Python Data Science Handbook

Handbook for Folklore and Ethnomusicology Fieldwork offers a comprehensive review of the ethnographic process for developing a project, implementing the plan, and completing and preserving the data co

《Python程序設計》由淺入深、循序漸進地通過大量實例闡述了Python語言的基礎知識，同時介紹如何使用Python來解決科學計算問題，以及如何使用Python面向對象編程，還介紹了很多Python的應用技術。《Python程序設計》共9章，第1章主要是Python語言概述，第2章介紹Python的基本元素，第3章介紹Python語言的流程控制和循環語句，第4章介紹Python函數的定義與使用，

Large datasets tend to be distributed, non-uniform, and prone to change. Dask simplifies the process of ingesting, filtering, and transforming data, reducing or eliminating the need for a heavyweight

Second edition of the best selling Python book in the world. A fast-paced, no-nonsense guide to programming in Python. This book teaches beginners the basics of programming in Python with a focus on r

Written in lucid language, this valuable textbook brings together fundamental concepts of data mining and data warehousing in a single volume. Important topics including information theory, decision tree, Naïve Bayes classifier, distance metrics, partitioning clustering, associate mining, data marts and operational data store are discussed comprehensively. The textbook is written to cater to the needs of undergraduate students of computer science, engineering and information technology for a course on data mining and data warehousing. The text simplifies the understanding of the concepts through exercises and practical examples. Chapters such as classification, associate mining and cluster analysis are discussed in detail with their practical implementation using Weka and R language data mining tools. Advanced topics including big data analytics, relational data models and NoSQL are discussed in detail. Pedagogical features including unsolved problems and multiple-choice questions are

This full-color text shows readers how to transform data into something meaningful - information. It is meant for anyone interested in the art and science of communicating data to others. Drawing on t

Data-intensive systems are a technological building block supporting Big Data and Data Science applications.This book familiarizes readers with core concepts that they should be aware of before contin

This volume provides a practical introduction to the method of maximum likelihood as used in social science research. Ward and Ahlquist focus on applied computation in R and use real social science data from actual, published research. Unique among books at this level, it develops simulation-based tools for model evaluation and selection alongside statistical inference. The book covers standard models for categorical data as well as counts, duration data, and strategies for dealing with data missingness. By working through examples, math, and code, the authors build an understanding about the contexts in which maximum likelihood methods are useful and develop skills in translating mathematical statements into executable computer code. Readers will not only be taught to use likelihood-based tools and generate meaningful interpretations, but they will also acquire a solid foundation for continued study of more advanced statistical techniques.

This Handbook provides a compendium of research methods that are essential for studying interaction and communication across the behavioral sciences. Focusing on coding of verbal and nonverbal behavior and interaction, the Handbook is organized into five parts. Part I provides an introduction and historic overview of the field. Part II presents areas in which interaction analysis is used, such as relationship research, group research, and nonverbal research. Part III focuses on development, validation, and concrete application of interaction coding schemes. Part IV presents relevant data analysis methods and statistics. Part V contains systematic descriptions of established and novel coding schemes, which allows quick comparison across instruments. Researchers can apply this methodology to their own interaction data and learn how to evaluate and select coding schemes and conduct interaction analysis. This is an essential reference for all who study communication in teams and groups.

This timely Handbook is based on the principle that disasters are social constructions and focuses on social science disaster research. It provides an interdisciplinary approach to disasters with theo

Bridging the gap between science and clinical practice, The Bethesda Handbook of Clinical Hematology, Fourth Edition, provides concise, up-to-date coverage of “need to know” information on

This compact open access reference delves beyond popular concepts of educated consumers and an informed public by examining the science behind deliberative engagement. Using data from four longitudina

An innovative new survival guide for the pediatric intensive care unit (PICU)The PICU Handbook is a unique, pocket-sized compilation of the data necessary for residents and fellows to navigate the mod

Widely regarded as the state-of-the-science reference on attachment, this handbook interweaves theory and cutting-edge research with clinical applications. Leading researchers examine the origins and

For courses in computer science and programming Starting Out with Java: From Control Structures through Data Structures provides a smooth introduction to programming with Java that moves fl

