Computational approaches offer exciting opportunities for us to do social science differently. This beginner’s guide discusses a range of computational methods and how to use them to study the problems and questions you want to research. It assumes no knowledge of programming, offering step-by-step guidance for coding in Python and drawing on examples of real data analysis to demonstrate how you can apply each approach in any discipline. The book also: Considers important principles of social scientific computing, including transparency, accountability and reproducibility. Understands the realities of completing research projects and offers advice for dealing with issues such as messy or incomplete data and systematic biases. Empowers you to learn at your own pace, with online resources including screencast tutorials and datasets that enable you to practice your skills and get up to speed. For anyone who wants to use computational methods to conduct a social science research project, this book equips you with the skills, good habits and best working practices to do rigorous, high quality work.
Understand data analysis concepts to make accurate decisions based on data using Python programming and Jupyter Notebook Key FeaturesFind out how to use Python code to extract insights from data using real-world examplesWork with structured data and free text sources to answer questions and add value using dataPerform data analysis from scratch with the help of clear explanations for cleaning, transforming, and visualizing dataBook Description Data literacy is the ability to read, analyze, work with, and argue using data. Data analysis is the process of cleaning and modeling your data to discover useful information. This book combines these two concepts by sharing proven techniques and hands-on examples so that you can learn how to communicate effectively using data. After introducing you to the basics of data analysis using Jupyter Notebook and Python, the book will take you through the fundamentals of data. Packed with practical examples, this guide will teach you how to clean, wrangle, analyze, and visualize data to gain useful insights, and you'll discover how to answer questions using data with easy-to-follow steps. Later chapters teach you about storytelling with data using charts, such as histograms and scatter plots. As you advance, you'll understand how to work with unstructured data using natural language processing (NLP) techniques to perform sentiment analysis. All the knowledge you gain will help you discover key patterns and trends in data using real-world examples. In addition to this, you will learn how to handle data of varying complexity to perform efficient data analysis using modern Python libraries. By the end of this book, you'll have gained the practical skills you need to analyze data with confidence. What you will learnUnderstand the importance of data literacy and how to communicate effectively using dataFind out how to use Python packages such as NumPy, pandas, Matplotlib, and the Natural Language Toolkit (NLTK) for data analysisWrangle data and create DataFrames using pandasProduce charts and data visualizations using time-series datasetsDiscover relationships and how to join data together using SQLUse NLP techniques to work with unstructured data to create sentiment analysis modelsDiscover patterns in real-world datasets that provide accurate insightsWho this book is for This book is for aspiring data analysts and data scientists looking for hands-on tutorials and real-world examples to understand data analysis concepts using SQL, Python, and Jupyter Notebook. Anyone looking to evolve their skills to become data-driven personally and professionally will also find this book useful. No prior knowledge of data analysis or programming is required to get started with this book.
Formed by Harvey S. Shipley Miller, trustee of the Judith Rothschild Foundation, and given to MoMA in 2005, The Judith Rothschild Foundation Contemporary Drawings Collection was conceived to be a broad survey of contemporary drawing practice, and it more than fulfils that goal, mixing drawings of the 1960s and 1970s with major works of the past twenty years by such artists as Kai Althoff, Robert Crumb, Peter Doig, Marcel Dzama, Mark Grotjahn, Charline von Heyl, Martin Kippenberger, Sherrie Levine, Agnes Martin, Fred Sandback, Paul Thel and Andrea Zittel, among many others. This definitive catalogue raisonné presents the collection as a whole, with an introduction by Christian Rattemeyer; five essays each focusing on a different geographic area of artistic production; images throughout; and a text on paper conservation.
Published through the Recovering Languages and Literacies of the Americas initiative, supported by the Andrew W. Mellon Foundation In Chehalis Stories Jolynn Amrine Goertz and the Confederated Tribes of the Chehalis Reservation in Western Washington have assembled a collaborative volume of traditional stories collected by the anthropologist Franz Boas from tribal knowledge keepers in the early twentieth century. Both Boas and Amrine Goertz worked with past and present elders, including Robert Choke, Marion Davis, Peter Heck, Blanche Pete Dawson, and Jonas Secena, in collecting and contextualizing traditional knowledge of the Chehalis people. The elders shared stories with Boas at a critical juncture in Chehalis history, when assimilation efforts during the 1920s affected almost every aspect of Chehalis life. These are stories of transformation, going away, and coming back. The interwoven adventures of tricksters and transformers in Coast Salish narratives recall the time when people and animals lived together in the Chehalis River Valley. Catastrophic floods, stolen children, and heroic rescues poignantly evoke the resiliency of the people who have carried these stories for generations. Working with contemporary Chehalis people, Amrine Goertz has extensively reviewed the work of anthropologists in western Washington. This important collection examines the methodologies, shortcomings, and limitations of anthropologists' relationship with Chehalis people and presents complementary approaches to field work and its contextualization.
