A national bestseller in 1971, Born to Win still sells thousands of copies each year. The insights in the book are now fundamental to how we see ourselves. It was one of the first self-help books to analyze communication styles and its 50 gestalt exercises are as revealing as ever about the roles people reenact in their Parent and Child ego states. Photos.

库尔特•P.弗雷（Kurt P. Frey）是新罗谢尔学院心理学副教授。他于1993年在普渡大学获得社会与人格心理学博士学位。艾登•P.格雷格（Aiden P. Gregg）是南安普顿大学心理学副教授。他于2000年在耶鲁大学获得心理学博士学位。 译者简介 白学军，天津师范大学心理学教授、博士生导师、教育部长江学者特聘教授、国家“万人计划”哲学社会科学领军人才、全国文化名家暨四个一批人才、教育部高等学校心理学类专业教学指导委员会副主任。主要译著有《心理学的邀请（第三版）》《发展心理学》《改变心理学的40项研究：探索心理学研究的历史》等。

An aging ruler, an oppressive police state, a restless polarized people seething with paranoia: it’s a dangerous time for poets. Two of them—the great Kit Marlowe and the up-and-comer Will Shakespeare—meet in the back room of a pub to collaborate on a history play cycle, navigate the perils of art under a totalitarian regime, and flirt like young men with everything to lose. On...(展开全部)

克里斯托弗·麦克杜格尔（Christopher McDougall） 美国作家。毕业于哈佛大学，后入美联社担任记者，赴安哥拉、刚果和卢旺达报道战事。返美后为《户外》、《纽约时报》、《跑步者世界》等刊物撰稿。曾三度入围全美杂志报道奖。 爱好跑步，但频受脚痛之苦。偶然得知在墨西哥的铜峡谷，隐居着史上最强的长跑族群塔拉乌马拉人。于是穿越峡谷，寻找和族人颇有交情的奇人卡巴洛，一睹这支与世隔绝的部族的真面貌，探得跑步真谛。此间学习塔拉乌马拉人的跑法，脚伤不治自愈，后写下《天生就会跑》一书。 目前在练跑步之余，写下一部作品。

Alan S. Gerber is Professor of Political Science and Director of the Center for the Study of American Politics at Yale University where he teaches courses on experimental methods, statistics, and American politics. His experimental research has appeared in numerous academic journals including the leading journals in political science. Donald P. Green is Professor of Political S...(展开全部)

Unabridged CDs • 9 CDs, 11 hours The story of the most important political campaign in modern times, by the man who went through it side by side with President Obama.

Budding data scientists and data analysts who are new to the concept of data analysis, or who want to build efficient analytical models in R will find this book to be useful. No prior exposure to data analysis is needed, although a fundamental understanding of the R programming language is required to get the best out of this book.

The second edition of Bayesian Analysis with Python is an introduction to the main concepts of applied Bayesian inference and its practical implementation in Python using PyMC3, a state-of-the-art probabilistic programming library, and ArviZ, a new library for exploratory analysis of Bayesian models.The main concepts of Bayesian statistics are covered using a practical and computational approach. Synthetic and real data sets are used to introduce several types of models, such as generalized linear models for regression and classification, mixture models, hierarchical models, and Gaussian processes, among others. By the end of the book, you will have a working knowledge of probabilistic modeling and you will be able to design and implement Bayesian models for your own data science problems. After reading the book you will be better prepared to delve into more advanced material or specialized statistical modeling if you need to.

Data Analysis with Python offers a modern approach to data analysis so that you can work with the latest and most powerful Python tools, AI techniques, and open source libraries. Industry expert David Taieb shows you how to bridge data science with the power of programming and algorithms in Python. You'll be working with complex algorithms, and cutting-edge AI in your data analysis. Learn how to analyze data with hands-on examples using Python-based tools and Jupyter Notebook. You'll find the right balance of theory and practice, with extensive code files that you can integrate right into your own data projects.Explore the power of this approach to data analysis by then working with it across key industry case studies. Four fascinating and full projects connect you to the most critical data analysis challenges you’re likely to meet in today. The first of these is an image recognition application with TensorFlow – embracing the importance today of AI in your data analysis. The second industry project analyses social media trends, exploring big data issues and AI approaches to natural language processing. The third case study is a financial portfolio analysis application that engages you with time series analysis - pivotal to many data science applications today. The fourth industry use case dives you into graph algorithms and the power of programming in modern data science. You'll wrap up with a thoughtful look at the future of data science and how it will harness the power of algorithms and artificial intelligence.

This is a practical, step by step guide that will help you to quickly become proficient in the data analysis using R. The book is packed with clear examples, screenshots, and code to carry on your data analysis without any hurdle. If you are a data analyst, business or information technology professional, student, educator, researcher, or anyone else who wants to learn to analy...(展开全部)