Chapter 1, First Steps, introduces Jupyter notebooks and demonstrates how you can have access to the data run in the tutorials.

Chapter 2, Data Munging, gives an overview of the data science pipeline and explores all the key tools for handling and preparing data before you apply any learning algorithm and set up your hypothesis experimentation schedule.

Chapter 3, The Data Pipeline, discusses all the operations that can potentially improve or even boost your results.

Chapter 4, Machine Learning, delves into the principal machine learning algorithms offered by the Scikit-learn package, such as, among others, linear models, support vector machines, ensembles of trees, and unsupervised techniques for clustering.

Chapter 5, Social Network Analysis, introduces graphs, which is an interesting deviation from the predic-tors/target flat matrices. It is quite a hot topic in data science now. Expect to delve into very complex and intricate networks!

Chapter 6, Visualization, Insights, and Results, the concluding chapter, introduces you to the basics of visualization with Matplotlib, how to operate EDA with pandas, how to achieve beautiful visualizations with Seaborn and Bokeh, and also how to set up a web server to provide information on demand.

Appendix, Strengthen Your Python Foundations, covers a few Python examples and tutorials focused on the key features of the language that are indispensable in order to work on data science projects.

Python and all the data science tools mentioned in the book, from IPython to Scikit-learn, are free of charge and can be freely downloaded from the Internet. To run the code that accompanies the book, you need a computer that uses Windows, Linux, or Mac OS operating systems. The book will introduce you step-by-step to the process of installing the Python interpreter and all the tools and data that you need to run the examples.

If you are an aspiring data scientist and you have at least a working knowledge of data analysis and Python, this book will get you started in data science. Data analysts with experience in R or MATLAB will also find the book to be a comprehensive reference to enhance their data manipulation and machine learning skills.

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: "By using the to_bokehmethod, any chart and plot from other packages can be easily ported into Bokeh."

A block of code is set as follows:

File: bottle1.py
from bottle import route, run, template
port = 9099
@route('/personal/<name>')
def homepage(name):
return template('Hi <b>{{name}}</b>!', name=name)
print("Try going to http://localhost:{}/personal/Tom".format(port))
print("Try going to http://localhost:{}/personal/Carl".format(port))
run(host='localhost', port=port)

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

In: import numpy as np
from bokeh.plotting import figure, output_file, show
x = np.linspace(0, 5, 50)
y_cos = np.cos(x)
output_file("cosine.html")
p = figure()
p.line(x, y_cos, line_width=2)
show(p)

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: "Once the Jupyter instance has opened in the browser, click on the New button."

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