Important Note!

Plotting Multiple Curves

Exercise: Plotting Temperatures

Solution Review: Plotting Temperatures

Exercise: Plotting Torus

Solution Review: Plotting Torus

Systems of Linear Equations

7 Lessons

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

Building and Solving Linear Equations

Eigenvalues and Eigenvectors

Matrix Operations

Sparse Matrices

Quiz 5!

Exercise: Fitting a Wave

Solution Review: Fitting a Wave

Symbolic Computation

17 Lessons

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

Introduction

Symbols and Complex Numbers

Numerical Evaluation

Algebraic Manipulation

Ordinary Differential Equations

Quiz 8!

Exercise: Integrating Complex Functions

Solution Review: Integrating Complex Functions

Exercise: Solve a Differential Equation

Solution Review: Solve a Differential Equation

Scientific Algorithms

13 Lessons

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

Introduction

Numerical Integration

Exercise: Triple Integral Over a Bounded Region

Solution Review: Triple Integral Over a Bounded Region

Exercise: Parameters of an FID Signal

Solution Review: Parameters of an FID Signal

Random Variables

9 Lessons

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

Random Numbers

Flipping Coins

Bernoulli Variable

Normal Continuous Random Variables

Histograms and Probability Density Function

Percentiles

Quiz 11!

Exercise: Predicting Election Results

Solution Review: Predicting Election Results

Applications

10 Lessons

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

Introduction

Preview: Setting Up an Optical System

Exercise: Setting Up an Optical System

Solution Review: Setting Up an Optical System

Preview: Transfer Functions

Exercise: Transfer Functions

Solution Review: Transfer Functions

Preview: Harmonographs

Exercise: Harmonographs

Solution Review: Harmonographs

10.

Conclusion

2 Lessons

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

Last Thoughts

Where to Go from Here

11.

Appendix

2 Lessons

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

Files I/O

LaTeX Formatting

Python for Scientists and Engineers Exam

Assessment

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

Show License and Attributions

Developed by MAANG Engineers

Every Educative lesson is designed by our in-house team of ex-MAANG software engineers and PhD computer science educators, and developed in consultation with developers and data scientists working at Meta, Google, and more. Our mission is to get you hands-on with the necessary skills to stay ahead in a constantly changing industry. No video, no fluff. Just interactive, project-based learning with personalized feedback that adapts to your goals and experience.

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Hands-on Learning Powered by AI

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Personalized Interview Prep

Skip the LeetCode grind with a custom roadmap that adapts to your goals. Hands-on practice for Coding Interviews, System Design, and more.

Mock Interviews

Test your skills in a simulated interview setting. Receive personalized feedback based on your performance. Available for Coding Interviews, System Design, and more.

AI Prompt

Build prompt engineering skills. Practice implementing AI-informed solutions.

Code Feedback

Evaluate and debug your code with the click of a button. Get real-time feedback on test cases, including time and space complexity of your solutions.

Explain with AI

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AI Code Mentor

AI Code Mentor helps you quickly identify errors in your code, learn from your mistakes, and nudge you in the right direction — just like a 1:1 tutor!

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