Finding great data analysts is difficult. Despite the explosive growth of data in industries ranging from manufacturing and retail to high technology, finance, and healthcare, learning and accessing data analysis tools has remained a challenge. This pragmatic guide will help train you in one of the most important tools in the field - Python. Filled with practical case studies, Python for Data Analysis demonstrates the nuts and bolts of manipulating, processing, cleaning, and crunching data with Python. It also serves as a modern introduction to scientific computing in Python for data-intensive applications. Learn about the growing field of data analysis from an expert in the community. Learn everything you need to start doing real data analysis work with Python Get the most complete instruction on the basics of the "modern scientific Python platform" Learn from an insider who builds tools for the scientific stack Get an excellent introduction for novices and a wealth of advanced methods for experienced analysts

Like many organizations and social movements, the Third Republic French labour movement exhibited a marked tendency to schism into competing sectarian organizations. During the roughly 50-year period from the fall of the Paris Commune to the creation of the powerful French Communist Party, the French labour movement shifted from schism to broad-based solidarity and back to schism. In this 2001 book, Ansell analyses the dynamic interplay between political mobilization, organization-building, and ideological articulation that produced these shifts between schism and solidarity. The aim is not only to shed light on the evolution of the Third Republic French labour movement, but also to develop a more generic understanding of schism and solidarity in organizations and social movements. To develop this broader understanding, the book builds on insights drawn from sociological analyses of Protestant sects and anthropological studies of segmentary societies, as well as from organization and social movement theory.

A survey of computational methods for understanding, generating, and manipulating human language, which offers a synthesis of classical representations and algorithms with contemporary machine learning techniques.
This textbook provides a technical perspective on natural language processing―methods for building computer software that understands, generates, and manipulates human language. It emphasizes contemporary data-driven approaches, focusing on techniques from supervised and unsupervised machine learning. The first section establishes a foundation in machine learning by building a set of tools that will be used throughout the book and applying them to word-based textual analysis. The second section introduces structured representations of language, including sequences, trees, and graphs. The third section explores different approaches to the representation and analysis of linguistic meaning, ranging from formal logic to neural word embeddings. The final section offers chapter-length treatments of three transformative applications of natural language processing: information extraction, machine translation, and text generation. End-of-chapter exercises include both paper-and-pencil analysis and software implementation.
The text synthesizes and distills a broad and diverse research literature, linking contemporary machine learning techniques with the field's linguistic and computational foundations. It is suitable for use in advanced undergraduate and graduate-level courses and as a reference for software engineers and data scientists. Readers should have a background in computer programming and college-level mathematics. After mastering the material presented, students will have the technical skill to build and analyze novel natural language processing systems and to understand the latest research in the field.

This is the 2nd edition of "Speech and Language Processing, 2000" (http://www.douban.com/subject/1810715/).
An explosion of Web-based language techniques, merging of distinct fields, availability of phone-based dialogue systems, and much more make this an exciting time in speech and language processing. The first of its kind to thoroughly cover language technology – at all levels and with all modern technologies – this book takes an empirical approach to the subject, based on applying statistical and other machine-learning algorithms to large corporations. Builds each chapter around one or more worked examples demonstrating the main idea of the chapter, usingthe examples to illustrate the relative strengths and weaknesses of various approaches. Adds coverage of statistical sequence labeling, information extraction, question answering and summarization, advanced topics in speech recognition, speech synthesis. Revises coverage of language modeling, formal grammars, statistical parsing, machine translation, and dialog processing. A useful reference for professionals in any of the areas of speech and language processing.

Most textbooks on regression focus on theory and the simplest of examples. Real statistical problems, however, are complex and subtle. This is not a book about the theory of regression. It is about using regression to solve real problems of comparison, estimation, prediction, and causal inference. Unlike other books, it focuses on practical issues such as sample size and missing data and a wide range of goals and techniques. It jumps right in to methods and computer code you can use immediately. Real examples, real stories from the authors' experience demonstrate what regression can do and its limitations, with practical advice for understanding assumptions and implementing methods for experiments and observational studies. They make a smooth transition to logistic regression and GLM. The emphasis is on computation in R and Stan rather than derivations, with code available online. Graphics and presentation aid understanding of the models and model fitting.

