A new and important contribution to the re-emergent field of comparative anthropology, this book argues that comparative ethnographic methods are essential for more contextually sophisticated accounts of a number of pressing human concerns today. The book includes expert accounts from an international team of scholars, showing how these methods can be used to illuminate important theoretical and practical projects. Illustrated with examples of successful inter-disciplinary projects, it highlights the challenges, benefits, and innovative strategies involved in working collaboratively across disciplines. Through its focus on practical methodological and logistical accounts, it will be of value to both seasoned researchers who seek practical models for conducting their own cutting-edge comparative research, and to teachers and students who are looking for first-person accounts of comparative ethnographic research.

Even though many data analytics tools have been developed in the past years, their usage in the field of cyber twin warrants new approaches that consider various aspects including unified data representation, zero-day attack detection, data sharing across threat detection systems, real-time analysis, sampling, dimensionality reduction, resource-constrained data processing, and time series analysis for anomaly detection. Further study is required to fully understand the opportunities, benefits, and difficulties of data analytics and the internet of things in today's modern world. New Approaches to Data Analytics and Internet of Things Through Digital Twin considers how data analytics and the internet of things can be used successfully within the field of digital twin as well as the potential future directions of these technologies. Covering key topics such as edge networks, deep learning, intelligent data analytics, and knowledge discovery, this reference work is ideal for computer scientists, industry professionals, researchers, scholars, practitioners, academicians, instructors, and students.

Learn how to make the right decisions for your business with the help of Python recipes and the expertise of data leaders Key Features Learn and practice various clustering techniques to gather market insights Explore real-life use cases from the business world to contextualize your learning Work your way through practical recipes that will reinforce what you have learned Book DescriptionOne of the most valuable contributions of data science is toward helping businesses make the right decisions. Understanding this complicated confluence of two disparate worlds, as well as a fiercely competitive market, calls for all the guidance you can get. The Art of Data-Driven Business is your invaluable guide to gaining a business-driven perspective, as well as leveraging the power of machine learning (ML) to guide decision-making in your business. This book provides a common ground of discussion for several profiles within a company. You'll begin by looking at how to use Python and its many libraries for machine learning. Experienced data scientists may want to skip this short introduction, but you'll soon get to the meat of the book and explore the many and varied ways ML with Python can be applied to the domain of business decisions through real-world business problems that you can tackle by yourself. As you advance, you'll gain practical insights into the value that ML can provide to your business, as well as the technical ability to apply a wide variety of tried-and-tested ML methods. By the end of this Python book, you'll have learned the value of basing your business decisions on data-driven methodologies and have developed the Python skills needed to apply what you've learned in the real world. What you will learn Create effective dashboards with the seaborn library Predict whether a customer will cancel their subscription to a service Analyze key pricing metrics with pandas Recommend the right products to your customers Determine the costs and benefits of promotions Segment your customers using clustering algorithms Who this book is forThis book is for data scientists, machine learning engineers and developers, data engineers, and business decision makers who want to apply data science for business process optimization and develop the skills needed to implement data science projects in marketing, sales, pricing, customer success, ad tech, and more from a business perspective. Other professionals looking to explore how data science can be used to improve business operations, as well as individuals with technical skills who want to back their technical proposal with a strong business case will also find this book useful.

Elementary Statistics: A Guide to Data Analysis Using R provides students with an introduction to both the field of statistics and R, one of the most widely used languages for statistical computing, analysis, and graphing in a variety of fields, including the sciences, finance, banking, health care, e-commerce, and marketing. Part I provides an overview of both statistics and R. Part II focuses on descriptive statistics and probability. In Part III, students learn about discrete and continuous probability distributions with chapters addressing probability distributions, binominal probability distributions, and normal probability distributions. Part IV speaks to statistical inference with content covering confidence intervals, hypothesis testing, chi-square tests and F-distributions. The final part explores additional statistical inference and assumptions, including correlation, regression, and nonparametric statistics. Helpful appendices provide students with an index of terminology, an index of applications, a glossary of symbols, and a guide to the most common R commands. Elementary Statistics is an ideal resource for introductory courses in undergraduate statistics, graduate statistics, and data analysis across the disciplines.

This report discusses the role computer-assisted personal interviewing (CAPI) can play in transforming survey data collection to allow better monitoring of the Sustainable Development Goals. The first part of this publication provides rigorous quantitative evidence on why CAPI is a better alternative to the traditional pen and paper interviewing method, particularly in the context of nationally representative surveys. The second part discusses the benefits of delivering CAPI training to statisticians using the popular massive online open course format. The final part provides a summary of existing CAPI platforms and offers some preliminary advice for NSOs to consider when selecting a CAPI platform for their institution. This is a Special Supplement to the Key Indicators for Asia and the Pacific 2019.

