Hardback ISBN: 9781009588928
Expected Hardback publication date: 31 December 2025
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A Hands-On Introduction to Data Science with Python
2nd edition
, University of Washington
Coming soon
Online ISBN: 9781009588911
Expected online publication date:
December 2025
Paperback ISBN: 9781009588942
Expected Paperback publication date: 31 December 2025
- Textbook
Students will develop a practical understanding of data science with this hands-on textbook for introductory courses. This new edition is fully revised and updated, with numerous exercises and examples in the popular data science tool Python, a new chapter on using Python for statistical analysis, and a new chapter that demonstrates how to use Python within a range of cloud platforms. The many practice examples, drawn from real-life applications, range from small to big data and come to life in a new end-to-end project in Chapter 11. New 'Data Science in Practice' boxes highlight how concepts introduced work within an industry context and many chapters include new sections on AI and Generative AI. A suite of online material for instructors provides a strong supplement to the book, including lecture slides, solutions, additional assessment material and curriculum suggestions. Datasets and code are available for students online. This entry-level textbook is ideal for readers from a range of disciplines wishing to build a practical, working knowledge of data science.
Students will develop a practical understanding of data science with this hands-on textbook for introductory courses. This new edition is fully revised and updated, with numerous exercises and examples in the popular data science tool Python, a new chapter on using Python for statistical analysis, and a new chapter that demonstrates how to use Python within a range of cloud platforms. The many practice examples, drawn from real-life applications, range from small to big data and come to life in a new end-to-end project in Chapter 11. New 'Data Science in Practice' boxes highlight how concepts introduced work within an industry context and many chapters include new sections on AI and Generative AI. A suite of online material for instructors provides a strong supplement to the book, including lecture slides, solutions, additional assessment material and curriculum suggestions. Datasets and code are available for students online. This entry-level textbook is ideal for readers from a range of disciplines wishing to build a practical, working knowledge of data science.
Managerial Economics
Problem-Solving in a Digital World
2nd edition
, Richmond, The American International University in London
Online ISBN: 9781108989367
Online publication date:
09 February 2022
Hardback ISBN: 9781108839143
Hardback publication date: 07 April 2022
Paperback ISBN: 9781108984508
Paperback publication date: 07 April 2022
- Textbook
Managerial economics, meaning the application of economic methods in the managerial decision-making process, is a fundamental part of any business or management course. The current business environment presents managers with increasingly difficult decisions, amidst the Covid-19 pandemic and associated lockdowns, as well as the digital revolution and improved technology. Now in its second edition, this textbook features a new focus on how managerial economics has been transformed by the increasing importance of digitization within both the workplace and wider economy. It also features a new chapter on consumer theory, which emphasizes psychological factors and behavioural economics. Wilkinson adapts a user-friendly problem-solving approach to take the reader in gradual steps from simple problems through increasingly difficult material to complex case studies, demonstrating how to apply the principles of managerial economics to real-life situations. This book will be invaluable to business and economics students at both undergraduate and graduate levels.
Managerial economics, meaning the application of economic methods in the managerial decision-making process, is a fundamental part of any business or management course. The current business environment presents managers with increasingly difficult decisions, amidst the Covid-19 pandemic and associated lockdowns, as well as the digital revolution and improved technology. Now in its second edition, this textbook features a new focus on how managerial economics has been transformed by the increasing importance of digitization within both the workplace and wider economy. It also features a new chapter on consumer theory, which emphasizes psychological factors and behavioural economics. Wilkinson adapts a user-friendly problem-solving approach to take the reader in gradual steps from simple problems through increasingly difficult material to complex case studies, demonstrating how to apply the principles of managerial economics to real-life situations. This book will be invaluable to business and economics students at both undergraduate and graduate levels.
A Hands-On Introduction to Data Science with R
2nd edition
, University of Washington
Coming soon
Online ISBN: 9781009589024
Expected online publication date:
December 2025
Hardback ISBN: 9781009589079
Expected Hardback publication date: 31 December 2025
Paperback ISBN: 9781009589055
Expected Paperback publication date: 31 December 2025
- Textbook
Students will develop a practical understanding of data science with this hands-on textbook for introductory courses. This new edition is fully revised and updated, with numerous exercises and examples in the popular data science tool R, a new chapter on using R for statistical analysis, and a new chapter that demonstrates how to use R within a range of cloud platforms. The many practice examples, drawn from real-life applications, range from small to big data and come to life in a new end-to-end project in Chapter 11. New 'Data Science in Practice' boxes highlight how concepts introduced work within an industry context and many chapters include new sections on AI and Generative AI. A suite of online material for instructors provides a strong supplement to the book, including lecture slides, solutions, additional assessment material and curriculum suggestions. Datasets and code are available for students online. This entry-level textbook is ideal for readers from a range of disciplines wishing to build a practical, working knowledge of data science.
Students will develop a practical understanding of data science with this hands-on textbook for introductory courses. This new edition is fully revised and updated, with numerous exercises and examples in the popular data science tool R, a new chapter on using R for statistical analysis, and a new chapter that demonstrates how to use R within a range of cloud platforms. The many practice examples, drawn from real-life applications, range from small to big data and come to life in a new end-to-end project in Chapter 11. New 'Data Science in Practice' boxes highlight how concepts introduced work within an industry context and many chapters include new sections on AI and Generative AI. A suite of online material for instructors provides a strong supplement to the book, including lecture slides, solutions, additional assessment material and curriculum suggestions. Datasets and code are available for students online. This entry-level textbook is ideal for readers from a range of disciplines wishing to build a practical, working knowledge of data science.
Page 1 of 1