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.

FINALIST: Business Book Awards 2019 - HR and Management Category Traditionally seen as a purely people function unconcerned with numbers, HR is now uniquely placed to use company data to drive performance, both of the people in the organization and the organization as a whole. Data-Driven HR is a practical guide which enables HR professionals to leverage the value of the vast amount of data available at their fingertips. Covering how to identify the most useful sources of data, collect information in a transparent way that is in line with data protection requirements and turn this data into tangible insights, this book marks a turning point for the HR profession. Covering all the key elements of HR including recruitment, employee engagement, performance management, wellbeing and training, Data-Driven HR examines the ways data can contribute to organizational success by, among other things, optimizing processes, driving performance and improving HR decision making. Packed with case studies and real-life examples, this is essential reading for all HR professionals looking to make a measurable difference in their organizations.

Data science has never had more influence on the world. Large companies are now seeing the benefit of employing data scientists to interpret the vast amounts of data that now exists. However, the field is so new and is evolving so rapidly that the analysis produced can be haphazard at best. The 9 Pitfalls of Data Science shows us real-world examples of what can go wrong. Written to be an entertaining read, this invaluable guide investigates the all too common mistakes of data scientists - who can be plagued by lazy thinking, whims, hunches, and prejudices - and indicates how they have been at the root of many disasters, including the Great Recession. Gary Smith and Jay Cordes emphasise how scientific rigor and critical thinking skills are indispensable in this age of Big Data, as machines often find meaningless patterns that can lead to dangerous false conclusions. The 9 Pitfalls of Data Science is loaded with entertaining tales of both successful and misguided approaches to interpreting data, both grand successes and epic failures. These cautionary tales will not only help data scientists be more effective, but also help the public distinguish between good and bad data science.

Practical guide for deriving insight and commercial gain from data Monetising Data offers a practical guide for anyone working with commercial data but lacking deep knowledge of statistics or data mining. The authors -- noted experts in the field -- show how to generate extra benefit from data already collected and how to use it to solve business problems. In accessible terms, the book details ways to extract data to enhance business practices and offers information on important topics such as data handling and management, statistical methods, graphics and business issues. The text presents a wide range of illustrative case studies and examples to demonstrate how to adapt the ideas towards monetisation, no matter the size or type of organisation. The authors explain on a general level how data is cleaned and matched between data sets and how we learn from data analytics to address vital business issues. The book clearly shows how to analyse and organise data to identify people and follow and interact with them through the customer lifecycle. Monetising Data is an important resource: Focuses on different business scenarios and opportunities to turn data into value Gives an overview on how to store, manage and maintain data Presents mechanisms for using knowledge from data analytics to improve the business and increase profits Includes practical suggestions for identifying business issues from the data Written for everyone engaged in improving the performance of a company, including managers and students, Monetising Data is an essential guide for understanding and using data to enrich business practice.

Margarita Pivovarova is an Assistant Professor of Education Economics at the Mary Lou Fulton Teachers College at Arizona State University. Her research interests include education policy and related quantitative research. Specifically, her research studies the economic consequences of accountability and financial incentives in education. In addition, she studies peer interactions in school contexts and optimal classroom or school design.

Recently, there has been a rapid increase in interest regarding social network analysis in the data mining community. Cognitive radios are expected to play a major role in meeting this exploding traffic demand on social networks due to their ability to sense the environment, analyze outdoor parameters, and then make decisions for dynamic time, frequency, space, resource allocation, and management to improve the utilization of mining the social data. Cognitive Social Mining Applications in Data Analytics and Forensics is an essential reference source that reviews cognitive radio concepts and examines their applications to social mining using a machine learning approach so that an adaptive and intelligent mining is achieved. Featuring research on topics such as data mining, real-time ubiquitous social mining services, and cognitive computing, this book is ideally designed for social network analysts, researchers, academicians, and industry professionals.

