 Название: Data Science Fundamentals with R, Python, and Open Data
Автор: Marco Cremonini
Издательство: Wiley
Год: 2024
Страниц: 480
Язык: английский
Формат: pdf (true), epub (true)
Размер: 12.8 MB
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.
R and Python, together and with the meaning just described, represent the knowledge to start approaching data science, carry out the first simple steps, complete the educational examples, get acquainted with real data, consider more advanced features, familiarize oneself with other real data, experiment with particular cases, analyze the logic behind mechanisms, gain experience with more complex real data, analyze online discussions on exceptional cases, look for data sources in the world of open data, think about the results to be obtained, even more sources of data now to put together, familiarize yourself with different data formats, with large datasets, with datasets that will drive you crazy before obtaining a workable version, and finally be ready to move to other technologies, other applications, uses, types of results, projects of ever-increasing complexity. This is the journey that starts here, and as discussed in the preface, it is within the reach of anyone who puts some effort and time into it. A single book, of course, cannot contain everything, but it can help to start, proceed in the right direction, and accompany for a while.
With this text, we will start from the elementary steps to gain speed quickly. We will use simplified teaching examples, but also immediately familiarize ourselves with the type of data that exists in reality, rather than in the unreality of the teaching examples. We will finish by addressing some elaborate examples, in which even the inconsistencies and errors that are part of daily reality will emerge, requiring us to find solutions.
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.
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Data Science Fundamentals with R, Python, and Open Data 
