An Introduction to Statistical Learning: with Applications in Python (Springer Texts in Statistics)
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An Introduction to Statistical Learning: with Applications in Python (Springer Texts in Statistics)
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning
An Introduction to Statistical Learning: with Applications in Python (Springer Texts in Statistics)
Nº de artículo: 74381285

An Introduction to Statistical Learning: with Applications in Python (Springer Texts in Statistics)

Nº de artículo: 74381285

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What Stands Out

Practical Applications
Focuses on real-world applications of statistical learning using Python, making it accessible for practitioners and students alike.
Comprehensive Coverage
Covers a vast range of statistical methods and machine learning techniques, providing a thorough foundation for learners at all levels.
User-Friendly Approach
Designed with clarity in mind, featuring detailed explanations and examples that simplify complex concepts for easier comprehension.

Detalles de producto

  • An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R(ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.
Publisher Springer
Publication date July 1, 2023
Edition 2023rd
Language English
Print length 622 pages
ISBN-10 3031387465
ISBN-13 978-3031387463
Item Weight 3.6 pounds (1.63 kg)
Dimensions 7.17 x 1.65 x 10.08 inches (18.2 x 4.2 x 25.6 cm)
Part of series Springer Texts in Statistics

Who Should Buy?

Suitable For
  • Beginner Students

    Ideal for students new to statistical learning and looking for clear explanations and practical applications using Python.

  • Data Scientists

    Useful for data scientists seeking to enhance their statistical analysis skills with hands-on Python projects and exercises.

  • Educators

    Great resource for instructors who want a comprehensive textbook for teaching statistical learning methodologies in Python.

Not Suitable For
  • Advanced Statisticians

    May not meet the needs of advanced statisticians looking for in-depth theoretical insights beyond practical applications.

  • Casual Readers

    Not suitable for those seeking light reading; the content requires focused study and engagement with statistical concepts.

  • Non-Python Users

    Readers unfamiliar with Python programming may struggle to grasp the applications and examples presented throughout the book.

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Preguntas y respuestas de los clientes

  • Pregunta: What topics are covered in 'An Introduction to Statistical Learning: with Applications in Python'?

    Respuesta: This book covers a comprehensive range of topics essential to statistical learning, including regression methods, classification, resampling methods, and variable selection. In addition, it delves into advanced concepts such as ensemble learning, tree-based methods, and support vector machines. Each topic is illustrated with practical examples using Python, making it a suitable resource for data enthusiasts and professionals looking to refine their analytical skills. By learning these techniques, readers can enhance their ability to make data-driven decisions in fields like finance, healthcare, and marketing.
  • Pregunta: Who is the target audience for this book?

    Respuesta: The book is primarily aimed at undergraduate and graduate students in statistics, data science, and related disciplines, as well as professionals who seek to understand practical applications of statistical learning techniques. It is written in an accessible manner, making it beneficial for individuals with a fundamental background in statistics or programming. Readers can enhance their statistical knowledge and apply these concepts in real-life scenarios, fostering a deeper understanding of data analysis and interpretation.
  • Pregunta: How does this edition differ from previous editions?

    Respuesta: The 2023rd edition includes updated content that reflects the latest developments and trends in statistical learning and data science. It features enhanced Python applications with more practical examples, improving the reader's ability to apply theorized concepts to real-world problems. New chapters may also be introduced, offering insights into current techniques and methodologies that have emerged since earlier editions, allowing readers to stay abreast of advancements in the field.
  • Pregunta: Is prior knowledge of programming required to understand this book?

    Respuesta: While having a basic understanding of programming can be beneficial, the book is designed for readers with varying levels of experience. It introduces core concepts of Python and provides step-by-step guidance on how to implement statistical techniques using the language. This approach allows beginners to grasp fundamental programming skills alongside statistical learning while giving more experienced programmers the tools to apply their knowledge effectively in data analysis contexts.
  • Pregunta: Can this book be useful for self-taught data scientists?

    Respuesta: Absolutely! 'An Introduction to Statistical Learning: with Applications in Python' is particularly useful for self-taught data scientists seeking a structured approach to learning statistical concepts and methodologies. With its clear explanations, practical examples, and hands-on exercises, learners can gradually build their expertise in key areas of statistical learning. This book serves as both a study guide and a reference, making it an invaluable resource for those pursuing a career in data science independently.
  • Pregunta: What are some practical applications of the techniques discussed in the book?

    Respuesta: The techniques covered in this book have wide-ranging applications across various industries. For instance, regression analysis can be used in finance for risk assessment and forecasting, while classification techniques are essential in healthcare for predicting patient outcomes. Moreover, machine learning algorithms discussed can enhance customer segmentation in marketing, optimize supply chains in logistics, and improve fraud detection in banking. These methodologies empower professionals to leverage data effectively for informed decision-making.
  • Pregunta: Are there any supplementary materials included with the book?

    Respuesta: Yes, the book often includes supplementary materials such as datasets, code snippets, and a companion website. These resources enhance the learning experience by providing hands-on practice and tools that readers can use to apply the concepts discussed in each chapter. They offer a practical avenue for experimenting with the techniques taught in the book, supporting deeper understanding and retention of the material as readers work through real datasets.
  • Pregunta: Does this book include practical exercises?

    Respuesta: Yes, 'An Introduction to Statistical Learning: with Applications in Python' includes numerous exercises at the end of each chapter. These exercises are designed to reinforce the concepts learned and encourage hands-on experience with statistical techniques using Python. By engaging in these exercises, readers can gain practical insights and deepen their understanding, preparing them for real-world applications of statistical learning in their professional careers or studies.
  • Pregunta: What is the significance of Python in this book?

    Respuesta: Python plays a central role in this book as it is one of the most widely used programming languages in data science and statistical analysis. The authors have chosen Python to demonstrate statistical concepts because of its readability, versatility, and robust libraries like NumPy, Pandas, and Scikit-learn. By learning statistical techniques through Python, readers can acquire valuable programming skills alongside their statistical knowledge, allowing for comprehensive data analysis and model development.
  • Pregunta: Where can I buy 'An Introduction to Statistical Learning: with Applications in Python' in Panama?

    Respuesta: You can purchase 'An Introduction to Statistical Learning: with Applications in Python, Springer Texts in Statistics 2023rd Edition' conveniently through Ubuy, which offers a wide selection of books and educational material. Ubuy provides a user-friendly platform for finding academic literature, and you can explore their range of titles to secure your copy. Enjoy a seamless shopping experience with Ubuy, making it your go-to for academic and professional resources.

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PAB 83

PAB 167

Haz tu pedido ahora y recíbelo por ahí Martes, Julio 21

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características y beneficios

  • Accessible overview of statistical learning
  • Covers important modeling and prediction techniques
  • Includes real-world examples
  • Targeted at statisticians and non-statisticians
  • Python-based alternative to ISLR
  • Useful labs for novices and experienced users

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