11 Books and Courses to Learn NumPy in a Month [2023]

NumPy is the wrench in every data scientist’s toolbox. It is an incredibly useful library for working with data and a must-have skill for all data scientists, analysts, and engineers.

If you want to learn this in-demand skill, read on, this article will explain what NumPy is, why it is important, and the best resources to learn.

What is NumPy?

NumPy stands for Numerical Python. It is a library created by Travis Oliphant in 2005 and is used for data analysis.

At the heart of NumPy is the array. An array is simply a list of data values. This array can be used to represent vectors. It is very similar to the Python built-in list type but has one key difference.

Unlike Python lists, data in NumPy is stored in contiguous memory. This means the values are stored next to each other in memory. This makes accessing the values faster; NumPy arrays are up to 50 times faster than Python lists for common operations.

Like Python lists, arrays can store other arrays as elements. This allows you to create more complex mathematical constructs like matrices and higher-order arrays. Arrays have helpful methods for common statistical operations such as calculating the mean, median and standard deviation. You can modify them by splitting, joining, shaping, and reshaping.

Requirements for Using Numpy

• A python installation
• Pip installation
• An IDE such as VSCode or, more ideally, a Notebook-based IDE such as Jupyter
• Knowledge of Python

Also read: Jupyter Notebook Introduction for Beginners

Use cases

• Numpy is used for data science tasks because of its faster arrays instead of the built-in Python lists.
• It can be used to solve linear algebra problems using its built-in functions.
• It is used in machine learning because of its fast computation of vectors and matrices.
• It is used to generate random datasets using its random statistical functions.

Courses to Learn NumPy

Below are some of the best resources to learn NumPy and Data Science. Most of these resources assume some familiarity with Python. If you haven’t learned Python yet, here’s our list of the best resources to learn Python.

Deep Learning Prerequisites: The Numpy Stack in Python

This Udemy course offers a gentle guide to prepare you for deep learning using Python. The course teaches you how to use Numpy for vector and matric calculations.

In addition, it covers Pandas, a library for dealing with datasets in Python: Matplotlib (a data visualization tool), and Scipy (a library for computing statistics in Python).

The course contains six hours of on-demand video, and once you buy it, you get free lifetime access to it. It includes a certification. Before attempting this course, you should be familiar and comfortable with Linear Algebra and programming in Python.

Data Analysis with Python: NumPy & Pandas Masterclass

This comprehensive course teaches you how to analyze data using Pandas and NumPy. The method comprises 216 lectures, 3 articles, and 2 downloadable resources. This gives you a total of more than thirteen hours of content.

It begins by introducing you to NumPy and the concept of an array, which is the central object in NumPy. Then afterward, the course will teach you to use Pandas, a popular and useful library for working with Datasets. Then lastly, you will learn data visualization using the Matplotlib library.

What makes this course different from most is that it makes the lessons more practical by teaching you through role-play. You will play the role of Data Analyst at a large multinational retail company analyzing the data collected from its different operations. As expected, the course assumes some familiarity with Python before you start the course.

Python with NumPy For Absolute Beginners

This course is one of the most beginner-friendly courses on NumPy. While you are expected to know Python, the course introduces NumPy from the beginning.

It begins by introducing you to NumPy arrays. It explains how they differ from Python lists and how they are faster and more suited to data science, engineering, and analysis.

In addition, you will learn all the different things you can do with these arrays. These include but are not limited to creating arrays, accessing them using indexes, slicing and joining them, and shaping and reshaping them.

This course has two hours of video content and only focuses on Numpy. You can complete this and get certified in a week.

Introduction to NumPy

This course by DataCamp is friendly to beginners to NumPy. The course is about 4 hours long and comprises 13 well-made videos and 49 exercises to help you solidify the concepts you have learned.

It is part of the Data Scientist track, so if you complete other courses in the same track, you will earn your DataCamp Data Scientist certification.

