Book Image

Practical Data Science with Python

By : Nathan George
Book Image

Practical Data Science with Python

By: Nathan George

Overview of this book

Practical Data Science with Python teaches you core data science concepts, with real-world and realistic examples, and strengthens your grip on the basic as well as advanced principles of data preparation and storage, statistics, probability theory, machine learning, and Python programming, helping you build a solid foundation to gain proficiency in data science. The book starts with an overview of basic Python skills and then introduces foundational data science techniques, followed by a thorough explanation of the Python code needed to execute the techniques. You'll understand the code by working through the examples. The code has been broken down into small chunks (a few lines or a function at a time) to enable thorough discussion. As you progress, you will learn how to perform data analysis while exploring the functionalities of key data science Python packages, including pandas, SciPy, and scikit-learn. Finally, the book covers ethics and privacy concerns in data science and suggests resources for improving data science skills, as well as ways to stay up to date on new data science developments. By the end of the book, you should be able to comfortably use Python for basic data science projects and should have the skills to execute the data science process on any data source.
Table of Contents (30 chapters)
1
Part I - An Introduction and the Basics
4
Part II - Dealing with Data
10
Part III - Statistics for Data Science
13
Part IV - Machine Learning
21
Part V - Text Analysis and Reporting
24
Part VI - Wrapping Up
28
Other Books You May Enjoy
29
Index

To get the most out of this book

  • Readers should be interested in computing, using Python, and doing data science
  • This book is intended for beginners and intermediates to Python and data science, though more advanced practitioners can benefit by reading it as well (for example, to review and maybe learn some new things)
  • Readers should have some basic knowledge of how to use a computer and the internet
  • Installation of Python is required, but is covered in the book

Download the example code files

The code bundle for the book is hosted on GitHub at https://github.com/PacktPublishing/Practical-Data-Science-with-Python. We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Download the color images

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: https://static.packt-cdn.com/downloads/9781801071970_ColorImages.pdf.

Conventions used

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. For example: "Lists or sets can be converted to tuples with the tuple() function."

A block of code is set as follows:

def test_function(doPrint, printAdd='more'):
    """
    A demo function.
    """
    if doPrint:
        print('test' + printAdd)
        return printAdd

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

SELECT * FROM artists LIMIT 5;

Bold: Indicates a new term, an important word, or words that you see on the screen, for example, in menus or dialog boxes. For example: "We can create a new notebook by choosing New and then Python 3."

Warnings or important notes appear like this.

Tips and tricks appear like this.