Gain insights into utilizing NumPy for data manipulation and analytics. Learn about implementing concepts in both Python and NumPy through coding challenges and quizzes.

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If you're looking to grow your career in machine learning or data science in this day and age, adding a powerful library to your skill set is an important place to start. In that vein, Python has become one of the most widely used tools in the industry for serious data analytics, and NumPy is probably the most widely used data analytics library. With NumPy, you can manipulate data involving multi-dimensional arrays and matrices (think linear algebra).

Join us as we venture into the vast world of NumPy in this comprehensive course. Each lesson dive into the actual implementation of concepts in both pure Python and then NumPy, exploring how NumPy vectorization compares to traditional Python that uses a procedural and object-oriented approach.

Practice and test yourself along the way with in-browser coding challenges, quizzes, and more.

This course is intended for users who are already familiar with intermediate level Python.

From Python to Numpy

`Nicolas P. Rougier` is a full-time research scientist at Inria which is the
French national institute for research in computer science and control. This is
a public scientific and technological establishment (EPST) under the double
supervision of the Research & Education Ministry, and the Ministry of Economy
Finance and Industry. Nicolas P. Rougier is working within the Mnemosyne
project which lies at the frontier between integrative and computational
neuroscience in association with the `Institute of Neurodegenerative
Diseases`, the Bordeaux laboratory for research in computer science
(LaBRI), the `University of Bordeaux` and the national center for scientific
research (CNRS).

He has been using Python for more than 15 years and NumPy for more than 10
years for modeling in neuroscience, machine learning and for advanced
visualization (OpenGL). Nicolas P. Rougier is the author of several online
resources and tutorials (Matplotlib, NumPy, OpenGL) and he's teaching Python,
NumPy and scientific visualization at the University of Bordeaux and in various
conferences and schools worldwide (SciPy, EuroScipy, etc). He's also the author
of the popular article `Ten Simple Rules for Better Figures` and a popular
[matplotlib tutorial](http://www.labri.fr/perso/nrougier/teaching/matplotlib/matplotlib.html).

A brief introduction about the author of this course!

Introduction

Anatomy of an Array

Code Vectorization

Problem Vectorization

Custom Vectorization

Beyond NumPy

Conclusion

About the Author