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An Introduction to Data Science With Python
- Jeffrey S. Saltz - Syracuse University, USA
- Jeffrey M. Stanton - Syracuse University, USA
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.
Included with this title:
LMS Cartridge: Import this title’s instructor resources into your school’s learning management system (LMS) and save time. Don't use an LMS? You can still access all of the same online resources for this title via the password-protected Instructor Resource Site. Learn more.
Included with this title:
LMS Cartridge: Import this title’s instructor resources into your school’s learning management system (LMS) and save time. Don't use an LMS? You can still access all of the same online resources for this title via the password-protected Instructor Resource Site. Learn more.
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
Supplements
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.
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."
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."
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."
University of Maryland, College Park
"Useful, direct text for teaching data analysis using Python."
Eastern Kentucky University
"This book could expand our students' knowledge base and help them build new data analysis skills."
University of Connecticut
