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Hardcover. Condizione: Very Good. 1. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.

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Rilegato. Condizione: new. A cura di Foschi P., Paltrinieri G. e Tampellini A.Bologna, 2025; ril., pp. 344, cm 17x24. La rivista "Strada Maestra" è la storica rivista della Biblioteca comunale "G. C. Croce" di San Giovanni in Persiceto fondata nel 1968 dal professor Mario Gandini. Gli articoli in questo numero: - I Pepoli e i loro mulini. Un approfondimento sui mulini di Valbona, macchine protoindustriali strategicamente collocate in posizione centrale fra i territori e le comunità di Crevalcore, Sant'Agata e San Giovanni in Persiceto (secoli XIII-XIX) (Paola Foschi) -La emptio Melchioris Bencevenni, fabro lignario di San Giovanni in Persiceto. 'Frammenti' di vita persicetana in un atto di compravendita del 31 ottobre 1583 (Alberto Tampellini) - Il Tempo in piazza a San Giovanni in Persiceto segnato dall'orologio solare più grande del mondo entrato nel Guinness World Records (2023) (Giovanni Paltrinieri, Romano Serra) - I muri raccontano. Alcune riflessioni ispirate dai murali dell'Istituto di istruzione superiore 'Archimede' di San Giovanni in Persiceto (Monica Mazzacori) - Di Bertoldo e di altro. Breve rassegna delle manifestazioni culturali per il 150° anniversario del carnevale storico persicetano (1874-2024) (Pierangelo Pancaldi) - Il patrimonio culturale della moda: l'Archivio Renato Manzoni (Silvia Monfredini) -Introduzione allo studio di Silvia Monfredini dedicato al più antico e consistente caso di archivio calzaturiero presente nella regione Emilia-Romagna (Elisa Tosi Brandi). Libro.

Hardcover. Condizione: New.

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Condizione: As New. Unread book in perfect condition.

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Hardcover. Condizione: new. Hardcover. Data Science Fundamentals with R, Python, and Open Data Introduction to essential concepts and techniques of the fundamentals of R and Python needed to start data science projects Organized with a strong focus on open data, Data Science Fundamentals with R, Python, and Open Data discusses concepts, techniques, tools, and first steps to carry out data science projects, with a focus on Python and RStudio, reflecting a clear industry trend emerging towards the integration of the two. The text examines intricacies and inconsistencies often found in real data, explaining how to recognize them and guiding readers through possible solutions, and enables readers to handle real data confidently and apply transformations to reorganize, indexing, aggregate, and elaborate. This book is full of reader interactivity, with a companion website hosting supplementary material including datasets used in the examples and complete running code (R scripts and Jupyter notebooks) of all examples. Exam-style questions are implemented and multiple choice questions to support the readers active learning. Each chapter presents one or more case studies. Written by a highly qualified academic, Data Science Fundamentals with R, Python, and Open Data discuss sample topics such as: Data organization and operations on data frames, covering reading CSV dataset and common errors, and slicing, creating, and deleting columns in R Logical conditions and row selection, covering selection of rows with logical condition and operations on dates, strings, and missing values Pivoting operations and wide form-long form transformations, indexing by groups with multiple variables, and indexing by group and aggregations Conditional statements and iterations, multicolumn functions and operations, data frame joins, and handling data in list/dictionary format Data Science Fundamentals with R, Python, and Open Data is a highly accessible learning resource for students from heterogeneous disciplines where Data Science and quantitative, computational methods are gaining popularity, along with hard sciences not closely related to computer science, and medical fields using stochastic and quantitative models. "Data science is an interdisciplinary academic field that uses statistics, scientific computing, scientific methods, processes, algorithms and systems to extract or extrapolate knowledge and insights from noisy, structured, and unstructured data."-- Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Condizione: As New. Unread book in perfect condition.

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Condizione: As New. Unread book in perfect condition.

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Condizione: As New. Unread book in perfect condition.

Hardback. Condizione: New. New copy - Usually dispatched within 4 working days.

Condizione: New. 2024. 1st Edition. hardcover. . . . . .