The general theme of this book is to encourage the use of relevant methodology in data mining which is or could be applied to the interplay of education, statistics and computer science to solve psychometric issues and challenges in the new generation of assessments. In addition to item response data, other data collected in the process of assessment and learning will be utilized to help solve psychometric challenges and facilitate learning and other educational applications. Process data include those collected or available for collection during the process of assessment and instructional phase such as responding sequence data, log files, the use of help features, the content of web searches, etc. Some book chapters present the general exploration of process data in large -scale assessment. Further, other chapters also address how to integrate psychometrics and learning analytics in assessment and survey, how to use data mining techniques for security and cheating detection, how to use more assessment results to facilitate student's learning and guide teacher's instructional efforts. The book includes both theoretical and methodological presentations that might guide the future in this area, as well as illustrations of efforts to implement big data analytics that might be instructive to those in the field of learning and psychometrics. The context of the effort is diverse, including K-12, higher education, financial planning, and survey utilization. It is hoped that readers can learn from different disciplines, especially those who are specialized in assessment, would be critical to expand the ideas of what we can do with data analytics for informing assessment practices.

This volume focuses on the ethics of internet and social networking research exploring the challenges faced by researchers making use of social media and big data in their research. The internet, the world wide web and social media - indeed all forms of online communications - are attractive fields of research across a range of disciplines. They offer opportunities for methodological initiatives and innovations in research and easily accessed, massive amounts of primary and secondary data sources. This collection examines the new challenges posed by data generated online, explores how researchers are addressing those ethical challenges, and provides rich case studies of ethical decision making in the digital age.

Written for anyone beginning a research project, this introductory book takes you through the process of analysing your data from start to finish. The author sets out an easy-to-use model for coding data in order to break it down into parts, and then to reassemble it to create a meaningful picture of the phenomenon under study. Full of useful advice, the book guides the reader through the last difficult integrating phase of qualitative analysis including diagramming, memoing, thinking aloud, and using one's feelings, and how to incorporate the use of software where appropriate. Ideal for third year undergraduate students, master students, postgraduates and anybody beginning a research project, the book includes examples covering a wide range of subjects - making the book useful for students across the social science disciplines. Hennie Boeije is currently an Associate Professor with the Department of Methodology and Statistics of the Faculty of Social and Behavioural Sciences at Utrecht University, The Netherlands.

This book highlights some of the unique aspects of spatio-temporal graph data from the perspectives of modeling and developing scalable algorithms. The authors discuss in the first part of this book, the semantic aspects of spatio-temporal graph data in two application domains, viz., urban transportation and social networks. Then the authors present representational models and data structures, which can effectively capture these semantics, while ensuring support for computationally scalable algorithms. In the first part of the book, the authors describe algorithmic development issues in spatio-temporal graph data. These algorithms internally use the semantically rich data structures developed in the earlier part of this book. Finally, the authors introduce some upcoming spatio-temporal graph datasets, such as engine measurement data, and discuss some open research problems in the area. This book will be useful as a secondary text for advanced-level students entering into relevant fields of computer science, such as transportation and urban planning. It may also be useful for researchers and practitioners in the field of navigational algorithms.

"Modeling with Data" fully explains how to execute computationally intensive analyses on very large data sets, showing readers how to determine the best methods for solving a variety of different problems, how to create and debug statistical models, and how to run an analysis and evaluate the results.

Ben Klemens introduces a set of open and unlimited tools, and uses them to demonstrate data management, analysis, and simulation techniques essential for dealing with large data sets and computationally intensive procedures. He then demonstrates how to easily apply these tools to the many threads of statistical technique, including classical, Bayesian, maximum likelihood, and Monte Carlo methods. Klemens's accessible survey describes these models in a unified and nontraditional manner, providing alternative ways of looking at statistical concepts that often befuddle students. The book includes nearly one hundred sample programs of all kinds. Links to these programs will be available on this page at a later date.

"Modeling with Data" will interest anyone looking for a comprehensive guide to these powerful statistical tools, including researchers and graduate students in the social sciences, biology, engineering, economics, and applied mathematics.

