Page 14 - Data Science Algorithms in a Week

Preface

Appendix D, Glossary of Algorithms and Methods in Data Science, Provides a glossary for some
of the most important and powerful algorithms and methods from the fields of the data
science and machine learning.

What you need for this book

Most importantly, an active attitude to think of the problems--a lot of new content is
presented in the exercises. Then you also need to be able to run Python and R programs
under the operating system of your choice. The author ran the programs under Linux
operating system using command line.

Who this book is for

This book is for aspiring data science professionals who are familiar with Python & R and
have some statistics background. Those developers who are currently implementing 1 or 2
data science algorithms and now want to learn more to expand their skill will find this book
quite useful.

Conventions

In this book, you will find a number of text styles that distinguish between different kinds
of information. Here are some examples of these styles and an explanation of their meaning.
Code words in text, database table names, folder names, filenames, file extensions,
pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "For the
visualization depicted earlier in this chapter, the matplotlib library was used."

A block of code is set as follows:

import sys
sys.path.append('..')
sys.path.append('../../common')
import knn # noqa
import common # noqa

Any command-line input or output is written as follows:

$ python knn_to_data.py mary_and_temperature_preferences.data
mary_and_temperature_preferences_completed.data 1 5 30 0 10
New terms and important words are shown in bold. Words that you see on the screen, for
example, in menus or dialog boxes, appear in the text like this: "In order to download new
modules, we will go to Files | Settings | Project Name | Project Interpreter."

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