Transform your data into insights with must-know techniques and mathematical concepts to unravel the secrets hidden within your data Key Features Learn practical data science combined with data theory to gain maximum insights from data Discover methods for deploying actionable machine learning pipelines while mitigating biases in data and models Explore actionable case studies to put your new skills to use immediately Purchase of the print or

The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contribution...(展开全部)

This book is intended for use in a one- or two-semester course in environmental science, human ecology, or environmental studies at the college or advanced placement high school level. Because most students who will use this book are freshman or sophomore nonscience majors, the authors have tried to make the text readable and accessible without technical jargon or a presumption...(展开全部)

This title now updated: the definitive neuroscience resource-from Eric R. Kandel, MD (winner of the Nobel Prize in 2000); James H. Schwartz, MD, PhD; Thomas M. Jessell, PhD; Steven A. Siegelbaum, PhD; and A. J. Hudspeth, PhD 900 full-color illustrations. Deciphering the link between the human brain and behavior has always been one of the most intriguing - and often challenging-...(展开全部)

A concise introduction to the emerging field of data science, explaining its evolution, relation to machine learning, current uses, data infrastructure issues, and ethical challenges.

This is the most authoritative introduction to the brain, its structure, function, development, and control of behavior available today. It presents both a comprehensive summary of the state of the science and a full discussion of historical issues in the study of the brain. Neuroanatomy, cell and molecular mechanisms, mechanisms, of signaling, and development are thoroughly de...(展开全部)

Rather than the 25 to 30 chapters found in most environmental science textbooks, the authors have limited Principles of Environmental Science: Inquiry and Applications to 15 chapters--perfect for the one-semester, non-majors environmental science course. True to its title, the goal of this concise text is to provide an up-to-date, introductory view of essential themes in enviro...(展开全部)

◎作者简介 威廉·坎宁安（William Cunningham），美国明尼苏达大学的名誉教授，他在植物学、遗传学、细胞生物学以及生物保护计划方面持续了36年的教学工作。1963年，他获得得克萨斯州植物学博士学位，并在普渡大学担任博士后研究员两年。他曾多次在瑞典、挪威、印度尼西亚和中国做访问学者。坎宁安博士致力于生物学的本科阶段的教育和教学发展。他从结构生物学开始了他的教学生涯，他还长期专注于环境科学和教学课程，并获得了明尼苏达大学授予的两项最高荣誉——杰出教学奖和“Amoco”校友奖。 玛丽·安·坎宁安（Mary Ann Cunningham），一直致力于地理和地理信息系统（GIS）以及瓦萨学院的环境研究。她的研究包括使用GIS来评估保护和生物多样性的景观级问题。她对了解草地环境破坏的本质和对鸟类群落构成的影响感兴趣。她研究的农业景观涉及工作景观、资...(展开全部)

Services like social networks, web analytics, and intelligent e-commerce often need to manage data at a scale too big for a traditional database. Complexity increases with scale and demand, and handling big data is not as simple as just doubling down on your RDBMS or rolling out some trendy new technology. Fortunately, scalability and simplicity are not mutually exclusive—you j...(展开全部)

Data Science with Python begins by introducing you to data science and teaches you to install the packages you need to create a data science coding environment. You will learn three major techniques in machine learning: unsupervised learning, supervised learning, and reinforcement learning. You will also explore basic classification and regression techniques, such as support vector machines, decision trees, and logistic regression.As you make your way through chapters, you will study the basic functions, data structures, and syntax of the Python language that are used to handle large datasets with ease. You will learn about NumPy and pandas libraries for matrix calculations and data manipulation, study how to use Matplotlib to create highly customizable visualizations, and apply the boosting algorithm XGBoost to make predictions. In the concluding chapters, you will explore convolutional neural networks (CNNs), deep learning algorithms used to predict what is in an image. You will also understand how to feed human sentences to a neural network, make the model process contextual information, and create human language processing systems to predict the outcome.By the end of this book, you will be able to understand and implement any new data science algorithm and have the confidence to experiment with tools or libraries other than those covered in the book.