<p>For introductory-level Python programming and/or data-science courses.</p><h3>A groundbreaking, flexible approach to computer science and data science</h3><p>A subset of chapters from the "big book," <cite><strong>Introduction to Python for Computer Science and Data Science, Brief Version</strong></cite>, takes a unique approach to teaching and learning Python programming with an optional data science track in each chapter. Students learn Python by solving interesting problems using real-world datasets, with a focus on software engineering and program clarity. Jupyter Notebooks supplements, downloadable Python source code, and hundreds of hands-on examples, exercises and projects provide opportunities for students to test their skills.</p>

<div class="c-un-numbered-headers-and-contents-list__container"><h3>PART 1</h3><ul><li>CS: Python Fundamentals Quickstart</li><li>CS 1. Introduction to Computers and Python</li><li>DS Intro: AI–at the Intersection of CS and DS</li><li>CS 2. Introduction to Python Programming</li><li>DS Intro: Basic Descriptive Stats</li><li>CS 3. Control Statements and Program Development</li><li>DS Intro: Measures of Central Tendency—Mean, Median, Mode</li><li>CS 4. Functions</li><li>DS Intro: Basic Statistics— Measures of Dispersion</li><li>CS 5. Lists and Tuples</li><li>DS Intro: Simulation and Static Visualization</li></ul><h3>PART 2</h3><ul><li>CS: Python Data Structures, Strings and Files</li><li>CS 6. Dictionaries and Sets</li><li>DS Intro: Simulation and Dynamic Visualization</li><li>CS 7. Array-Oriented Programming with NumPy, High-Performance NumPy Arrays</li><li>DS Intro: Pandas Series and DataFrames</li><li>CS 8. Strings: A Deeper Look Includes Regular Expressions</li><li>DS Intro: Pandas, Regular Expressions and Data Wrangling</li><li>CS 9. Files and Exceptions</li><li>DS Intro: Loading Datasets from CSV Files into Pandas DataFrames</li></ul><h3>PART 3</h3><ul><li>CS: Python High-End Topics</li><li>CS 10. Object-Oriented Programming</li><li>DS Intro: Time Series and Simple Linear Regression</li><li>CS 11. Computer Science Thinking: Recursion, Searching, Sorting and Big O</li><li>CS and DS Other Topics Blog</li></ul></div>

<p>A subset of chapters from the "big book," <cite><strong>Introduction to Python for Computer Science and Data Science, Brief Version</strong></cite>, takes a unique, modular approach to teaching and learning introductory Python programming that is relevant for both computer science and data science audiences. The Deitels cover the most current topics and applications to prepare you for your subsequent courses and career. Interesting, real-world problems use real-world datasets, with a focus on software engineering and program clarity. Jupyter Notebooks supplements, downloadable Python source code, and hundreds of hands-on examples, exercises and projects provide opportunities for you to test and apply your skills as you learn. </p>

Paul Deitel

Harvey M. Deitel

Revel for Intro to Python for Computer Science and Data Science (Brief Version)

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