Scientific Computing with NumPy – Python Data Science | Udemy

Scientific Computing with NumPy – Python Data Science | Udemy [Update 11/2021]
English | Size:
Genre: eLearning

Explore data science in Python by doing linear algebra, image processing, simple machine learning and more in NumPy!

What you’ll learn
Learn to confidently work with vectors and matrices in NumPy.
Learn basic functionality like sorting, calculating means, and finding max/min values.
Learn to draw line plots, bar plots, and scatterplots.
Learn to generate different types of random vectors.
Learn to modify and reshape matrices to your advantage.
Learn Boolean indexing and advanced slicing to extract useful information.
Learn to do basic linear algebra in NumPy like solving linear systems, calculating inverses, and more!
Get an understanding of how ndarrays work and utilize this to create fast code.
Learn Fourier transforms with NumPy and use this to manipulate images and audio.
Learn advanced linear algebra like the QR decomposition and partial least squares.
Learn how to preserve your NumPy objects in different formats.
Learn about neighboring libraries and that NumPy is used everywhere in Python’s data science stack.

Do you want to learn NumPy in 2021 to get started with data analysis in Python?

Hi there!

We’re a couple (Eirik and Stine) who love to create high-quality courses! In the past, Eirik has taught both Python and NumPy at the university level, while Stine has written learning material for a university course that has used NumPy. We both love NumPy and can’t wait to teach you all about it!

What this course is all about:

In this course, we will teach you the ins and outs of the Python library NumPy. This library is incredibly powerful and is used for scientific computing, linear algebra, image processing, machine learning and more. If you are interested in one of these topics, or simply want to get started with data science in Python, then this is the course for you!

The course will teach you everything you need to know to professionally use NumPy. We will start with the basics, and then gradually move on to more complicated topics. As NumPy is the fundamental building block for other popular Python libraries like Pandas, Scikit-Learn and PyTorch, it’s a great library to get you started with data science in Python.

Why choose us?

This course is a comprehensive introduction to NumPy! We don’t shy away from the technical stuff and want you to stand out with your newly learned NumPy skills.

The course is filled with carefully made exercises that will reinforce the topics we teach. In between videos, we give small exercises that help you reinforce the material. Additionally, we have larger exercises where you will be given a Jupiter Notebook sheet and asked to solve a series of questions that revolve around a single topic. We give exercises on awesome topics like audio processing, linear regression, and image manipulation!

Topics we will cover:

We will cover a lot of different topics in this course. In order of appearance, they are:

Introduction to NumPy

Working with Vectors

Universal Functions and Plotting

Randomness and Statistics

Making and Modifying Matrices

Broadcasting and Advanced Indexing

Basic Linear Algebra

Understanding n-dimensional Arrays

Fourier Transforms

Advanced Linear Algebra

Saving and Loading Data

By completing our course, you will be comfortable with NumPy and have a solid foundation for learning data science in Python.

Still not decided?

The course has a 30-day refund policy, so if you are unhappy with the course, then you can get your money back painlessly. If are still uncertain after reading this, then take a look at some of the free previews below, and see if you enjoy them. Hope to see you soon!

Who this course is for:
Anyone who wants to get a good understanding of NumPy.
Students who want to implement topics like linear algebra, machine learning, and image processing in Python.
Python developers who are curious about NumPy and data science!



If any links die or problem unrar, send request to

Leave a Comment

This site uses Akismet to reduce spam. Learn how your comment data is processed.