Data Science Programming All-in-One For Dummies
Published by: John Wiley & Sons, Inc.

About This Book
You might find that this book starts off a little slowly because
most people don’t have a good grasp on getting a system
prepared for data science use. Book 1 helps you configure your
system. The book uses Jupyter Notebook as an Integrated
Development Environment (IDE) for both Python and R. That
way, if you choose to view the examples in both languages, you
use the same IDE to do it. Jupyter Notebook also relies on the
literate programming strategy first proposed by Donald Knuth
(see http://www.literateprogramming.com/) to make
your coding efforts significantly easier and more focused on the
data. In addition, in contrast to other environments, you don’t
actually write entire applications before you see something; you
write code and focus on the results of just that code block as
part of a whole application.
After you have a development environment installed and ready
to use, you can start working with data in all its myriad forms
in Book 2
. This book covers a great many of these forms —
everything from in-memory datasets to those found on large
websites. In addition, you see a number of data formats ranging
from flat files to Relational Database Management Systems
(RDBMSs) and Not Only SQL (NoSQL) databases.
Of course, manipulating data is worthwhile only if you can do
something useful with it. Book 3 discusses common sorts of
analysis, such as linear and logistic regression, Bayes’ Theorem,
and K-Nearest Neighbors (KNN).
Most data science books stop at this point. In this book,
however, you discover AI, machine learning, and deep learning
techniques to get more out of your data than you might have
thought possible. This exciting part of the book, Book 4
represents the cutting edge of analysis. You use huge datasets
to discover important information about large groups of people
that will help you improve their health or sell them products.
Performing analysis may be interesting, but analysis is only a
step along the path. Book 5 shows you how to put your
analysis to use in recommender systems, to classify objects,
work with nontextual data like music and video, and display the
results of an analysis in a form that everyone can appreciate.
The final minibook, Book 6
, offers something you won’t find in
many places, not even online. You discover how to detect and
fix problems with your data, the logic used to interpret the data,
and the code used to perform tasks such as analysis. By the
time you complete Book 6
, you’ll know much more about how
to ensure that the results you get are actually the results you
need and want.