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An Introduction to Statistical Learning
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods pr...
Critical Fabulations
A proposal to redefine design in a way that not only challenges the field's dominant paradigms but also changes the practice of design itself. In Critical Fabulations, Daniela Rosner proposes redefining design as investigative and activist, personal and culturally situated, responsive and responsible. Challenging the field's dominant paradigms and reinterpreting its history, Rosner wants to change the way we historicize the practice, reworking it from the inside. Focusing on the development of computational systems, she takes on powerful narratives of innovation and technology shaped by the professional expertise that has become integral to the field's mounting status within the new industrial economy. To do so, she intervenes in legacies of design, expanding what is considered "design" to include long-silenced narratives of practice, and enhancing existing design methodologies based on these rediscovered inheritances. Drawing on discourses of feminist technoscience, she examines craftwork's contributions to computing innovation--how craftwork becomes hardware manufacturing, and how hardware manufacturing becomes craftwork.
Refining the Concept of Scientific Inference When Working with Big Data
The concept of utilizing big data to enable scientific discovery has generated tremendous excitement and investment from both private and public sectors over the past decade, and expectations continue to grow. Using big data analytics to identify complex patterns hidden inside volumes of data that have never been combined could accelerate the rate of scientific discovery and lead to the development of beneficial technologies and products. However, producing actionable scientific knowledge from such large, complex data sets requires statistical models that produce reliable inferences (NRC, 2013). Without careful consideration of the suitability of both available data and the statistical model...
Evolution of Translational Omics
Technologies collectively called omics enable simultaneous measurement of an enormous number of biomolecules; for example, genomics investigates thousands of DNA sequences, and proteomics examines large numbers of proteins. Scientists are using these technologies to develop innovative tests to detect disease and to predict a patient's likelihood of responding to specific drugs. Following a recent case involving premature use of omics-based tests in cancer clinical trials at Duke University, the NCI requested that the IOM establish a committee to recommend ways to strengthen omics-based test development and evaluation. This report identifies best practices to enhance development, evaluation, and translation of omics-based tests while simultaneously reinforcing steps to ensure that these tests are appropriately assessed for scientific validity before they are used to guide patient treatment in clinical trials.
Starting Research in Clinical Education
Starting Research in Clinical Education A practical guide to clinical education research with top tips, common pitfalls and ethical issues. Starting Research in Clinical Education is written by a global team of experienced and emerging clinical education researchers who have a wealth of knowledge designing rigorous research projects and expertise in contemporary methods. Covering a broad spectrum of methods used by clinical education researchers, the book is split into five parts: research design, evidence synthesis and mixed methods research, qualitative research, quantitative research and succeeding in clinical education research. These sections are also accompanied by a companion website ...
Machine Learning Toolbox for Social Scientists
Machine Learning Toolbox for Social Scientists covers predictive methods with complementary statistical "tools" that make it mostly self-contained. The inferential statistics is the traditional framework for most data analytics courses in social science and business fields, especially in Economics and Finance. The new organization that this book offers goes beyond standard machine learning code applications, providing intuitive backgrounds for new predictive methods that social science and business students can follow. The book also adds many other modern statistical tools complementary to predictive methods that cannot be easily found in "econometrics" textbooks: nonparametric methods, data...
Data Science
Data Science: A First Introduction with Python focuses on using the Python programming language in Jupyter notebooks to perform data manipulation and cleaning, create effective visualizations, and extract insights from data using classification, regression, clustering, and inference. It emphasizes workflows that are clear, reproducible, and shareable, and includes coverage of the basics of version control. Based on educational research and active learning principles, the book uses a modern approach to Python and includes accompanying autograded Jupyter worksheets for interactive, self-directed learning. The text will leave readers well-prepared for data science projects. It is designed for l...
Machine Learning
Presents carefully selected supervised and unsupervised learning methods from basic to state-of-the-art,in a coherent statistical framework.
Approaches to Teaching The Plum in the Golden Vase (The Golden Lotus)
The Plum in the Golden Vase (also known as The Golden Lotus) was published in the early seventeenth century and may be the first long work of Chinese fiction written by a single (though anonymous) author. Featuring both complex structural elements and psychological and emotional realism, the novel centers on the rich merchant Ximen Qing and his household and describes the physical surroundings and material objects of a Ming Dynasty city. In part a social, political, and moral critique, the novel reflects on hierarchical power relations of family and state and the materialism of life at the time. The essays in this volume provide ideas for teaching the novel using a variety of approaches, from questions of genre, intertextuality, and the novel's reception to material culture, family and social dynamics, and power structures in sexual relations. Insights into the novel's representation of Buddhism, Chinese folk religion, legal culture, class, slavery, and obscenity are offered throughout the volume.
Practical Statistics for Data Scientists
Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why ...
