Data Science Fundamentals with R, Python, and Open Data (Hardcover)
Language: English
Published by John Wiley & Sons Inc, New York, 2024
- Hardcover
- New
Seller: CitiRetail, Stevenage, United KingdomCitiRetail
AbeBooks seller since June 29, 2022
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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."-- This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. …
Seller Inventory # 9781394213245
- Title
- Data Science Fundamentals with R, Python, and Open Data (Hardcover)
- Author
- Marco Cremonini
- Publisher
- John Wiley & Sons Inc, New York
- Publication year
- 2024
- Condition
- new
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 1394213247
- ISBN 13
- 9781394213245
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.
"Synopsis" may belong to another edition of this title.
About the Author
Marco Cremonini is Assistant Professor with the Department of Social and Political Sciences at the University of Milan, Italy. He is Academic Editor and Board Member of PLOS ONE and his current research interests are focused on computational network and agent-based models of propagation and behavior.
"About the title" may belong to another edition of this title.
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