Artículos relacionados a Modern Statistics: A Computer-Based Approach with Python...
Modern Statistics: A Computer-Based Approach with Python (Statistics for Industry, Technology, and Engineering) - Tapa dura
Sinopsis
This innovative textbook presents material for a course on modern statistics that incorporates Python as a pedagogical and practical resource. Drawing on many years of teaching and conducting research in various applied and industrial settings, the authors have carefully tailored the text to provide an ideal balance of theory and practical applications. Numerous examples and case studies are incorporated throughout, and comprehensive Python applications are illustrated in detail. A custom Python package is available for download, allowing students to reproduce these examples and explore others.
Modern Statistics: A Computer-Based Approach with Python is intended for a one- or two-semester advanced undergraduate or graduate course. Because of the foundational nature of the text, it can be combined with any program requiring data analysis in its curriculum, such as courses on data science, industrial statistics, physical and social sciences, and engineering. Researchers, practitioners, and data scientists will also find it to be a useful resource with the numerous applications and case studies that are included.
A second, closely related textbook is titled Industrial Statistics: A Computer-Based Approach with Python. It covers topics such as statistical process control, including multivariate methods, the design of experiments, including computer experiments and reliability methods, including Bayesian reliability. These texts can be used independently or for consecutive courses.
The mistat Python package can be accessed at https://gedeck.github.io/mistat-code-solutions/ModernStatistics/
"In this book on Modern Statistics, the last two chapters on modern analytic methods contain what is very popular at the moment, especially in Machine Learning, such as classifiers, clustering methods and text analytics. But I also appreciate the previous chapters since I believe that people using machine learning methods should be aware that they rely heavily on statistical ones. I very much appreciate the many worked out cases, based on the longstanding experience of the authors. They are very useful to better understand, and then apply, the methods presented in the book. The use of Python corresponds to the best programming experience nowadays. For all these reasons, I think the book has also a brilliant and impactful future and I commend the authors for that."
Professor Fabrizio Ruggeri
Research Director at the National Research Council, Italy
President of the International Society for Business and Industrial Statistics (ISBIS)
Editor-in-Chief of Applied Stochastic Models in Business and Industry (ASMBI)
"Sinopsis" puede pertenecer a otra edición de este libro.
Acerca del autor
Professor Ron Kenett is Chairman of the KPA Group, Israel and Senior Research Fellow at the Samuel Neaman Institute, Technion, Haifa Israel and Professor, University of Turin, Italy. He is an applied statistician combining expertise in academic, consulting and business domains.
Shelemyahu Zacks is a Distinguished Professor emeritus in the Mathematical Sciences department of Binghamton University.He is a Fellow of the IMS, ASA, AAAS and an elected member of the ISI. Professor Zacks has published eleven books and more than 170 journal articles on subjects of design of experiments, statistical process control, statistical decision theory, sequential analysis, reliability and sampling from finite populations. Professor Zacks served as an Editor and Associate Editor of several Statistics and Probability journals.
Dr. Peter Gedeck, a Senior Data Scientist at Collaborative Drug Discovery, specializes in the development of machine learning algorithms to predict biological and physicochemical properties of drug candidates. In addition, he teaches data science at the University of Virginia and at statistics.com.
De la contraportada
This innovative textbook presents material for a course on modern statistics that incorporates Python as a pedagogical and practical resource. Drawing on many years of teaching and conducting research in various applied and industrial settings, the authors have carefully tailored the text to provide an ideal balance of theory and practical applications. Numerous examples and case studies are incorporated throughout, and comprehensive Python applications are illustrated in detail. A custom Python package is available for download, allowing students to reproduce these examples and explore others.
The first chapters of the text focus on analyzing variability, probability models, and distribution functions. Next, the authors introduce statistical inference and bootstrapping, and variability in several dimensions and regression models. The text then goes on to cover sampling for estimation of finite population quantities and time series analysis and prediction, concluding with two chapters on modern data analytic methods. Each chapter includes exercises, data sets, and applications to supplement learning.
Modern Statistics: A Computer-Based Approach with Python is intended for a one- or two-semester advanced undergraduate or graduate course. Because of the foundational nature of the text, it can be combined with any program requiring data analysis in its curriculum, such as courses on data science, industrial statistics, physical and social sciences, and engineering. Researchers, practitioners, and data scientists will also find it to be a useful resource with the numerous applications and case studies that are included.