The social sciences are becoming datafied. The questions once considered the domain of sociologists are now answered by data scientists operating on large datasets and breaking with methodological tradition, for better or worse. The traditional social sciences, such as sociology or anthropology, are under the double threat of becoming marginalized or even irrelevant, both from new methods of research which require more computational skills and from increasing competition from the corporate world which gains an additional advantage based on data access. However, unlike data scientists, sociologists and anthropologists have a long history of doing qualitative research. The more quantified datasets we have, the more difficult it is to interpret them without adding layers of qualitative interpretation. Big Data therefore needs Thick Data. This book presents the available arsenal of new methods and tools for studying society both quantitatively and qualitatively, opening ground for the social sciences to take the lead in analysing digital behaviour. It shows that Big Data can and should be supplemented and interpreted through thick data as well as cultural analysis. Thick Big Data is critically important for students and researchers in the social sciences to understand the possibilities of digital analysis, both in the quantitative and qualitative area, and to successfully build mixed-methods approaches.

Many people go through life in a rather hit-or-miss fashion, casting about for ideas to explain why their projects improve or decline, why they are successful or why they are not. Guessing and "hunches," however, are not very reliable. And without the knowledge of how to actually investigate situations, good or bad, and get the true facts, a person is set adrift in a sea of unevaluated data. Accurate investigation is, in fact, a rare commodity. Man's tendency in matters he doesn't understand is to accept the first proffered explanation, no matter how faulty. Thus investigatory technology had not actually been practiced or refined. However, L. Ron Hubbard made a breakthrough in the subject of logic and reasoning which led to his development of the first truly effective way to search for and consistently find the actual causes for things. Knowing how to investigate gives one the power to navigate through the random facts and opinions and emerge with the real reasons behind success or failure in any aspect of life. By really finding out why things are the way they are, one is therefore able to remedy and improve a situation-any situation. This is an invaluable technology for people in all walks of life.

This publication presents a case study in East Java, Indonesia, about ADB's collaboration with local governments and other stakeholders in monitoring, implementing, raising awareness, and advocating for the Sustainable Development Goals (SDGs). The SDGs set global, big-picture targets that nations have committed to attaining. However, unless action is taken at the local level, these targets can never be reached. The case study on Lumajang and Pacitan district demonstrate how ADB has been helping to make data available and accessible in a visually attractive and easy-to-understand way for different local stakeholders, thereby contributing to localizing SDGs.

Explore common and not-so-common data transformation scenarios and solutions to become well-versed with Tableau Prep and create efficient and powerful data pipelines Key Features Combine, clean, and shape data for analysis using self-service data preparation techniques Become proficient with Tableau Prep for building and managing data flows across your organization Learn how to combine multiple data transformations in order to build a robust dataset Book DescriptionTableau Prep is a tool in the Tableau software suite, created specifically to develop data pipelines. This book will describe, in detail, a variety of scenarios that you can apply in your environment for developing, publishing, and maintaining complex Extract, Transform and Load (ETL) data pipelines. The book starts by showing you how to set up Tableau Prep Builder. You'll learn how to obtain data from various data sources, including files, databases, and Tableau Extracts. Next, the book demonstrates how to perform data cleaning and data aggregation in Tableau Prep Builder. You'll also gain an understanding of Tableau Prep Builder and how you can leverage it to create data pipelines that prepare your data for downstream analytics processes, including reporting and dashboard creation in Tableau. As part of a Tableau Prep flow, you'll also explore how to use R and Python to implement data science components inside a data pipeline. In the final chapter, you'll apply the knowledge you've gained to build two use cases from scratch, including a data flow for a retail store to prepare a robust dataset using multiple disparate sources and a data flow for a call center to perform ad hoc data analysis. By the end of this book, you'll be able to create, run, and publish Tableau Prep flows and implement solutions to common problems in data pipelines. What you will learn Perform data cleaning and preparation techniques for advanced data analysis Understand how to combine multiple disparate datasets Prepare data for different Business Intelligence (BI) tools Apply Tableau Prep's calculation language to create powerful calculations Use Tableau Prep for ad hoc data analysis and data science flows Deploy Tableau Prep flows to Tableau Server and Tableau Online Who this book is forThis book is for business intelligence professionals, data analysts, and Tableau users looking to learn Tableau Prep essentials and create data pipelines or ETL processes using it. Beginner-level knowledge of data management will be beneficial to understand the concepts covered in this Tableau cookbook more effectively.

New and expanded edition. An International Bestseller - Over One Million Copies Sold! Shortlisted for the Financial Times/Goldman Sachs Business Book of the Year Award. Since Aristotle, we have fought to understand the causes behind everything. But this ideology is fading. In the age of big data, we can crunch an incomprehensible amount of information, providing us with invaluable insights about the what rather than the why. We're just starting to reap the benefits: tracking vital signs to foresee deadly infections, predicting building fires, anticipating the best moment to buy a plane ticket, seeing inflation in real time and monitoring social media in order to identify trends. But there is a dark side to big data. Will it be machines, rather than people, that make the decisions? How do you regulate an algorithm? What will happen to privacy? Will individuals be punished for acts they have yet to commit? In this groundbreaking and fascinating book, two of the world's most-respected data experts reveal the reality of a big data world and outline clear and actionable steps that will equip the reader with the tools needed for this next phase of human evolution.