This book constitutes the refereed proceedings of the First International Conference on Data Science Analytics and Applications, DaSAA 2017, held in Chennai, India, in January 2017. The 16 revise

This book is published open access under a CC BY 4.0 license.It covers aspects of unsupervised machine learning used for knowledge discovery in data science and introduces a data-driven approach to cl

Direct from Microsoft, this Exam Ref is the official study guide for the Microsoft 70-774 Perform Cloud Data Science with Azure Machine Learning certification exam, the second of two exams required fo

This book is the first of its kind to provide a reference that enables students and practitioners to easily learn to code in Python if they are familiar with R and vice versa, even if they are beginne

The history of data brings together topics and themes from a variety of perspectives in history of science: histories of the material culture of information and of computing, the history of politics o

Data Literacy: How to Make Your Experiments Robust and Reproducible provides an overview of basic concepts and skills in handling data which are common to diverse areas of science. Readers will gain a

The typical data science task in industry starts with an “ask” from the business. But few data scientists have been taught what to do with that ask. This book shows them how to assess it in the contex

This book constitutes the proceedings of the 7th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2017, held in Dalian, China, in September 2017.The 48 full papers an

There is a long history of governments, businesses, science and citizens producing and utilising data in order to monitor, regulate, profit from, and make sense of the urban world. Recently, we have

Heavy tailed data appears frequently in social science, internet traffic, insurance and finance. Statistical inference has been studied for many years, which includes recent bias-reduction estimation

"Big Data" is on the covers of Science, Nature, the Economist, and Wired magazines, on the front pages of the Wall Street Journal and the New York Times. B

The Second Edition of this essential handbook provides a comprehensive, updated overview of the science that informs best practices for the implementation of response to intervention (RTI) processes w

The Oxford Handbook of the U.S. Constitution offers a comprehensive overview and introduction to the U.S. Constitution from the perspectives of history, political science, law, rights, and constitutio

Through the use of real-life social science examples, Analyzing Qualitative Data: Systematic Approaches Second Edition by H. Russell Bernard, Amber Wutich and Gery W. Ryan, w

A good data scientist knows how to do something really well, but a great data scientist can do "something of everything." From raw data all the way to shining in front of C-level executives,

This book covers several of the statistical concepts and data analytic skills needed to succeed in data-driven life science research.The authors proceed from relatively basic concepts related to compu

Written for diverse academic audience, this text serves as a handbook for professors, instructors, and advisors who oversee data collection by undergraduate students for the purpose of writing a resea

Finally! Deep Neural Networks Simplified with PythonDeep Learning Step by Step with Python takes you on a gentle, fun and unhurried journey to building your own deep neural network models in Python. U

Over 140 practical recipes to help you make sense of your data with ease and build production-ready data apps About This Book * Analyze Big Data sets, create attractive visualizations, and manipulate

In the fourth DATA Set adventure, the DATA Set race to deprogram an army of robots before they take over Newtonberg Elementary School.One morning, Mrs. Bell, the science teacher, challenges the studen

Since 1984, Geophysical Data Analysis has filled the need for a short, concise reference on inverse theory for individuals who have an intermediate background in science and mathematics. The new editi

Handbook for Transversely Finned Tubes Heat Exchangers Design contains detailed experimental data, correlations, and design methods for designing and improving the performance of finned tube heat exch

Learn the art of regression analysis with PythonAbout This BookBecome competent at implementing regression analysis in PythonSolve some of the complex data science problems related to predicting outco

Quantitative research in social science research is changing rapidly. Researchers have vast and complex arrays of data with which to work: we have incredible tools to sift through the data and recognize patterns in that data; there are now many sophisticated models that we can use to make sense of those patterns; and we have extremely powerful computational systems that help us accomplish these tasks quickly. This book focuses on some of the extraordinary work being conducted in computational social science - in academia, government, and the private sector - while highlighting current trends, challenges, and new directions. Thus, Computational Social Science showcases the innovative methodological tools being developed and applied by leading researchers in this new field. The book shows how academics and the private sector are using many of these tools to solve problems in social science and public policy.

This book constitutes the refereed proceedings of the First International Conference on Dynamic Data-Driven Environmental Systems Science, DyDESS 2014, held in Cambridge, MA, USA, in November 2014.The