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Foundational Python for Data Science
- By Kennedy Behrman
- Published Oct 7, 2021 by Pearson. Part of the Addison-Wesley Data & Analytics Series series.
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- List Price: $77.98
- Includes EPUB and PDF
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eBook
- Your Price: $37.99
- Includes EPUB and PDF
- About eBook Formats
This eBook includes the following formats, accessible from your Account page after purchase:
EPUB
The open industry format known for its reflowable content and usability on supported mobile devices.
PDF
The popular standard, used most often with the free Acrobat® Reader® software.
This eBook requires no passwords or activation to read. We customize your eBook by discreetly watermarking it with your name, making it uniquely yours.
About
Features
- Master Google colab notebook Data Science programming
- Manipulate data with popular Python libraries such as: pandas and numpy
- Apply Python Data Science recipes to real world projects
- Learn functional programming essentials unique to Data Science
- Access case studies, chapter exercises, learning assessments, comprehensive Jupyter based Notebooks, and a complete final project
Description
- Copyright 2022
- Dimensions: 7" x 9-1/8"
- Pages: 256
- Edition: 1st
- Book
- ISBN-10: 0-13-662435-9
- ISBN-13: 978-0-13-662435-6
Data science and machine learning—two of the world's hottest fields—are attracting talent from a wide variety of technical, business, and liberal arts disciplines. Python, the world's #1 programming language, is also the most popular language for data science and machine learning. This is the first guide specifically designed to help millions of people with widely diverse backgrounds learn Python so they can use it for data science and machine learning.
Leading data science instructor and practitioner Kennedy Behrman first walks through the process of learning to code for the first time with Python and Jupyter notebook, then introduces key libraries every Python data science programmer needs to master. Once you've learned these foundations, Behrman introduces intermediate and applied Python techniques for real-world problem-solving.
Throughout, Foundational Python for Data Science presents hands-on exercises, learning assessments, case studies, and more—all created with Colab (Jupyter compatible) notebooks, so you can execute all coding examples interactively without installing or configuring any software.
Sample Content
Online Sample Chapter
Sequences in the Python Programming Language
Sample Pages
Download the sample pages (includes Chapter 3)
Table of Contents
Preface xiiiI: Learning Python in a Notebook Environment 1
1 Introduction to Notebooks 3II: Data Science Libraries 83
2 Fundamentals of Python 13
3 Sequences 25
4 Other Data Structures 37
5 Execution Control 55
6 Functions 67
7 NumPy 85III: Intermediate Python 171
8 SciPy 103
9 Pandas 113
10 Visualization Libraries 135
11 Machine Learning Libraries 153
12 Natural Language Toolkit 159
13 Functional Programming 173
14 Object-Oriented Programming 187
15 Other Topics 201
A Answers to End-of-Chapter Questions 215
Index 221
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