Intro to Scientific Computing with NumPy

Intro to Scientific Computing with NumPy

Learn how to use NumPy for scientific computing and build the foundations of Data Science and large data processing

10 hours

36 assignments

7 videos



NumPy was the library that got started the Data Science / Python revolution. In this course we'll explore all the fundamental concepts it introduced: low level bindings (C, Fortran), multidimensional arrays, vectorized operations, etc. The final project will involve matrix processing and a lot of thinking writing a Sudoku Solver!

"For this course, I focused on drawing the parallelism between basic Python data structures and NumPy arrays, and specially, how to apply it to scientific computing, without forgetting about performance: how faster is NumPy compared to Python?"

Instructor Santiago Basulto

Santiago Basulto

Course Instructor


Intro to Numpy

Vectorized operations

Boolean Arrays and Masking

Advanced NumPy

Final Project


Sudoku meets NumPy


Intro to NumPy

Basic NumPy Exercises 1

Basic NumPy Exercises 2

Intro to Linear Algebra using NumPy

Matrices Exercises


Intro to Vectorized Operators using NumPy

Vectorized Operators Exercises

NumPy universal functions

Universal functions Exercises


Introduction to NumPy Boolean Arrays

Boolean Arrays Exercises 1

Boolean Arrays Exercises 2


Summary statistics using NumPy

Statistics Exercises

Comparing NumPy efficiency vs Pure Python

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William Ponton

The perfect combination of building real-world skills through challenging coursework and projects.

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I have tried multiple ways to learn Data Science. Now, with the lessons and practice provided here, I finally feel like I am making real progress.

Chris Mccluskey
Chris McCluskey

Clear guidance while providing detailed explanations. Greatly enhanced my knowledge in a short amount of time!

Course instructor

Santiago Basulto

Data Scientist at RMOTR

Santiago is an experienced Data Scientist and Python programmer with more than 10 years of experience in the field. He started as a Java developer working as a consultant on high performance and critical systems before moving to Python. In 2012 was hired as the CTO of, a startup analyzing data from runners. He then fell in love with Data processing with Python and in 2015 founded RMOTR, to provide expert-level Data Science training.