Hardcover. Condizione: new. Hardcover. Data Science Fundamentals with R, Python, and Open Data Introduction to essential concepts and techniques of the fundamentals of R and Python needed to start data science projects Organized with a strong focus on open data, Data Science Fundamentals with R, Python, and Open Data discusses concepts, techniques, tools, and first steps to carry out data science projects, with a focus on Python and RStudio, reflecting a clear industry trend emerging towards the integration of the two. The text examines intricacies and inconsistencies often found in real data, explaining how to recognize them and guiding readers through possible solutions, and enables readers to handle real data confidently and apply transformations to reorganize, indexing, aggregate, and elaborate. This book is full of reader interactivity, with a companion website hosting supplementary material including datasets used in the examples and complete running code (R scripts and Jupyter notebooks) of all examples. Exam-style questions are implemented and multiple choice questions to support the readers active learning. Each chapter presents one or more case studies. Written by a highly qualified academic, Data Science Fundamentals with R, Python, and Open Data discuss sample topics such as: Data organization and operations on data frames, covering reading CSV dataset and common errors, and slicing, creating, and deleting columns in R Logical conditions and row selection, covering selection of rows with logical condition and operations on dates, strings, and missing values Pivoting operations and wide form-long form transformations, indexing by groups with multiple variables, and indexing by group and aggregations Conditional statements and iterations, multicolumn functions and operations, data frame joins, and handling data in list/dictionary format Data Science Fundamentals with R, Python, and Open Data is a highly accessible learning resource for students from heterogeneous disciplines where Data Science and quantitative, computational methods are gaining popularity, along with hard sciences not closely related to computer science, and medical fields using stochastic and quantitative models. "Data science is an interdisciplinary academic field that uses statistics, scientific computing, scientific methods, processes, algorithms and systems to extract or extrapolate knowledge and insights from noisy, structured, and unstructured data."-- Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Hardback. Condizione: New. New copy - Usually dispatched within 4 working days.

Condizione: New. 1st edition NO-PA16APR2015-KAP.

Hardback. Condizione: New. Communicate the data that is powering our changing world with this essential text The advent of machine learning and neural networks in recent years, along with other technologies under the broader umbrella of 'artificial intelligence,' has produced an explosion in Data Science research and applications. Data Visualization, which combines the technical knowledge of how to work with data and the visual and communication skills required to present it, is an integral part of this subject. The expansion of Data Science is already leading to greater demand for new approaches to Data Visualization, a process that promises only to grow. Data Visualization in R and Python offers a thorough overview of the key dimensions of this subject. Beginning with the fundamentals of data visualization with Python and R, two key environments for data science, the book proceeds to lay out a range of tools for data visualization and their applications in web dashboards, data science environments, graphics, maps, and more. With an eye towards remarkable recent progress in open-source systems and tools, this book offers a cutting-edge introduction to this rapidly growing area of research and technological development. Data Visualization in R and Python readers will also find: Coverage suitable for anyone with a foundational knowledge of R and PythonDetailed treatment of tools including the Ggplot2, Seaborn, and Altair libraries, Plotly/Dash, Shiny, and othersCase studies accompanying each chapter, with full explanations for data operations and logic for each, based on Open Data from many different sources and of different formats Data Visualization in R and Python is ideal for any student or professional looking to understand the working principles of this key field.

Hardcover. Condizione: new. Hardcover. Communicate the data that is powering our changing world with this essential text The advent of machine learning and neural networks in recent years, along with other technologies under the broader umbrella of artificial intelligence, has produced an explosion in Data Science research and applications. Data Visualization, which combines the technical knowledge of how to work with data and the visual and communication skills required to present it, is an integral part of this subject. The expansion of Data Science is already leading to greater demand for new approaches to Data Visualization, a process that promises only to grow. Data Visualization in R and Python offers a thorough overview of the key dimensions of this subject. Beginning with the fundamentals of data visualization with Python and R, two key environments for data science, the book proceeds to lay out a range of tools for data visualization and their applications in web dashboards, data science environments, graphics, maps, and more. With an eye towards remarkable recent progress in open-source systems and tools, this book offers a cutting-edge introduction to this rapidly growing area of research and technological development. Data Visualization in R and Python readers will also find: Coverage suitable for anyone with a foundational knowledge of R and PythonDetailed treatment of tools including the Ggplot2, Seaborn, and Altair libraries, Plotly/Dash, Shiny, and othersCase studies accompanying each chapter, with full explanations for data operations and logic for each, based on Open Data from many different sources and of different formats Data Visualization in R and Python is ideal for any student or professional looking to understand the working principles of this key field. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Hardcover. Condizione: Brand New. 256 pages. 10.24x7.24x1.26 inches. In Stock.

Condizione: New. 2024. 1st Edition. hardcover. . . . . .

Condizione: New. pp. 576 1st edition NO-PA16APR2015-KAP.