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Author: Yanni Alexander Loukissas
Publisher: MIT Press
ISBN: 0262039664
Category : Computers
Languages : en
Pages : 267
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Book Description
How to analyze data settings rather than data sets, acknowledging the meaning-making power of the local. In our data-driven society, it is too easy to assume the transparency of data. Instead, Yanni Loukissas argues in All Data Are Local, we should approach data sets with an awareness that data are created by humans and their dutiful machines, at a time, in a place, with the instruments at hand, for audiences that are conditioned to receive them. The term data set implies something discrete, complete, and portable, but it is none of those things. Examining a series of data sources important for understanding the state of public life in the United States—Harvard's Arnold Arboretum, the Digital Public Library of America, UCLA's Television News Archive, and the real estate marketplace Zillow—Loukissas shows us how to analyze data settings rather than data sets. Loukissas sets out six principles: all data are local; data have complex attachments to place; data are collected from heterogeneous sources; data and algorithms are inextricably entangled; interfaces recontextualize data; and data are indexes to local knowledge. He then provides a set of practical guidelines to follow. To make his argument, Loukissas employs a combination of qualitative research on data cultures and exploratory data visualizations. Rebutting the “myth of digital universalism,” Loukissas reminds us of the meaning-making power of the local.
Author: Yanni Alexander Loukissas
Publisher: MIT Press
ISBN: 0262039664
Category : Computers
Languages : en
Pages : 267
Get Book Here
Book Description
How to analyze data settings rather than data sets, acknowledging the meaning-making power of the local. In our data-driven society, it is too easy to assume the transparency of data. Instead, Yanni Loukissas argues in All Data Are Local, we should approach data sets with an awareness that data are created by humans and their dutiful machines, at a time, in a place, with the instruments at hand, for audiences that are conditioned to receive them. The term data set implies something discrete, complete, and portable, but it is none of those things. Examining a series of data sources important for understanding the state of public life in the United States—Harvard's Arnold Arboretum, the Digital Public Library of America, UCLA's Television News Archive, and the real estate marketplace Zillow—Loukissas shows us how to analyze data settings rather than data sets. Loukissas sets out six principles: all data are local; data have complex attachments to place; data are collected from heterogeneous sources; data and algorithms are inextricably entangled; interfaces recontextualize data; and data are indexes to local knowledge. He then provides a set of practical guidelines to follow. To make his argument, Loukissas employs a combination of qualitative research on data cultures and exploratory data visualizations. Rebutting the “myth of digital universalism,” Loukissas reminds us of the meaning-making power of the local.
Author: Nancy Love
Publisher: Corwin Press
ISBN: 1412960851
Category : Education
Languages : en
Pages : 193
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Book Description
Collaborative inquiry + effective use of data = significant leaps in learning and achievement! This resource combines a powerful collaborative inquiry process, reflective dialogue, and rigorous use of data to improve outcomes for all students. The editor and contributors provide detailed examples of schools that have demonstrated dramatic gains by building collaborative cultures, nurturing ongoing inquiry, and using data systematically. The book shows school leaders how to: Implement collaborative inquiry to meet accountability mandates Build and support a high-performing data culture Establish a school climate characterized by collective responsibility for student learning and a respect for students’ cultures
Author: Len Silverston
Publisher: John Wiley & Sons
ISBN: 111808232X
Category : Computers
Languages : en
Pages : 572
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Book Description
A quick and reliable way to build proven databases for core business functions Industry experts raved about The Data Model Resource Book when it was first published in March 1997 because it provided a simple, cost-effective way to design databases for core business functions. Len Silverston has now revised and updated the hugely successful 1st Edition, while adding a companion volume to take care of more specific requirements of different businesses. This updated volume provides a common set of data models for specific core functions shared by most businesses like human resources management, accounting, and project management. These models are standardized and are easily replicated by developers looking for ways to make corporate database development more efficient and cost effective. This guide is the perfect complement to The Data Model Resource CD-ROM, which is sold separately and provides the powerful design templates discussed in the book in a ready-to-use electronic format. A free demonstration CD-ROM is available with each copy of the print book to allow you to try before you buy the full CD-ROM.
Author: Victoria Bernhardt
Publisher: Routledge
ISBN: 1317930770
Category : Education
Languages : en
Pages : 129
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Book Description
The latest book from best-selling author Victoria L. Bernhardt is an easy-to-read primer that describes what it takes to achieve student learning growth at every grade level, in every subject area, and with every student group.
Author: Anna Bernasek
Publisher: Bold Type Books
ISBN: 1568584741
Category : Business & Economics
Languages : en
Pages : 258
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Book Description
Examines the corporate practice of gathering massive amounts of data on consumer behaviors, needs, and desires, and using it to influence decision making and determine how much individuals will pay based on how much they are able to.