Classification and regression trees (CART) is one of the several contemporary statistical techniques with good promise for research in many academic fields. There are very few books on CART, especially on applied CART. This book, as a good practical primer with a focus on applications, introduces the relatively new statistical technique of CART as a powerful analytical tool. The easy-to-understand (non-technical) language and illustrative graphs (tables) as well as the use of the popular statistical software program (SPSS) appeal to readers without strong statistical background. This book helps readers understand the foundation, the operation, and the interpretation of CART analysis, thus becoming knowledgeable consumers and skillful users of CART. The chapter on advanced CART procedures not yet well-discussed in the literature allows readers to effectively seek further empowerment of their research designs by extending the analytical power of CART to a whole new level. This highly practical book is specifically written for academic researchers, data analysts, and graduate students in many disciplines such as economics, social sciences, medical sciences, and sport sciences who do not have strong statistical background but still strive to take full advantage of CART as a powerful analytical tool for research in their fields.

In this provocative yet practical guidebook Steve Morlidge demonstrates why the approach and methods of performance reporting that all information professionals have been taught fails, and what we need to do differently to help us make sense of the dynamic, complex and data rich world in which we now live and work. Reporting on performance should not be treated as worthy but dull, requiring no more than routine comparisons of actual against targets. This traditional approach is based on the false premise organisations can be managed as if they were a simple mechanical system operating in a predictable environment. And the methods associated with it, such as variance analyses and data tables that are used to measure and communicate performance, are completely inadequate. Instead, Morlidge argues performance reporting should be reconceived as an act of perception conducted on behalf of the organisation, helping to make sense of the sensory inputs (data) that it has at its disposal. And to do so effectively performance reporters need to learn from and exploit the strengths of our own brains, compensate for its weaknesses and communicate in a way that makes it easy for their audience's brains to assimilate. Drawing on the latest insights from cognitive science in this book you will learn: * how to bring a dynamic perspective into performance reporting * how to deploy a set of simple tools to help speared the signal from the noise inherent in large data sets and to make sound inferences * how to set goals intelligently * about the grammar of data visualization and how use it to design powerful and simple reports In this way information professionals are uniquely charged with the responsibility for creating the shared consciousness that is a prerequisite for organisations to effectively respond and adapt to their environments.

Edited by Terri D. Pigott, Ann Marie Ryan, and Charles Tocci, the purpose of this volume is to present high-quality reviews that examine change to teaching practice from a variety of perspectives and a range of disciplines with an eye toward the enormous scope of the field. Taken as a whole, this volume presents a compelling profile of the core challenges and opportunities facing those engaged in the work of changing teaching practice and those who research these efforts. Divided into four sections, the first section of this volume delves into the history and policy of changing teaching practice, the second set of chapters consider the capacity of teachers to make changes, the third set of chapters review literature examining how to change practice in numerous settings in various ways, and the final section of the volume centers on emerging issues for practice. This volume considers some of the most critical problems facing educators and scholars today: how our history shapes our present-day possibilities, how we develop the capacity of educators to change and improve practice, the innumerable aspects that can be changed, which dimensions of teaching should we prioritize, and what emerging issues will shape this work in the coming years?

Classification and regression trees (CART) is one of the several contemporary statistical techniques with good promise for research in many academic fields. There are very few books on CART, especially on applied CART. This book, as a good practical primer with a focus on applications, introduces the relatively new statistical technique of CART as a powerful analytical tool. The easy-to-understand (non-technical) language and illustrative graphs (tables) as well as the use of the popular statistical software program (SPSS) appeal to readers without strong statistical background. This book helps readers understand the foundation, the operation, and the interpretation of CART analysis, thus becoming knowledgeable consumers and skillful users of CART. The chapter on advanced CART procedures not yet well-discussed in the literature allows readers to effectively seek further empowerment of their research designs by extending the analytical power of CART to a whole new level. This highly practical book is specifically written for academic researchers, data analysts, and graduate students in many disciplines such as economics, social sciences, medical sciences, and sport sciences who do not have strong statistical background but still strive to take full advantage of CART as a powerful analytical tool for research in their fields.