NumPy: Fundamentals Of Python Data Science

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NumPy is written specifically for working with large quantities of numbers. It can be used by itself, but it’s also at the core of the most popular data science and machine learning packages, like Pandas and TensorFlow.

NumPy has a strong focus on performance. For this reason, NumPy is written mainly in a low-level programming language called C. It offers easy-to-use bindings for us Python users, so we don’t have to worry about low-level stuff.

In this course, you will learn NumPy from the ground up. We cover all the essential concepts and work up to some pretty advanced topics.

You won’t regret learning NumPy using this course if you are serious about:

  • Scientific computing
  • AI and machine learning
  • data science and data analysis

It offers a great starting point and lays a solid foundation to build on.

Several examples in this course use synthetical crypto currency data. You can download this data below:

Disclaimer: The cryptocurrency data presented here is entirely artificial; any resemblance with actual prices and volumes is coincidental. No example where such data is manipulated constitutes investment or trading advice.

Course Content

Introduction
Linear Algebra Basics
Matrices
Tensors 1 Quiz
Array Fundamentals
Lesson Content
NumPy dtypes (data types) 1 Quiz
Lesson Content
Special 1D array creation functions 1 Quiz
1D Array indexing 1 Quiz
Lesson Content
Append, insert, delete, and sort 1 Quiz
1D math operations on arrays 1 Quiz
Lesson Content
Working with multidimensional arrays
NumPy’s ndarray 1 Quiz
Lesson Content
Reshaping arrays
Multiple ways to create ndarrays
ndarray indexing and slicing
Our example dataset and basic plotting
Working with the example dataset
Install the seaborn plotting library
Plotting 1D arrays
Plotting 2D arrays
Time notation and asset returns
ndarray methods
ndarray methods vs NumPy functions
argmax and argmin
Max and min
Clip
Copy
Cumsum
Diagonal
Flatten and ravel
Sum, mean, and std
tolist
T and transpose
Method chaining
Array Axes
What are axes and how do they work?
Basic ndarray manipulation using axes
Using axes with array methods
Functions and axes
Vectorized Computing
Vector optimized operations
Broadcasting
Broadcasting as a problem-solving tool
Universal Functions (ufuncs)
Introduction
Unary ufuncs
Binary ufuncs
unfunc out and where arguments
ufunc methods
Creating high-performance custom ufuncs
Advanced array manipulation
Boolean arrays
Comparison functions
Boolean operators
Logic functions
Boolean masking
Boolean masking in practice
Boolean masking practice: missing crypto prices
Fancy indexing
Set operations
Copy vs. view
Randomness
Random number generators
Random choice
Normal distribution sampling
Selected Topics in Mathematics and Statistics
Inner product and matrix multiplication
Covariance and correlation matrices