Textbook in PDF format 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 (AI). With their trademark humor and clear explanations, Saltz and Stanton provide a gentle introduction to this powerful Data Science tool. For this edition of the book, we have chosen Python and the Jupyter Notebook environment as the teaching and learning platforms. Chapter 1 provides a compact introduction to the language without intending to duplicate the many excellent novice resources available for Python learners. (Instructors who prefer teaching in R may want to consider Data Science for Business with R.) Python is a free and open-source language designed to be easy for nonprogrammers to learn and offering a robust ecosystem of packages for analysis, visualization, and data management. Many professional data scientists consider Python as the primary language of data science. Instructors can use any Python development environment that they prefer, but the use of Jupyter Notebooks makes it possible for instructors and students to create and modify Python code using virtually any web browser without having to install any software. As a benefit to students and instructors, we have provided one standalone Jupyter Notebook file for each chapter—you can find links to these notebooks on the publisher’s companion website along with sample slide decks and other instructional material. All of the datasets used in the notebooks are available from public repositories