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

Intermediate

Course
Description

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

Curriculum

Intro to Numpy

Vectorized operations

Boolean Arrays and Masking

Advanced NumPy

Final Project

LESSONS: 1

Final Project

LESSONS: 4

Summary statistics using NumPy

Statistics Exercises

Comparing NumPy efficiency vs Pure Python

Advanced Assignments

LESSONS: 3

Introduction to NumPy Boolean Arrays

Boolean Arrays Exercises 1

Boolean Arrays Exercises 2

LESSONS: 4

Intro to Vectorized Operators using NumPy

Vectorized Operators Exercises

NumPy universal functions

Universal functions Exercises

LESSONS: 5

Intro to NumPy

Basic NumPy Exercises 1

Basic NumPy Exercises 2

Intro to Linear Algebra using NumPy

Matrices Exercises

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Chris McCluskey

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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 Athlete.com, 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.