Author: Nancy Love
Publisher: Corwin
ISBN: 1071841440
Category : Education
Languages : en
Pages : 409
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Book Description
"This book offers a compelling message of hope and resolve. The authors′ three-year journey in a multiplicity of diverse, underperforming, high-poverty schools across the nation has resulted in a treasure chest of knowledge and experiences about how to professionally develop data coaches in ways that benefit some of our most underserved students. This book provides powerful resources to those who have the belief, passion, and desire for implementing collaborative data inquiries in schools and districts." —From the Foreword by Ruth S. Johnson Use data as an effective tool for school change and improvement! How can data coaches create a collaborative culture in which data is used continuously and effectively to improve teaching and learning? The Data Coach′s Guide to Improving Learning for All Students provides detailed guidance for helping schools move away from unproductive data practices and toward examining data as a catalyst for systematic and continuous improvement in instruction and student learning. To help both current and aspiring data coaches facilitate school-based data teams and lead teachers in collaborative inquiry, the authors demonstrate a data model that has been field-tested and proven to be effective in: Narrowing achievement gaps between students in all content areas and grade levels Achieving strong, steady gains in local and state assessments in mathematics, science, and reading Using data as a springboard for powerful conversations about race/ethnicity, class, educational status, gender, and language differences Developing shared values and a vision for creating a high-performing, data-informed school culture This culturally responsive resource benefits staff developers, teachers, and administrators interested in creating change through effective data practices, and includes a CD-ROM keyed to the book, with templates, handouts, PowerPoint slides, resources, and sample goals and agendas.
Author: Patricia Pulliam Phillips
Publisher: John Wiley & Sons
ISBN: 0470179015
Category : Business & Economics
Languages : en
Pages : 192
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Book Description
Data Collection Data Collection is the second of six books in the Measurement and Evaluation Series from Pfeiffer. The proven ROI Methodology--developed by the ROI Institute--provides a practical system for evaluation planning, data collection, data analysis, and reporting. All six books in the series offer the latest tools, most current research, and practical advice for measuring ROI in a variety of settings. Data Collection offers an effective process for collecting data that is essential to the implementation of the ROI Methodology. The authors outline the techniques, processes, and critical issues involved in successful data collection. The book examines the various methods of data collection, including questionnaires, interviews, focus groups, observation, action plans, performance contracts, and monitoring records. Written for evaluators, facilitators, analysts, designers, coordinators, and managers, Data Collection is a valuable guide for collecting data that are adequate in quantity and quality to produce a complete and credible analysis.
Author: Alane J. Starko
Publisher: Prufrock Press
ISBN: 9780936386607
Category : Critical thinking
Languages : en
Pages : 142
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Book Description
Provides some tools of basic research methodology in the areas of critical thinking, information organization and investigative skills.
Author: John Paul Mueller
Publisher: John Wiley & Sons
ISBN: 1119626110
Category : Computers
Languages : en
Pages : 768
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Book Description
Your logical, linear guide to the fundamentals of data science programming Data science is exploding—in a good way—with a forecast of 1.7 megabytes of new information created every second for each human being on the planet by 2020 and 11.5 million job openings by 2026. It clearly pays dividends to be in the know. This friendly guide charts a path through the fundamentals of data science and then delves into the actual work: linear regression, logical regression, machine learning, neural networks, recommender engines, and cross-validation of models. Data Science Programming All-In-One For Dummies is a compilation of the key data science, machine learning, and deep learning programming languages: Python and R. It helps you decide which programming languages are best for specific data science needs. It also gives you the guidelines to build your own projects to solve problems in real time. Get grounded: the ideal start for new data professionals What lies ahead: learn about specific areas that data is transforming Be meaningful: find out how to tell your data story See clearly: pick up the art of visualization Whether you’re a beginning student or already mid-career, get your copy now and add even more meaning to your life—and everyone else’s!
Author: Pablo Casas
Publisher:
ISBN: 9789874273666
Category :
Languages : en
Pages :
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Book Description
This book is a practical guide to problems that commonly arise when developing a machine learning project. The book's topics are: Exploratory data analysis Data Preparation Selecting best variables Assessing Model Performance More information on predictive modeling will be included soon. This book tries to demonstrate what it says with short and well-explained examples. This is valid for both theoretical and practical aspects (through comments in the code). This book, as well as the development of a data project, is not linear. The chapters are related among them. For example, the missing values chapter can lead to the cardinality reduction in categorical variables. Or you can read the data type chapter and then change the way you deal with missing values. You¿ll find references to other websites so you can expand your study, this book is just another step in the learning journey. It's open-source and can be found at http://livebook.datascienceheroes.com
