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An Introduction to Data Science With Python

An Introduction to Data Science With Python

June 2024 | 256 pages | SAGE Publications, Inc
An Introduction to Data Science with Python by Jeffrey S. Saltz and Jeffery M. Stanton provides readers who are new to Python and data science with a step-by-step walkthrough of the tools and techniques used to analyze data and generate predictive models. After introducing the basic concepts of data science, the book builds on these foundations to explain data science techniques using Python-based Jupyter Notebooks. The techniques include making tables and data frames, computing statistics, managing data, creating data visualizations, and building machine learning models. Each chapter breaks down the process into simple steps and components so students with no more than a high school algebra background will still find the concepts and code intelligible. Explanations are reinforced with linked practice questions throughout to check reader understanding. The book also covers advanced topics such as neural networks and deep learning, the basis of many recent and startling advances in machine learning and artificial intelligence. With their trademark humor and clear explanations, Saltz and Stanton provide a gentle introduction to this powerful data science tool.

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Introduction - Data Science, Many Skills
Chapter 1 - Begin at the Beginning With Python
Chapter 2 - Rows and Columns
Chapter 3 - Data Munging
Chapter 4 - What’s My Function?
Chapter 5 - Beer, Farms, Peas, and Statistics
Chapter 6 - Sample in a Jar
Chapter 7 - Storage Wars
Chapter 8 - Pictures vs. Numbers
Chapter 9 - Map Magic
Chapter 10 - Linear Models
Chapter 11 - Classic Classifiers
Chapter 12 - Left Unsupervised
Chapter 13 - Words of Wisdom: Doing Text Analysis
Chapter 14 - In the Shallows of Deep Learning


Instructor Resources
Online resources included with this text

The online resources for your text are available via the password-protected Instructor Resource Site, which offers access to all text-specific resources, including a standalone Jupyter Notebook file for each chapter and editable, chapter-specific PowerPoint® slides.

"Easy to understand, useful, practical."

Yi Liu
University of the Incarnate Word

"I have not come across another similar book on Python. The content, structure, and writing style of this book are all quite unique because it is about Python."

Minjuan Wang
San Diego State University and Immersive Learning Research Network (iLRN)

"A book focused on providing an introduction to data science with a breadth of topics that might stir up interest in further exploration."

John Bono
University of Maryland, College Park

"Useful, direct text for teaching data analysis using Python."

James N. Maples
Eastern Kentucky University

"This book could expand our students' knowledge base and help them build new data analysis skills."

Miao Guo
University of Connecticut
Key features
  • Hands-on examples, including a case study that runs through all the chapters, are relatable to most students.
  • Each chapter is accompanied by a standalone Jupyter Notebook file.
  • A chapter on deep learning, the basis of many recent and startling advances in machine learning and artificial intelligence, introduces the essential ideas behind deep learning and develops a simple predictive model to illustrate the training process.
  • Brief code blocks help readers break down Python code into digestible chunks.
  • Chapter Challenge questions at the end of each chapter offer further exercises so readers can expand their knowledge.
  • Throughout the text, gentle humor helps make Python less intimidating.
  • A unified bibliography provides many suggestions for follow-up readings.

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ISBN: 9781071850657