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Telling Stories with Data
Extensive code examples. Ethics integrated throughout. Reproducibility integrated throughout. Focus on data gathering, messy data, and cleaning data. Extensive formative assessment throughout.
Natural Language Processing in the Real World
Natural Language Processing in the Real World is a practical guide for applying data science and machine learning to build Natural Language Processing (NLP) solutions. Where traditional, academic-taught NLP is often accompanied by a data source or dataset to aid solution building, this book is situated in the real world where there may not be an existing rich dataset. This book covers the basic concepts behind NLP and text processing and discusses the applications across 15 industry verticals. From data sources and extraction to transformation and modelling, and classic Machine Learning to Deep Learning and Transformers, several popular applications of NLP are discussed and implemented. This book provides a hands-on and holistic guide for anyone looking to build NLP solutions, from students of Computer Science to those involved in large-scale industrial projects.
Data Science for Sensory and Consumer Scientists
Data Science for Sensory and Consumer Scientists is a comprehensive textbook that provides a practical guide to using data science in the field of sensory and consumer science through real-world applications. It covers key topics including data manipulation, preparation, visualization, and analysis, as well as automated reporting, machine learning, text analysis, and dashboard creation. Written by leading experts in the field, this book is an essential resource for anyone looking to master the tools and techniques of data science and apply them to the study of consumer behavior and sensory-led product development. Whether you are a seasoned professional or a student just starting out, this book is the ideal guide to using data science to drive insights and inform decision-making in the sensory and consumer sciences. Key Features: • Elucidation of data scientific workflow. • Introduction to reproducible research. • In-depth coverage of data-scientific topics germane to sensory and consumer science. • Examples based in industrial practice used throughout the book
Data Science in Practice
Data Science in Practice is the ideal introduction to data science. With or without math skills, here, you get the all-round view that you need for your projects. This book describes how to properly question data, in order to unearth the treasure that data can be. You will get to know the relevant analysis methods, and will be introduced to the programming language R, which is ideally suited for data analysis. Associated tools like notebooks that make data science programming easily accessible are included in this introduction. Because technology alone is not enough, this book also deals with problems in project implementation, illuminates various fields of application, and does not forget t...
Big Data Analytics
Successfully navigating the data-driven economy presupposes a certain understanding of the technologies and methods to gain insights from Big Data. This book aims to help data science practitioners to successfully manage the transition to Big Data. Building on familiar content from applied econometrics and business analytics, this book introduces the reader to the basic concepts of Big Data Analytics. The focus of the book is on how to productively apply econometric and machine learning techniques with large, complex data sets, as well as on all the steps involved before analysing the data (data storage, data import, data preparation). The book combines conceptual and theoretical material wi...
The Cambridge Companion to Alexander the Great
Has any ancient figure captivated the imagination of people over the centuries so much as Alexander the Great? In less than a decade he created an empire stretching across much of the Near East as far as India, which led to Greek culture becoming dominant in much of this region for a millennium. Here, an international team of experts clearly explains the life and career of one of the most significant figures in world history. They introduce key themes of his campaign as well as describing aspects of his court and government and exploring the very different natures of his engagements with the various peoples he encountered and their responses to him. The reader is also introduced to the key sources, including the more important fragmentary historians, especially Ptolemy, Aristobulus and Clitarchus, with their different perspectives. The book closes by considering how Alexander's image was manipulated in antiquity itself.
Spatial Statistics for Data Science
Spatial data is crucial to improve decision-making in a wide range of fields including environment, health, ecology, urban planning, economy, and society. Spatial Statistics for Data Science: Theory and Practice with R describes statistical methods, modeling approaches, and visualization techniques to analyze spatial data using R. The book provides a comprehensive overview of the varying types of spatial data, and detailed explanations of the theoretical concepts of spatial statistics, alongside fully reproducible examples which demonstrate how to simulate, describe, and analyze spatial data in various applications. Combining theory and practice, the book includes real-world data science exa...
R for Data Science
Use R to turn data into insight, knowledge, and understanding. With this practical book, aspiring data scientists will learn how to do data science with R and RStudio, along with the tidyverse—a collection of R packages designed to work together to make data science fast, fluent, and fun. Even if you have no programming experience, this updated edition will have you doing data science quickly. You'll learn how to import, transform, and visualize your data and communicate the results. And you'll get a complete, big-picture understanding of the data science cycle and the basic tools you need to manage the details. Updated for the latest tidyverse features and best practices, new chapters show you how to get data from spreadsheets, databases, and websites. Exercises help you practice what you've learned along the way. You'll understand how to: Visualize: Create plots for data exploration and communication of results Transform: Discover variable types and the tools to work with them Import: Get data into R and in a form convenient for analysis Program: Learn R tools for solving data problems with greater clarity and ease Communicate: Integrate prose, code, and results with Quarto