Ralph Waldo Emerson, the man and thinker, will be fully revealed for the first time in this new edition of his journals and notebooks. The old image of the ideal nineteenth-century gentleman, created by editorial omissions of his spontaneous thoughts, is replaced by the picture of Emerson as he really was. His frank and often bitter criticisms of men and society, his "nihilizing," his anguish at the death of his first wife, his bleak struggles with depression and loneliness, his sardonic views of woman, his earthy humor, his ideas of the Negro, of religion, of God--these and other expressions of his private thought and feeling, formerly deleted or subdued, are here restored. Restored also is the full evidence needed for studies of his habits of composition, the development of his style, and the sources of his ideas. Cancelled passages are reproduced, misreadings are corrected, and hitherto unpublished manuscripts are now printed. The text comes as close to a literal transcription as is feasible. A full apparatus of annotation, identification of quotations, and textual notes is supplied. Reproduced in this volume are twelve facsimile manuscript pages, many with Emerson's marginal drawings. The first volume includes some of the "Wide Worlds," journals begun while Emerson was at Harvard, and four contemporary notebooks, mostly unpublished. In these storehouses of quotation, juvenile verse, themes, and stories are the first versions of Emerson's "Valedictory Poem," Bowdoin Prize Essays, and first published work. Together they give a faithful picture of Emerson's apprenticeship as an artist and reveal the extent of his hidden and frustrated ambition--to become a writer.
A remarkable portrait of a web of artistic connections, traced outward from Jay DeFeo's uniquely generative work of art Through deep archival research and nuanced analysis, Elizabeth Ferrell examines the creative exchange that developed with and around The Rose, a monumental painting on which the San Francisco artist Jay DeFeo (1929-1989) worked almost exclusively from 1958 to 1966. From its early state to its dramatic removal from DeFeo's studio, the painting was a locus of activity among Fillmore District artists. Wallace Berman, Bruce Conner, Wally Hedrick, and Michael McClure each took up The Rose in their photographs, films, paintings, and poetry, which DeFeo then built upon in turn. The resulting works established a dialogue between artists rather than seamless cooperation. Illustrated with archival photographs and personal correspondence, in addition to the artworks, Ferrell's book traces how The Rose became a stage for experimentation with authorship and community, defying traditional definitions of collaboration and creating alternatives to Cold War America's political and artistic binaries.
Your comprehensive entry-level guide to machine learning While machine learning expertise doesn’t quite mean you can create your own Turing Test-proof android—as in the movie Ex Machina—it is a form of artificial intelligence and one of the most exciting technological means of identifying opportunities and solving problems fast and on a large scale. Anyone who masters the principles of machine learning is mastering a big part of our tech future and opening up incredible new directions in careers that include fraud detection, optimizing search results, serving real-time ads, credit-scoring, building accurate and sophisticated pricing models—and way, way more. Unlike most machine learning books, the fully updated 2nd Edition of Machine Learning For Dummies doesn't assume you have years of experience using programming languages such as Python (R source is also included in a downloadable form with comments and explanations), but lets you in on the ground floor, covering the entry-level materials that will get you up and running building models you need to perform practical tasks. It takes a look at the underlying—and fascinating—math principles that power machine learning but also shows that you don't need to be a math whiz to build fun new tools and apply them to your work and study. Understand the history of AI and machine learning Work with Python 3.8 and TensorFlow 2.x (and R as a download) Build and test your own models Use the latest datasets, rather than the worn out data found in other books Apply machine learning to real problems Whether you want to learn for college or to enhance your business or career performance, this friendly beginner's guide is your best introduction to machine learning, allowing you to become quickly confident using this amazing and fast-developing technology that's impacting lives for the better all over the world.