MP4 | Video: AVC, 1280x720 15fps | Audio: AAC, 48 KHz, 2 Ch | Duration: 3h 54m

Skill Level: Intermediate | Genre: eLearning | Language: English + Subtitles | Size: 600 MB

NumPy Data Science Essential Training introduces the beginning to intermediate data scientist to NumPy, the Python library that supports numerical, scientific, and statistical programming, including machine learning. The library supports several aspects of data science, providing multidimensional array objects, derived objects (matrixes and masked arrays), and routines for math, logic, sorting, statistics, and random number generation. Here Charles Kelly shows how to work with NumPy and Python within Jupyter Notebook, a browser-based tool for creating interactive documents with live code, annotations, and even visualizations such as plots. Learn how to create NumPy arrays, use NumPy statements and snippets, and index, slice, iterate, and otherwise manipulate arrays. Plus, learn how to plot data and combine NumPy arrays with Python classes, and get examples of NumPy in action: solving linear equations, finding patterns, performing statistics, generating magic cubes, and more.

Topics include:

Using Jupyter Notebook

Creating NumPy arrays from Python structures

Slicing arrays

Using Boolean masking and broadcasting techniques

Plotting in Jupyter notebooks

Joining and splitting arrays

Rearranging array elements

Creating universal functions

Finding patterns

Building magic squares and magic cubes with NumPy and Python