For CS1 courses in Python Programming (including majors and non-majors). A problem-solving approach to programming with Python. The Practice of Computing Using Python introduces CS1 students (majors and non-majors) to computational thinking using Python.With data-manipulation as a theme, students quickly see the value in what they’re learning and leave the course with a set of immediately useful computational skills that can be applied to problems they encounter in future pursuits. The book takes an “object-use-first” approach–writing classes is covered only after students have mastered using objects. This edition is available with MyProgrammingLab, an innovative online homework and assessment tool. Through the power of practice and immediate personalized feedback, MyProgrammingLab helps students fully grasp the logic, semantics, and syntax of programming. Note: If you are purchasing the standalone text or electronic version, MyProgrammingLab does not come automatically packaged with the text. To purchase MyProgrammingLab, please visit: myprogramminglab.com or you can purchase a package of the physical text + MyProgrammingLab by searching for ISBN 10: 0132992833 / ISBN 13: 9780132992831.MyProgrammingLab is not a self-paced technology and should only be purchased when required by an instructor

For courses in Python Programming Introduces Python programming with an emphasis on problem-solving Now in its Third Edition, Practice of Computing Using Python continues to effectively introduce readers to computational thinking using Python, with a strong emphasis on problem solving through computer science. The authors have chosen Python for its simplicity, powerful built-in data structures, advanced control constructs, and practicality. The text is built from the ground up for Python programming, rather than having been translated from Java or C++. Focusing on data manipulation and analysis as a theme, the text allows readers to work on real problems using Internet-sourced or self-generated data sets that represent their own work and interests. The authors also emphasize program development and provide readers of all backgrounds with a practical foundation in programming that suit their needs. Among other changes, the Third Edition incorporates a switch to the Anaconda distribution, the SPYDER IDE, and a focus on debugging and GUIs. Also available with MyProgrammingLab(TM) MyProgrammingLab is an online learning system designed to engage students and improve results. MyProgrammingLab consists of a set of programming exercises correlated to specific Pearson CS1/Intro to Programming textbooks. Through practice exercises and immediate, personalized feedback, MyProgrammingLab improves the programming competence of beginning students who often struggle with the basic concepts of programming languages. Note: You are purchasing a standalone product; MyLab(TM) & Mastering(TM) does not come packaged with this content. Students, if interested in purchasing this title with MyLab & Mastering, ask your instructor for the correct package ISBN and Course ID. Instructors, contact your Pearson representative for more information. If you would like to purchase boththe physical text and MyLab & Mastering, search for: 0134520513 / 9780134520513 The Practice of Computing Using Python plus MyProgrammingLab with Pearson eText -- Access Card Package, 3/e Package consists of: 0134381327 / 9780134381329 MyProgrammingLab with Pearson eText -- Access Card Package 0134379764 / 9780134379760 The Practice of Computing Using Python, 3/e

Perkovic's Introduction to Programming Using Python is more than just an introduction to programming. It is an inclusive introduction to Computer Science that takes the pedagogical approach of "the right tool for the job at the right moment," and focuses on application development. The approach is hands-on and problem-oriented, with practice problems and solutions appearing throughout the text. The text is imperative-first, but does not shy away from discussing objects early where appropriate. Discussions of user-defined classes and Object-Oriented Programming appear later in the text, when students have more background and concepts can be motivated. Chapters include an introduction to problem solving techniques and classical algorithms, problem-solving and programming and ways to apply core skills to application development.

The new edition of an introductory text that teaches students the art of computational problem solving, covering topics ranging from simple algorithms to information visualization. This book introduces students with little or no prior programming experience to the art of computational problem solving using Python and various Python libraries, including PyLab. It provides students with skills that will enable them to make productive use of computational techniques, including some of the tools and techniques of data science for using computation to model and interpret data. The book is based on an MIT course (which became the most popular course offered through MIT's OpenCourseWare) and was developed for use not only in a conventional classroom but in in a massive open online course (MOOC). This new edition has been updated for Python 3, reorganized to make it easier to use for courses that cover only a subset of the material, and offers additional material including five new chapters. Students are introduced to Python and the basics of programming in the context of such computational concepts and techniques as exhaustive enumeration, bisection search, and efficient approximation algorithms. Although it covers such traditional topics as computational complexity and simple algorithms, the book focuses on a wide range of topics not found in most introductory texts, including information visualization, simulations to model randomness, computational techniques to understand data, and statistical techniques that inform (and misinform) as well as two related but relatively advanced topics: optimization problems and dynamic programming. This edition offers expanded material on statistics and machine learning and new chapters on Frequentist and Bayesian statistics.

Computing is revolutionizing the practice of biology. This book, which assumes no prior computing experience, provides students with the tools to write their own Python programs and to understand fundamental concepts in computational biology and bioinformatics. Each major part of the book begins with a compelling biological question, followed by the algorithmic ideas and programming tools necessary to explore it: the origins of pathogenicity are examined using gene finding, the evolutionary history of sex determination systems is studied using sequence alignment, and the origin of modern humans is addressed using phylogenetic methods. In addition to providing general programming skills, this book explores the design of efficient algorithms, simulation, NP-hardness, and the maximum likelihood method, among other key concepts and methods. Easy-to-read and designed to equip students with the skills to write programs for solving a range of biological problems, the book is accompanied by numerous programming exercises, available at www.cs.hmc.edu/CFB.

The book serves as a first introduction to computer programming of scientific applications, using the high-level Python language. The exposition is example and problem-oriented, where the applications are taken from mathematics, numerical calculus, statistics, physics, biology and finance. The book teaches "Matlab-style" and procedural programming as well as object-oriented programming. High school mathematics is a required background and it is advantageous to study classical and numerical one-variable calculus in parallel with reading this book. Besides learning how to program computers, the reader will also learn how to solve mathematical problems, arising in various branches of science and engineering, with the aid of numerical methods and programming. By blending programming, mathematics and scientific applications, the book lays a solid foundation for practicing computational science. From the reviews: Langtangen ... does an excellent job of introducing programming as a set of skills in problem solving. He guides the reader into thinking properly about producing program logic and data structures for modeling real-world problems using objects and functions and embracing the object-oriented paradigm. ... Summing Up: Highly recommended. F. H. Wild III, Choice, Vol. 47 (8), April 2010 Those of us who have learned scientific programming in Python ‘on the streets’ could be a little jealous of students who have the opportunity to take a course out of Langtangen’s Primer.” John D. Cook, The Mathematical Association of America, September 2011 This book goes through Python in particular, and programming in general, via tasks that scientists will likely perform. It contains valuable information for students new to scientific computing and would be the perfect bridge between an introduction to programming and an advanced course on numerical methods or computational science. Alex Small, IEEE, CiSE Vol. 14 (2), March /April 2012 “This fourth edition is a wonderful, inclusive textbook that covers pretty much everything one needs to know to go from zero to fairly sophisticated scientific programming in Python...” Joan Horvath, Computing Reviews, March 2015

Easy to understand and fun to read, this updated edition of Introducing Python is ideal for beginning programmers as well as those new to the language. Author Bill Lubanovic takes you from the basics to more involved and varied topics, mixing tutorials with cookbook-style code recipes to explain concepts in Python 3. End-of-chapter exercises help you practice what you’ve learned. You’ll gain a strong foundation in the language, including best practices for testing, debugging, code reuse, and other development tips. This book also shows you how to use Python for applications in business, science, and the arts, using various Python tools and open source packages.