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hardcover. Zustand: Very Good. Cover and edges may have some wear.

HRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Zustand: NEW.

Hardcover. Zustand: New.

Zustand: New.

HRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Zustand: New. In English.

Zustand: New. In English.

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

Zustand: New. 2024. 1st Edition. hardcover. . . . . . Books ship from the US and Ireland.

Zustand: New. 2024. 1st Edition. hardcover. . . . . . Books ship from the US and Ireland.

Buch. Zustand: Neu. Neuware - 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 not Elektronisches Buch) 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.…

Buch. Zustand: Neu. Neuware - 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 Python- Detailed treatment of tools including the Ggplot2, Seaborn, and Altair libraries, Plotly/Dash, Shiny, and others- Case 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.…