As for the content, it introduces arrays and explains the advantages of using them over lists in Python. Next, you will learn broadcasting and vectorisation techniques to make your code faster and more efficient. You will practice array operations on the Monet dataset.

Simplilearn NumPy Tutorial

This free tutorial by Simplilearn covers the basics of Numpy. It is brief and goes straight to the point. The article has minimal explanations and is ideal if you are using it as a reference or if you already know what Numpy is and what the different functions do.

Also contained in the article are code snippets to illustrate the usage of different functions with examples. It is ideal when you are in a hurry and want to learn Numpy in ten minutes. Being an article, it has no place to practice or datasets to use.

You would have to set up a practice environment yourself and find datasets to practice from. Kaggle is a good place to look for datasets and create notebooks to practice data science.

W3Schools

This tutorial by W3Schools is my personal favorite. It is free and comprehensive, covering all the basics of NumPy and more advanced topics such as generating random statistical distributions and using universal functions to implement vectorisation.

In total, the tutorial is 43 web pages of succinct but adequate explanations and code snippets to illustrate with examples. In addition, w3schools comes with an editor for writing your Numpy queries and a quiz where you can test your knowledge.

All these are optional but would aid your learning experience. By enrolling in the Numpy course for a fee, you can earn a certification to add to your resume.

Scaler Course

This course on Scaler is well put together. It comprises six modules that cover an introduction to NumPy, multidimensional arrays, data structures, functions, broadcasting, and other miscellaneous concepts.

In total, it has 32 lessons with 5 hours and 33 minutes of video content. There are 26 challenges to help you apply what you have learned and solidify the concepts in your mind. After completing the course, you get a certificate.

As expected, you must know the Python programming language before starting the course. The second prerequisite has an IDE with Python and Numpy installed on your machine.

Guide to Numpy by Travis Oliphant

Written by the creator of Numpy, this book is meant to be a reference for those who already know Python but would like to learn about Numpy and other tools.

In this book, Travis Oliphant covers not just how to use Numpy but also how to extend it using the API. This is probably the most in-depth and detailed resource on Numpy.

It is probably ideal for power users of Numpy who want a higher-level understanding of how Numpy works and a detailed guide so they can contribute to and extend the library.

Numpy Beginner’s Guide by Ivan Idris

This book on Numpy is meant to be beginner-friendly. It is meant for scientists, engineers, programmers, and analysts who are already familiar with Python but are looking to extend their skillset by taking on Numpy as an additional skill.

The book covers installing Numpy, Matplotlib, Scipy, and IPython on the local machine. It then covers arrays and the different array functions made available to you. Then you will use the library to perform matrix operations and test your code with Numpy.testing. All in all, this book is a comprehensive guide to Numpy.

NumPy: From Basic to Advanced by Karan Singh Bisht

The title “NumPy from Basic to Advanced” says it all. This book is meant to be a gentle slope taking you from knowing nothing about the library to knowing how to use some of its more advanced features.

The book covers the basics, such as explaining what an array is, to going to more advanced and under-the-hood topics, such as CPU-cache effects and the life-cycle of the Ndarray. It is meant to give you a solid foundation for further machine learning work using the Numpy library.

FreeCodeCamp YouTube Tutorial

FreeCodeCamp has grown in popularity recently as a source of high-quality coding and software development tutorials. Within its tutorial catalogue is this comprehensive Numpy tutorial. Like all its tutorials, it is available for free.

The tutorial is about an hour and covers the basics of Numpy. It is a gentle introduction to the library meant to be not overwhelming for those who have just started. As you would expect, Python knowledge is assumed before watching the video.

Final Words

Numpy is incredibly useful and versatile. It is an expected tool for most data science and engineering jobs. This article introduced you to Numpy and gave you a high-level and abstract overview of its key concepts.

Further, the article listed resources that could be helpful in your journey to learning Python. The brief description of each resource was able to help you make an informed choice of which one to choose.

Next, check out the best Python libraries for data scientists.