A second, closely related textbook is titled Industrial Statistics: A Computer-Based Approach with Python. It covers topics such as statistical process control, including multivariate methods, the design of experiments, including computer experiments and reliability methods, including Bayesian reliability. These texts can be used independently or for consecutive courses
The mistat Python package can be accessed at https://gedeck.github.io/mistat-code-solutions/ModernStatistics/
"In this book on Modern Statistics, the last two chapters on modern analytic methods contain what is very popular at the moment, especially in Machine Learning, such as classifiers, clustering methods and text analytics. But I also appreciate the previous chapters since I believe that people using machine learning methods should be aware that they rely heavily on statistical ones. I very much appreciate the many worked out cases, based on the longstanding experience of the authors. They are very useful to better understand, and then apply, the methods presented in the book. The use of Python corresponds to the best programming experience nowadays. For all these reasons, I think the book has also a brilliant and impactful future and I commend the authors for that."
Professor Fabrizio Ruggeri
Research Director at the National Research Council, Italy
President of the International Society for Business and Industrial Statistics (ISBIS)
Editor-in-Chief of Applied Stochastic Models in Business and Industry (ASMBI)
"Sobre este título" puede pertenecer a otra edición de este libro.
- EditorialBirkhäuser
- Año de publicación2022
- ISBN 10 303107565X
- ISBN 13 9783031075650
- EncuadernaciónTapa dura
- IdiomaInglés
- Número de edición1
- Número de páginas464
- Contacto del fabricanteno disponible
Comprar nuevo
Ver este artículo
EUR 84,08
GRATIS gastos de envío desde Estados Unidos de America a España
Destinos, gastos y plazos de envío
Resultados de la búsqueda para Modern Statistics: A Computer-Based Approach with Python...
Imagen de archivo
Modern Statistics : A Computer-Based Approach with Python
Publicado por
Springer International Publishing, 2022
ISBN 10: 303107565X
ISBN 13: 9783031075650
Antiguo o usado
Tapa dura
Librería: Buchpark, Trebbin, Alemania
Calificación del vendedor: 5 de 5 estrellas
Condición: Hervorragend. Zustand: Hervorragend | Seiten: 464 | Sprache: Englisch | Produktart: Bücher. Nº de ref. del artículo: 40529413/1
Cantidad disponible: 2 disponibles
Imagen de archivo
Modern Statistics, A Computer-Based Approach with Python
Librería: Romtrade Corp., STERLING HEIGHTS, MI, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Condición: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide. Nº de ref. del artículo: ABNR-31124
Cantidad disponible: 2 disponibles
Imagen de archivo
MODERN STATISTICS
Librería: Speedyhen, London, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Condición: NEW. Nº de ref. del artículo: NW9783031075650
Cantidad disponible: 3 disponibles
Imagen de archivo
Modern Statistics
Librería: PBShop.store UK, Fairford, GLOS, Reino Unido
Calificación del vendedor: 4 de 5 estrellas
HRD. Condición: New. New Book. Shipped from UK. Established seller since 2000. Nº de ref. del artículo: GB-9783031075650
Cantidad disponible: 3 disponibles
Imagen de archivo
Modern Statistics: A Computer-Based Approach with Python (Statistics for Industry, Technology, and Engineering)
Librería: Books Puddle, New York, NY, Estados Unidos de America
Calificación del vendedor: 4 de 5 estrellas
Condición: New. 1st ed. 2022 edition NO-PA16APR2015-KAP. Nº de ref. del artículo: 26396293246
Cantidad disponible: 2 disponibles
Imagen de archivo
Modern Statistics: A Computer-Based Approach with Python (Statistics for Industry, Technology, and Engineering)
Librería: Majestic Books, Hounslow, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Condición: New. Nº de ref. del artículo: 401165217
Cantidad disponible: 2 disponibles
Imagen de archivo
Modern Statistics
Librería: PBShop.store US, Wood Dale, IL, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
HRD. Condición: New. New Book. Shipped from UK. Established seller since 2000. Nº de ref. del artículo: GB-9783031075650
Cantidad disponible: 3 disponibles
Imagen de archivo
Modern Statistics: A Computer-Based Approach with Python (Statistics for Industry, Technology, and Engineering)
Librería: Biblios, Frankfurt am main, HESSE, Alemania
Calificación del vendedor: 5 de 5 estrellas
Condición: New. Nº de ref. del artículo: 18396293236
Cantidad disponible: 4 disponibles
Imagen del vendedor
Modern Statistics : A Computer-based Approach With Python
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Condición: New. Nº de ref. del artículo: 44603496-n
Cantidad disponible: Más de 20 disponibles
Imagen de archivo
Modern Statistics, A Computer-Based Approach with Python
Librería: Basi6 International, Irving, TX, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Condición: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service. Nº de ref. del artículo: ABEJUNE24-18385
Cantidad disponible: 1 disponibles