Machine learning is the study of algorithms that learn from data and experience. It is applied in a vast variety of application areas, from medicine to advertising, from military to pedestrian. Any area in which you need to make sense of data is a potential consumer of machine learning.
CIML is a set of introductory materials that covers most major aspects of modern machine learning (supervised learning, unsupervised learning, large margin methods, probabilistic modeling, learning theory, etc.). It's focus is on broad applications with a rigorous backbone. A subset can be used for an undergraduate course; a graduate course could probably cover the entire material and then some.

Educational policy-makers around the world constantly make decisions about how to use scarce resources to improve the education of children. Unfortunately, their decisions are rarely informed by evidence on the consequences of these initiatives in other settings. Nor are decisions typically accompanied by well-formulated plans to evaluate their causal impacts. As a result, knowledge about what works in different situations has been very slow to accumulate.
Over the last several decades, advances in research methodology, administrative record keeping, and statistical software have dramatically increased the potential for researchers to conduct compelling evaluations of the causal impacts of educational interventions, and the number of well-designed studies is growing. Written in clear, concise prose, Methods Matter: Improving Causal Inference in Educational and Social Science Research offers essential guidance for those who evaluate educational policies. Using numerous examples of high-quality studies that have evaluated the causal impacts of important educational interventions, the authors go beyond the simple presentation of new analytical methods to discuss the controversies surrounding each study, and provide heuristic explanations that are also broadly accessible. Murnane and Willett offer strong methodological insights on causal inference, while also examining the consequences of a wide variety of educational policies implemented in the U.S. and abroad. Representing a unique contribution to the literature surrounding educational research, this landmark text will be invaluable for students and researchers in education and public policy, as well as those interested in social science.

《会读才会写：导向论文写作的文献阅读技巧》（原名：How to Read Journal Articles in the Social Sciences: A Very Practical Guide for Students），本书专门教授高效率阅读社会科学类学术论文的实用技巧。
本书使用作者独创的阅读密码表，教学生在阅读社会与行为科学期刊论文时，如何将其作为在结构、技巧和语法方面可解码的文本处理。书里的技巧让读者能够进行系统化的阅读、笔记，并以易辨识、易提取的格式实现海量信息的组织。

《走向多元话语分析:后现代思潮的社会学意涵》内容简介：尽管对“后现代思潮的社会学意涵”有着种种不同的理解，但《走向多元话语分析:后现代思潮的社会学意涵》作者认为：这一思潮最重要的社会学内涵之一就是试图否定作为全部现代主义社会学理论之基础的那种“给定实在论”传统，用一种多元主义的“话语建构论”立场来取代之。尽管这一立场受到了不少人的批评和诟病，但正如S．塞德曼、R.布朗、C.勒麦特等人所指出的那样，它并非只是为我们修改、完善旧有的那些现代主义社会学研究框架提供了若干这样或那样的启发，而是蕴涵着一种与各种现代主义社会学研究框架很不相同的社会分析模式，即《走向多元话语分析:后现代思潮的社会学意涵》作者所谓的“多元话语分析”模式，从而有可能为社会学研究开辟一条新的发展方向和研究路径。在《走向多元话语分析:后现代思潮的社会学意涵》中，作者试图通过理论与经验研究方面的一系列具体论述来说明这一基本观点。

《参与观察法》在西方社会科学界长销不衰，已逐步成为一部得到广泛认可的经典工具书，也是我国内地在参与观察法方面的第一本译著。
《参与观察法》作者长期使用参与观察方法开展研究。基于其丰富的研究经验，他在书中既简要地讨论了参与观察法的方法论基础，以及适合的领域与局限；又翔实地讲解了这种方法实际操作的基本程序与具体技术，并展示了许多参与观察法研究的成果，提供了大量实用、有趣、新鲜、富于启发的信息。
《参与观察法》适合于管理学、社会学、人类学、教育学等社会及行为科学领域的研究人员和研究生阅读。