NumPy for Scientific Computation with Python - 2021 Edition
Last Update: 8/2021
Duration: 46m | Video: .MP4, 1280x720 30 fps | Audio: AAC, 44.1 kHz, 2ch | Size: 330 MB
Genre: eLearning | Language: English
Learn the A-Z of NumPy for working with multi-dimensional arrays in Python.
What you'll learn:
Learn what is NumPy, its history and benefits.
Learn to install and import NumPy in Python.
Learn the basics of NumPy arrays.
Learn the different ways to create NumPy arrays.
Learn how to perform indexing and slicing on NumPy arrays.
Learn how to perform various NumPy operations.
Learn how to save and load NumPy arrays in different file formats.
Requirements:
Python programming knowledge is a must.
Description:
Are you a beginner looking to kick off your career in data science using Python? Then, this course on NumPy is a must for you!NumPy, or Numerical Python, is an open-source Python library that helps you perform simple as well as complex computations on numerical data. It is the go-to scientific computation library for beginners as well as advanced Python programmers and it is used mostly by statisticians, data scientists, and engineers.
In this course, you will learn everything you need to know about NumPy arrays starting from how to install NumPy and import it in Python. You will be introduced to various methods of creating NumPy arrays and you will also learn various operations on them. Furthermore, the course helps you learn how to perform indexing and slicing on NumPy arrays. The course ends off by teaching you how to save/load NumPy arrays in different file formats.
Why you should take this course?
Updated 2021 course content: All our course content is updated as per the latest technologies and tools available in the market
Practical hands-on knowledge: This course is oriented to providing a step-by-step implementation guide rather than just sticking to the theory.
Guided support: We are always there to guide you through the Q/As so feel free to ask us your queries.
Who this course is for:
Beginner Python developers curious about data science
Mathematicians looking to work with multi-dimensional arrays
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