Social Network Analysis: Methods and Examples prepares social science students to conduct their own social network analysis (SNA) by covering basic methodological tools along with illustrative examples from various fields. This innovative book takes a conceptual rather than a mathematical approach as it discusses the connection between what SNA methods have to offer and how those methods are used in research design, data collection, and analysis. Four substantive applications chapters provide examples from politics, work and organizations, mental and physical health, and crime and terrorism studies.

Reinforce your understanding of data science and data analysis from a statistical perspective to extract meaningful insights from your data using Python programming Key Features Work your way through the entire data analysis pipeline with statistics concerns in mind to make reasonable decisions Understand how various data science algorithms function Build a solid foundation in statistics for data science and machine learning using Python-based examples Book DescriptionStatistics remain the backbone of modern analysis tasks, helping you to interpret the results produced by data science pipelines. This book is a detailed guide covering the math and various statistical methods required for undertaking data science tasks. The book starts by showing you how to preprocess data and inspect distributions and correlations from a statistical perspective. You'll then get to grips with the fundamentals of statistical analysis and apply its concepts to real-world datasets. As you advance, you'll find out how statistical concepts emerge from different stages of data science pipelines, understand the summary of datasets in the language of statistics, and use it to build a solid foundation for robust data products such as explanatory models and predictive models. Once you've uncovered the working mechanism of data science algorithms, you'll cover essential concepts for efficient data collection, cleaning, mining, visualization, and analysis. Finally, you'll implement statistical methods in key machine learning tasks such as classification, regression, tree-based methods, and ensemble learning. By the end of this Essential Statistics for Non-STEM Data Analysts book, you'll have learned how to build and present a self-contained, statistics-backed data product to meet your business goals. What you will learn Find out how to grab and load data into an analysis environment Perform descriptive analysis to extract meaningful summaries from data Discover probability, parameter estimation, hypothesis tests, and experiment design best practices Get to grips with resampling and bootstrapping in Python Delve into statistical tests with variance analysis, time series analysis, and A/B test examples Understand the statistics behind popular machine learning algorithms Answer questions on statistics for data scientist interviews Who this book is forThis book is an entry-level guide for data science enthusiasts, data analysts, and anyone starting out in the field of data science and looking to learn the essential statistical concepts with the help of simple explanations and examples. If you're a developer or student with a non-mathematical background, you'll find this book useful. Working knowledge of the Python programming language is required.

Spatial Regression Analysis Using Eigenvector Spatial Filtering provides theoretical foundations and guides practical implementation of the Moran eigenvector spatial filtering (MESF) technique. MESF is a novel and powerful spatial statistical methodology that allows spatial scientists to account for spatial autocorrelation in their georeferenced data analyses. Its appeal is in its simplicity, yet its implementation drawbacks include serious complexities associated with constructing an eigenvector spatial filter. This book discusses MESF specifications for various intermediate-level topics, including spatially varying coefficients models, (non) linear mixed models, local spatial autocorrelation, space-time models, and spatial interaction models. Spatial Regression Analysis Using Eigenvector Spatial Filtering is accompanied by sample R codes and a Windows application with illustrative datasets so that readers can replicate the examples in the book and apply the methodology to their own application projects. It also includes a Foreword by Pierre Legendre.

Models and methods for operational risks assessment and mitigation are gaining importance in financial institutions, healthcare organizations, industry, businesses and organisations in general. This book introduces modern Operational Risk Management and describes how various data sources of different types, both numeric and semantic sources such as text can be integrated and analyzed. The book also demonstrates how Operational Risk Management is synergetic to other risk management activities such as Financial Risk Management and Safety Management. Operational Risk Management: a practical approach to intelligent data analysis provides practical and tested methodologies for combining structured and unstructured, semantic-based data, and numeric data, in Operational Risk Management (OpR) data analysis. Key Features: * The book is presented in four parts: 1) Introduction to OpR Management, 2) Data for OpR Management, 3) OpR Analytics and 4) OpR Applications and its Integration with other Disciplines. * Explores integration of semantic, unstructured textual data, in Operational Risk Management. * Provides novel techniques for combining qualitative and quantitative information to assess risks and design mitigation strategies. * Presents a comprehensive treatment of "near-misses" data and incidents in Operational Risk Management. * Looks at case studies in the financial and industrial sector. * Discusses application of ontology engineering to model knowledge used in Operational Risk Management. Many real life examples are presented, mostly based on the MUSING project co-funded by the EU FP6 Information Society Technology Programme. It provides a unique multidisciplinary perspective on the important and evolving topic of Operational Risk Management. The book will be useful to operational risk practitioners, risk managers in banks, hospitals and industry looking for modern approaches to risk management that combine an analysis of structured and unstructured data. The book will also benefit academics interested in research in this field, looking for techniques developed in response to real world problems.