Data, Data Everywhere

Author: Victoria L. Bernhardt
Publisher: Eye On Education
ISBN: 1596671025
Category : Business & Economics
Languages : en
Pages : 122

Book Description
This book is an easy-to-read primer that describes what it takes to increase student achievement at every grade level, subject area, and student group. Readers will learn how to use data to drive their continuous improvement process as they develop an appreciation of the various types of data, uses for data, and how data are involved with the school improvement process. Online Course Available through a partnerhip with Knowledge Delivery Systems. Click here for more information. (CEUs may be available through your district.)

Data, Data Everywhere

Author: Victoria L. Bernhardt
Publisher: Routledge
ISBN: 1317930762
Category : Education
Languages : en
Pages : 103

Data Management. Data, Data Everywhere

Author: Richard Cooper
Publisher: Springer
ISBN: 3540733906
Category : Computers
Languages : en
Pages : 272

Book Description
This book features the refereed proceedings from the 24th British National Conference on Databases, held in Glasgow, Scotland in July 2007. The eighteen full papers and seven poster papers are presented, together with two invited contributions. Papers are organized into topical sections covering data applications, searching XML documents, querying XML documents, XML transformation, clustering and security, data mining, and extraction.

Learning Analytics in Higher Education

Author: Jaime Lester
Publisher: Routledge
ISBN: 1351400525
Category : Education
Languages : en
Pages : 290

Book Description
Learning Analytics in Higher Education provides a foundational understanding of how learning analytics is defined, what barriers and opportunities exist, and how it can be used to improve practice, including strategic planning, course development, teaching pedagogy, and student assessment. Well-known contributors provide empirical, theoretical, and practical perspectives on the current use and future potential of learning analytics for student learning and data-driven decision-making, ways to effectively evaluate and research learning analytics, integration of learning analytics into practice, organizational barriers and opportunities for harnessing Big Data to create and support use of these tools, and ethical considerations related to privacy and consent. Designed to give readers a practical and theoretical foundation in learning analytics and how data can support student success in higher education, this book is a valuable resource for scholars and administrators.

Foundations of Data Science

Author: Avrim Blum
Publisher: Cambridge University Press
ISBN: 1108617360
Category : Computers
Languages : en
Pages : 433

Book Description
This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data.

Handbook of Graphs and Networks in People Analytics

Author: Keith McNulty
Publisher: CRC Press
ISBN: 100059727X
Category : Business & Economics
Languages : en
Pages : 266

Book Description
Handbook of Graphs and Networks in People Analytics: With Examples in R and Python covers the theory and practical implementation of graph methods in R and Python for the analysis of people and organizational networks. Starting with an overview of the origins of graph theory and its current applications in the social sciences, the book proceeds to give in-depth technical instruction on how to construct and store graphs from data, how to visualize those graphs compellingly and how to convert common data structures into graph-friendly form. The book explores critical elements of network analysis in detail, including the measurement of distance and centrality, the detection of communities and cliques, and the analysis of assortativity and similarity. An extension chapter offers an introduction to graph database technologies. Real data sets from various research contexts are used for both instruction and for end of chapter practice exercises and a final chapter contains data sets and exercises ideal for larger personal or group projects of varying difficulty level. Key features: Immediately implementable code, with extensive and varied illustrations of graph variants and layouts. Examples and exercises across a variety of real-life contexts including business, politics, education, social media and crime investigation. Dedicated chapter on graph visualization methods. Practical walkthroughs of common methodological uses: finding influential actors in groups, discovering hidden community structures, facilitating diverse interaction in organizations, detecting political alignment, determining what influences connection and attachment. Various downloadable data sets for use both in class and individual learning projects. Final chapter dedicated to individual or group project examples.

Dear Data

Author: Giorgia Lupi
Publisher: Chronicle Books
ISBN: 1616895462
Category : Design
Languages : en
Pages : 304

Book Description
Equal parts mail art, data visualization, and affectionate correspondence, Dear Data celebrates "the infinitesimal, incomplete, imperfect, yet exquisitely human details of life," in the words of Maria Popova (Brain Pickings), who introduces this charming and graphically powerful book. For one year, Giorgia Lupi, an Italian living in New York, and Stefanie Posavec, an American in London, mapped the particulars of their daily lives as a series of hand-drawn postcards they exchanged via mail weekly—small portraits as full of emotion as they are data, both mundane and magical. Dear Data reproduces in pinpoint detail the full year's set of cards, front and back, providing a remarkable portrait of two artists connected by their attention to the details of their lives—including complaints, distractions, phone addictions, physical contact, and desires. These details illuminate the lives of two remarkable young women and also inspire us to map our own lives, including specific suggestions on what data to draw and how. A captivating and unique book for designers, artists, correspondents, friends, and lovers everywhere.

Creating Value with Big Data Analytics

Author: Peter C. Verhoef
Publisher: Routledge
ISBN: 1317561929
Category : Business & Economics
Languages : en
Pages : 339

Book Description
Our newly digital world is generating an almost unimaginable amount of data about all of us. Such a vast amount of data is useless without plans and strategies that are designed to cope with its size and complexity, and which enable organisations to leverage the information to create value. This book is a refreshingly practical, yet theoretically sound roadmap to leveraging big data and analytics. Creating Value with Big Data Analytics provides a nuanced view of big data development, arguing that big data in itself is not a revolution but an evolution of the increasing availability of data that has been observed in recent times. Building on the authors’ extensive academic and practical knowledge, this book aims to provide managers and analysts with strategic directions and practical analytical solutions on how to create value from existing and new big data. By tying data and analytics to specific goals and processes for implementation, this is a much-needed book that will be essential reading for students and specialists of data analytics, marketing research, and customer relationship management.

Data Analysis for Continuous School Improvement

Author: Victoria L. Bernhardt
Publisher: Routledge
ISBN: 1351587862
Category : Education
Languages : en
Pages : 426

Book Description
Data Analysis for Continuous School Improvement provides a new definition of school improvement, away from a singular focus on compliance, toward a true commitment to excellence. This book is a call to action. It is about inspiring schools and school districts to commit to continuous school improvement by providing a framework that will result in improving teaching for every teacher and learning for every student through the comprehensive use of data. A culmination of over 30 years of doing the hard work in schools and districts both nationally and internationally, Data Analysis for Continuous School Improvement shares new, evidence-based learnings about how to analyze, report, communicate, and use multiple measures of data. The updated edition provides a wealth of tools, protocols, timelines, examples, and strategies that will help schools and districts become genuine learning organizations.

Data Analysis for Continuous School Improvement

Author: Victoria Bernhardt
Publisher: Routledge
ISBN: 1317930800
Category : Education
Languages : en
Pages : 321

Book Description
5 PERCEPTIONS; Changing Perceptions; Assessing Perceptions; Questionnaires; Designing Questionnaires: Begin With the End in Mind; Data Collection Considerations; Our Example Schools; Study Questions for Perceptions; Summary; 6 STUDENT LEARNING; Ways to Measure Student Learning; Grades; Analyzing the Results, Descriptively; Analyzing the Results, Inferentially; Measurement Error; Looking Across Student Learning Measures; Other Common Testing Terms; Our Example Schools; Study Questions for Student Learning; Summary; 7 SCHOOL PROCESSES; School Level Processes; Classroom Level Processes.