Gain insights into data science with easy-to-follow, hands-on explanations. Explore essential concepts quickly and efficiently, even without prior statistics knowledge, for a career boost.

GrokkingDataScientist.tar.gz

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Master the skills that can get you a $100K+ salary even if you bunked your statistics classes. 

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Grokking Data Science

# 1. Computations on NumPy Arrays
The reason for NumPy's importance in the *Pythonic* data science world is its ability to perform computations in a fast and efficient manner. Now that we are familiar with the basic nuts and bolts of NumPy, we are going to dive into learning it to perform computations.

NumPy provides the so-called *universal functions* (*ufuncs*) that can be used to make repeated calculations on array elements in a very efficient manner. These are functions that operate on *nD*-arrays in an element-by-element fashion. Remember the vectorized operations from earlier.

## Mathematical Functions

What are some of the most common and useful mathematical *ufuncs* available in the NumPy package? Let's explore them with some concrete examples. 

The arithmetic operators, as shown in the code widget below, are conveniently wrapped around specific functions built into NumPy; for example, the *+* operator is a wrapper for the *add* ufunc. 

**Run** the code in the widget below, **tweak** the inputs, and **observe** the outputs of the print statements.

Python Fundamentals for Data Science

The Fundamentals of Statistics

Machine Learning 101

End-to-End Machine Learning Project

The Real Talk

NumPy Arithmetic and Statistics - Computations and Aggregations

1. Computations on NumPy Arrays

Mathematical Functions