Numpy Library for data science (All in one)

Name: Numpy Library for data science (All in one)
Rating: 4.5 (40 reviews)

Start your career towards the field of data science

Created byShambhavi Gupta

Last updated 5/2022

English

What you'll learn

Different Numpy function applied as Matrix/Array Operations
You will learn Numpy - Numerical Python Library
Various mathematical functions of Numpy
Go from absolute beginner to become a confident Python NumPy user
Dare to get the most out of Python NumPy
Go deeper to understand complex topics in Python NumPy

Course content

3 sections • 23 lectures • 3h 53m total length

IDE online and offline10:33
Compare offline python IDE options for data science, including Google Cloud notebooks, PyCharm Community, and Visual Studio Code. Learn Python installation on Windows and auto-save notebooks to Google Drive.
Introduction and Installation of numpy6:52
Explore the numpy library for data science and learn how to install it online or offline, then compare it with lists and start numerical calculations.
List vs Array10:11
Compare lists and arrays in numpy, noting similarities in mutability, indexing, and slicing, while arrays use less memory and offer faster performance with consistent data types.
Types of array4:46
Explore numpy arrays, focusing on one-dimensional and two-dimensional arrays, and learn two creation methods: using Python lists and the numpy library.

Declaration of array using function9:27
Learn how to declare arrays in Numpy by importing the library, using a short form, and creating one-dimensional arrays, including converting to complex form and printing results.
arange() function10:30
Learn how the arange function generates arrays of evenly spaced values, using start, stop, and optional step parameters; see examples from one to six, zero to nine, and complex outputs.
ones, zeros and empty function13:35
Master numpy's ones, zeros, and empty functions to create arrays with a given shape filled with ones or zeros, or an uninitialized array with random values.
Linspace function10:46
Learn how to generate evenly spaced numbers over a specified interval with NumPy linspace, by setting start, stop, and the number of samples, and deciding whether to include the endpoint.
Identity and Eye function15:43
Explore the NumPy identity function and identity matrix, with ones on the main diagonal and zeros elsewhere, and learn how to control rows and columns for matrix shapes.
Attributes of array9:03
Learn to inspect NumPy array attributes such as shape, dimensions, and size to understand the structure and contents of your arrays.
Indexing in array11:23
Learn how to index arrays in NumPy, including positive and negative indexing, and access elements in 1-D and 2-D arrays.
Slicing in array12:45
Learn how to use numpy slicing and indexing to extract subarrays from one-dimensional arrays, choosing start and end indices with a colon and optional step to control the output.
Arithmetic operators in array12:22
Learn to perform arithmetic operations on numpy arrays, including indexing and slicing, with examples of addition, subtraction, multiplication, division, and exponent on 1D and 2D arrays.
Reshape and resize function13:12
reshape and resize in numpy create a new shape for an array without altering its data, with resize potentially repeating data to fill the new size.
Flatten function8:39
Learn how the numpy flatten function converts multi-dimensional arrays into a single dimension and returns a copy, with default and column-wise ordering options.
Ravel function9:43
Explore NumPy ravel, which converts a multi-dimensional array into a contiguous one-dimensional array, similar to flatten, with an optional order parameter to control row- or column-major layout.
Transpose function13:05
Explore how the numpy transpose function interchanges rows and columns, reshaping a 5 by 2 array to 2 by 5, while 1-D arrays remain unchanged.
Swapaxes function8:53
Explore numpy's swapaxes function to interchange two axes in an array, and compare it with transpose for understanding axis swapping and data reshaping.
Concatenate function8:40
Explore the NumPy concatenate function to join arrays and strings by importing the library, applying it to multiple variables, and printing the combined results.

matrix addition9:06
Explore element-wise matrix addition in numpy by adding corresponding elements of two 2x2 matrices, A and B, with code and a 1-D array example.
matrix multiplication11:03
Learn how to perform matrix multiplication in numpy by using the dot function on two 2d arrays, with practical examples and correct results.
Linear algebra8:41
Explore linear algebra with numpy by computing matrix inverses, handling 2-D arrays, and applying functions to A and B, with practical printing of results.
Power of a matrix4:23
Learn to compute the power of a matrix using numpy by importing numpy, defining a two-dimensional array, and applying the matrix power operation.

Requirements

Should have a basic knowledge of Python

Description

Hello,

Welcome to Numpy Library for Data Science Course

Are you ready for the Data Science career?

Do you want to learn the Python Numpy from Scratch? or

Are you an experienced Data scientist and looking to improve your skills with Numpy!

In both cases, you are at the right place! The number of companies and enterprises using Python is increasing day by day. The world we are in is experiencing the age of informatics. Python and its Numpy library will be the right choice for you to take part in this world and create your own opportunities,

Numpy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.

Numpy aims to provide an array object that is up to 50x faster than traditional Python lists. The array object in Numpy is called ndarray , it provides a lot of supporting functions that make working with ndarray very easy. Arrays are very frequently used in data science.

In this course, we will open the door of the Data Science world and will move deeper. You will learn Numpy step by step with hands-on examples. Most importantly in Data Science, you should know how to use effectively the Numpy library. Because this library is limitless.

In this course you will learn;

Introduction and Installation
Similarities and difference in a list and an array
Declare an array
Array function in numpy library
arange function
ones, zeros and empty function
linspace function
identity and eye function
Attributes of array
Indexing in array
Slicing in array
Arithmetic operators in an array
reshape and resize function in an array
flatten function in an array
ravel function in an array
transpose function
swapaxes function
Concatenate function
Different matrices function like finding inverse , matix multiplication etc.
This course will take you from a beginner to a more experienced level.

If you are new to data science or have no idea about what data science is, no problem, you will learn anything from scratch you need to start data science.

If you are a software developer or familiar with other programming languages and you want to start a new world, you are also in the right place. You will learn step by step with hands-on examples.

See you in the course!

Who this course is for:

Any one can take this course who wants to go into field of data science

Numpy Library for data science (All in one)

What you'll learn

Explore related topics

Course content

Introduction4 lectures • 32min

Functions in array15 lectures • 2hr 48min

Matrix Functions4 lectures • 33min

Requirements

Description

Who this course is for: