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Implementing Reproducible Research
In computational science, reproducibility requires that researchers make code and data available to others so that the data can be analyzed in a similar manner as in the original publication. Code must be available to be distributed, data must be accessible in a readable format, and a platform must be available for widely distributing the data and code. In addition, both data and code need to be licensed permissively enough so that others can reproduce the work without a substantial legal burden. Implementing Reproducible Research covers many of the elements necessary for conducting and distributing reproducible research. It explains how to accurately reproduce a scientific result. Divided i...
Computational Modeling by Case Study
Mathematical models power the modern world; they allow us to design safe buildings, investigate changes to the climate, and study the transmission of diseases through a population. However, all models are uncertain: building contractors deviate from the planned design, humans impact the climate unpredictably, and diseases mutate and change. Modern advances in mathematics and statistics provide us with techniques to understand and quantify these sources of uncertainty, allowing us to predict and design with confidence. This book presents a comprehensive treatment of uncertainty: its conceptual nature, techniques to quantify uncertainty, and numerous examples to illustrate sound approaches. Several case studies are discussed in detail to demonstrate an end-to-end treatment of scientific modeling under uncertainty, including framing the problem, building and assessing a model, and answering meaningful questions. The book illustrates a computational approach with the Python package Grama, presenting fully reproducible examples that students and practitioners can quickly adapt to their own problems.
Proceedings of the 8th Python in Science Conference
The proceedings of the 8th annual Python for Scientific Computing conference.
Essays On Trading Strategy
This book directly focuses on finding optimal trading strategies in the real world and supports that with a well-defined theoretical foundation that allows trading strategy problems to be solved. Critically, it also delivers a menu of actual solutions that can be applied by traders with various risk profiles and objectives in markets that exhibit substantial tail risk. It shows how the Markowitz approach leads to excessive risk taking, and trader underperformance, in the real world. It summarizes the key features of Utility Theory, the deficiencies of the Sharpe Ratio as a statistic, and develops an optimal decision theory with fully developed examples for both 'Normal' and leptokurtotic distributions.
The Practice of Reproducible Research
The Practice of Reproducible Research presents concrete examples of how researchers in the data-intensive sciences are working to improve the reproducibility of their research projects. In each of the thirty-one case studies in this volume, the author or team describes the workflow that they used to complete a real-world research project. Authors highlight how they utilized particular tools, ideas, and practices to support reproducibility, emphasizing the very practical how, rather than the why or what, of conducting reproducible research. Part 1 provides an accessible introduction to reproducible research, a basic reproducible research project template, and a synthesis of lessons learned from across the thirty-one case studies. Parts 2 and 3 focus on the case studies themselves. The Practice of Reproducible Research is an invaluable resource for students and researchers who wish to better understand the practice of data-intensive sciences and learn how to make their own research more reproducible.
Data Science for Neuroimaging
Data science methods and tools—including programming, data management, visualization, and machine learning—and their application to neuroimaging research As neuroimaging turns toward data-intensive discovery, researchers in the field must learn to access, manage, and analyze datasets at unprecedented scales. Concerns about reproducibility and increased rigor in reporting of scientific results also demand higher standards of computational practice. This book offers neuroimaging researchers an introduction to data science, presenting methods, tools, and approaches that facilitate automated, reproducible, and scalable analysis and understanding of data. Through guided, hands-on explorations...
Tree-Based Methods for Statistical Learning in R
Tree-based Methods for Statistical Learning in R provides a thorough introduction to both individual decision tree algorithms (Part I) and ensembles thereof (Part II). Part I of the book brings several different tree algorithms into focus, both conventional and contemporary. Building a strong foundation for how individual decision trees work will help readers better understand tree-based ensembles at a deeper level, which lie at the cutting edge of modern statistical and machine learning methodology. The book follows up most ideas and mathematical concepts with code-based examples in the R statistical language; with an emphasis on using as few external packages as possible. For example, user...
Open Knowledge Institutions
The future of the university as an open knowledge institution that institutionalizes diversity and contributes to a common resource of knowledge: a manifesto. In this book, a diverse group of authors—including open access pioneers, science communicators, scholars, researchers, and university administrators—offer a bold proposition: universities should become open knowledge institutions, acting with principles of openness at their center and working across boundaries and with broad communities to generate shared knowledge resources for the benefit of humanity. Calling on universities to adopt transparent protocols for the creation, use, and governance of these resources, the authors draw ...
Data Science Using Python and R
Learn data science by doing data science! Data Science Using Python and R will get you plugged into the world’s two most widespread open-source platforms for data science: Python and R. Data science is hot. Bloomberg called data scientist “the hottest job in America.” Python and R are the top two open-source data science tools in the world. In Data Science Using Python and R, you will learn step-by-step how to produce hands-on solutions to real-world business problems, using state-of-the-art techniques. Data Science Using Python and R is written for the general reader with no previous analytics or programming experience. An entire chapter is dedicated to learning the basics of Python a...
