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

We’ve seen three forms of code vectorization through these examples:

* **Uniform vectorization** where elements share the same computation
  unconditionally and for the same duration.
* **Temporal vectorization** where elements share the same computation but
  necessitate a different number of iterations.
* **Spatial vectorization** where elements share the same computation but on
  dynamic spatial arguments.

And there are probably many more forms of such direct code vectorization. As
explained before, this kind of vectorization is one of the most simple even
though we've seen it can be really tricky to implement and requires some
experience, some help or both. For example, the solution to the boids exercise
was provided by [Divakar](http://stackoverflow.com/users/3293881/divakar) on
[stack overflow](http://stackoverflow.com/questions/40822983/multiple-individual-2d-rotation-at-once) after having explained my problem.

This lesson provides a brief summary of what we have covered so far in this chapter. 

Introduction

Anatomy of an Array

Code Vectorization

Problem Vectorization

Custom Vectorization

Beyond NumPy

Conclusion

Conclusion