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A practical guide to data-intensive humanities research using the Python programming language
The use of quantitative methods in the humanities and related social sciences has increased considerably in recent years, allowing researchers to discover patterns in a vast range of source materials. Despite this growth, there are few resources addressed to students and scholars who wish to take advantage of these powerful tools. Humanities Data Analysis offers the first intermediate-level guide to quantitative data analysis for humanities students and scholars using the Python programming language. This practical textbook, which assumes a basic knowledge of Python, teaches readers the necessary skills for conducting humanities research in the rapidly developing digital environment.
The book begins with an overview of the place of data science in the humanities,
A practical guide to data-intensive humanities research using the Python programming languageThe use of quantitative methods in the humanities and related social sciences has increased considerably in recent years, allowing researchers to discover patterns in a vast range of source materials
Augmenting Network Analysis with Linked Data for Humanities Research.- A Memorial Design Pattern Catalogue for commemorative digital culture.- Annotation in Digital Humanities.- Establishment of a complex database for the study of cultural her-itage through the reading and analysis of the
This case study-based textbook in multivariate analysis for advanced students in the humanities emphasizes descriptive, exploratory analyses of various types of datasets from a wide range of sub-disciplines, promoting the use of multivariate analysis and illustrating its wide applicability. Fields
See How Graphics Reveal InformationGraphical Data Analysis with R shows you what information you can gain from graphical displays. The book focuses on why you draw graphics to display data and which graphics to draw (and uses R to do so). All the datasets are available in R or one of its packages
Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan, Second Edition provides an accessible approach for conducting Bayesian data analysis, as material is explained clearly with concrete examples. Included are step-by-step instructions on how to carry out Bayesian data analyses in the
Table of Contents1. IntroductionHow to use this bookInstalling iPython NotebookWhat is iPython notebook?What is Anaconda?Getting StartedGetting the datasets for the workbook's exercises2. Getting Data into and out of PythonLoading Data from CSV FilesSaving Data to CSVLoading Data from Excel
Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python presents an applied approach to data mining concepts and methods, using Python software for illustration Readers will learn how to implement a variety of popular data mining algorithms in Python (a free and
Broadening its scope to nonstatisticians, Bayesian Methods for Data Analysis, Third Edition provides an accessible introduction to the foundations and applications of Bayesian analysis. Along with a complete reorganization of the material, this edition concentrates more on hierarchical Bayesian
Combining a modern, data-analytic perspective with a focus on applications in the social sciences, the Third Edition of Applied Regression Analysis and Generalized Linear Models provides in-depth coverage of regression analysis, generalized linear models, and closely related methods, such as
The revised second edition of this textbook provides the reader with a solid foundation in probability theory and statistics as applied to the physical sciences, engineering and related fields. It covers a broad range of numerical and analytical methods that are essential for the correct analysis
The State of the Art in Transcriptome Analysis RNA sequencing (RNA-seq) data offers unprecedented information about the transcriptome, but harnessing this information with bioinformatics tools is typically a bottleneck. RNA-seq Data Analysis: A Practical Approach enables researchers to examine
Why does the World Need--Excel Data Analysis, Modeling, and Simulation? When spreadsheets ?rst became widely available in the early 1980s, it spawned a revolution in teaching. What previously could only be done with arcane software and large scale computing was now available to the common-man, on a
Offers a clear and concise introduction to the economics and regulation of securities markets, with a single-minded focus on disclosure and the economics of disclosure. The book is concise, easy to read, and student
Complex Analysis: A First Course with Applications is a truly accessible introduction to the fundamental principles and applications of complex analysis. Designed for the undergraduate student with a calculus background but no prior experience with complex analysis, this text discusses the theory
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Contains primary data and lessons learned for policy makers Describes case studies useful as laboratory and field preparation materials Includes chapters by authors from different continents appealing to classroom teaching and to general audiences far beyond
Learn to produce quality radiographs on the first try with Radiographic Image Analysis, 5th Edition. This updated, user-friendly text reflects the latest ARRT guidelines and revamped chapters to reflect the latest digital technology. Chapters walk you through the steps of how to carefully evaluate
The question of ethics has dominated recent developments within the humanities. This volume brings together the most recent theories of ethics and reading and applies them to a wide variety of literary texts. Ethical and literary issues explored by the contributors include biography, sensibility,
This book discusses the statistical analysis of clustered survival data. Such data are encountered in many scientific disciplines including human and veterinary medicine, biology, epidemiology, public health and demography. A typical example is the time to death in cancer patients, with patients
Winner of the 2016 De Groot Prize from the International Society for Bayesian Analysis Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data
A practical reference and teaching book of case studies for nutrition practitioners and other healthcare professionals, demonstrating how to apply the latest evidence-informed principles of personalized nutrition. Covering a range of complex cases such as autoimmunity and inflammation, hormonal