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Beginning Data Science with Python and Jupyter

Beginning Data Science with Python and Jupyter

By : Alex Galea
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Beginning Data Science with Python and Jupyter

Beginning Data Science with Python and Jupyter

4 (5)
By: Alex Galea

Overview of this book

Get to grips with the skills you need for entry-level data science in this hands-on Python and Jupyter course. You'll learn about some of the most commonly used libraries that are part of the Anaconda distribution, and then explore machine learning models with real datasets to give you the skills and exposure you need for the real world. We'll finish up by showing you how easy it can be to scrape and gather your own data from the open web, so that you can apply your new skills in an actionable context.
Table of Contents (5 chapters)
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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 and Python language keywords in text are shown as follows: "With the newly created blank Notebook, click in the top cell and type print('hello world')"

Folder names, filenames, file extensions, pathnames, include file names in text are shown as follows: "The header file boost/asio.hpp includes most of the types and functions required for using the Asio library".

A block of code is set as follows:

y = df['MEDV'].copy()
del df['MEDV']
df = pd.concat((y, df), axis=1)

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: "Click on New in the upper-right corner and select a kernel from the drop-down menu."

Important new programming terms are shown in bold. Conceptual terms are shown in italics.

Note

Important additional details about a topic appear like this, as in a sidebar.

Tip

Important notes, tips, and tricks appear like this.

CONTINUE READING
83
Tech Concepts
36
Programming languages
73
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