SkillSoft Explore Course – Lambers, Inc.

IT Skills Data and Databases Data Science Python for Data Science

NumPy is oneof the fundamental packages for scientific computing that allows data to be represented in dimensional arrays. This course covers the array operations you can undertake such as image manipulation, fancy indexing, and broadcasting. To take this Aspire course, you should be comfortable with how to create, index, and slice Numpy arrays, and apply aggregate and universal functions. Among the topics, you will learn about the several options available in NumPy to split arrays. You will learn how to use NumPy to work with digital images, which are multidimensional arrays. Next, you will observe how to manipulate a color image, perform slicing operations to view sections of the image, and use a SciPy package for image manipulation. You will learn how to use masks, an array of index values, to access multiple elements of an array simultaneously, referred to as Sansi indexing. Finally, this course covers broadcasting to perform operations between mismatched arrays.

Asset ID	it_dspydsdj_02_enus
Course Type	Video Course
Course Category	Data and Databases

Objectives
Python for Data Science: Advanced Operations with NumPy Arrays Course Overview identify different ways in which arrays can be split up describe how grayscale and color images can be represented as multi-dimensional arrays perform some basic image manipulation after converting images to arrays create a view into a NumPy array and learn of the relationship between views and their base arrays compare deep copies of arrays with views and know when to use each of them use fancy indexing with arrays using an index mask use fancy indexing to analyze real-world data apply boolean masks to access array elements which fulfil a specific condition use structured arrays in order to store heterogeneous data describe how operations can be performed between arrays of mismatched shapes using broadcasting perform operations between arrays of mismatched shapes by applying broadcasting rules utilize NumPy to perform multi-dimensional array operations

Objectives

Python for Data Science: Advanced Operations with NumPy Arrays

Course Overview
identify different ways in which arrays can be split up
describe how grayscale and color images can be represented as multi-dimensional arrays
perform some basic image manipulation after converting images to arrays
create a view into a NumPy array and learn of the relationship between views and their base arrays
compare deep copies of arrays with views and know when to use each of them
use fancy indexing with arrays using an index mask
use fancy indexing to analyze real-world data
apply boolean masks to access array elements which fulfil a specific condition
use structured arrays in order to store heterogeneous data
describe how operations can be performed between arrays of mismatched shapes using broadcasting
perform operations between arrays of mismatched shapes by applying broadcasting rules
utilize NumPy to perform multi-dimensional array operations