Products related to Data:
-
Culturally Responsive Data Literacy
Vulnerable students have been marginalized through the use of accountability data for many years.This book describes an important construct, culturally responsive data literacy (CRDL), which merges data literacy and culturally responsive practices to help educators assume a whole child perspective, an asset model, and an equity lens to the examination and use of data.CRDL promotes the use of diverse data sources such as students’ personal backgrounds as well as academic history to help educators better meet individual needs, rather than reducing students to a single test score.The book provides authentic scenarios developed around topics educators may encounter in practice.It presents guiding questions that can help educators examine unconscious bias.The book, in taking a systems approach, seeks to reach educator preparation programs, in-service technical assistance providers, and professional organizations to build awareness of the importance of CRDL and to provide usable resources for educators at all levels. -The only all-in-one resource showing how to use data literacy to support culturally responsiveness in PK-12 classrooms. -There are few options that explore, and explore well, what it means to be culturally responsive when it comes to the assessment of students. -Accessible and practical for teachers, cohesively tied to theory underlying the practice. -Valuable resource for teacher professional development as well as preservice teacher preparation, with an abundance of concrete, practical examples and scenarios with actionable recommendations, not merely theory.
Price: 25.00 £ | Shipping*: 3.99 £ -
Interactive Visual Data Analysis
In the age of big data, being able to make sense of data is an important key to success.Interactive Visual Data Analysis advocates the synthesis of visualization, interaction, and automatic computation to facilitate insight generation and knowledge crystallization from large and complex data. The book provides a systematic and comprehensive overview of visual, interactive, and analytical methods.It introduces criteria for designing interactive visual data analysis solutions, discusses factors influencing the design, and examines the involved processes.The reader is made familiar with the basics of visual encoding and gets to know numerous visualization techniques for multivariate data, temporal data, geo-spatial data, and graph data.A dedicated chapter introduces general concepts for interacting with visualizations and illustrates how modern interaction technology can facilitate the visual data analysis in many ways.Addressing today’s large and complex data, the book covers relevant automatic analytical computations to support the visual data analysis.The book also sheds light on advanced concepts for visualization in multi-display environments, user guidance during the data analysis, and progressive visual data analysis. The authors present a top-down perspective on interactive visual data analysis with a focus on concise and clean terminology.Many real-world examples and rich illustrations make the book accessible to a broad interdisciplinary audience from students, to experts in the field, to practitioners in data-intensive application domains. Features:Dedicated to the synthesis of visual, interactive, and analysis methodsSystematic top-down view on visualization, interaction, and automatic analysisBroad coverage of fundamental and advanced visualization techniquesComprehensive chapter on interacting with visual representationsExtensive integration of automatic computational methodsAccessible portrayal of cutting-edge visual analytics technologyForeword by Jack van WijkFor more information, you can also visit the author website, where the book's figures are made available under the CC BY Open Access license.
Price: 56.99 £ | Shipping*: 0.00 £ -
Modern Data Strategy
This book contains practical steps business users can take to implement data management in a number of ways, including data governance, data architecture, master data management, business intelligence, and others.It defines data strategy, and covers chapters that illustrate how to align a data strategy with the business strategy, a discussion on valuing data as an asset, the evolution of data management, and who should oversee a data strategy.This provides the user with a good understanding of what a data strategy is and its limits. Critical to a data strategy is the incorporation of one or more data management domains.Chapters on key data management domains—data governance, data architecture, master data management and analytics, offer the user a practical approach to data management execution within a data strategy.The intent is to enable the user to identify how execution on one or more data management domains can help solve business issues. This book is intended for business users who work with data, who need to manage one or more aspects of the organization’s data, and who want to foster an integrated approach for how enterprise data is managed.This book is also an excellent reference for students studying computer science and business management or simply for someone who has been tasked with starting or improving existing data management.
Price: 129.99 £ | Shipping*: 0.00 £ -
Data Management at Scale : Modern Data Architecture with Data Mesh and Data Fabric
As data management continues to evolve rapidly, managing all of your data in a central place, such as a data warehouse, is no longer scalable.Today's world is about quickly turning data into value.This requires a paradigm shift in the way we federate responsibilities, manage data, and make it available to others.With this practical book, you'll learn how to design a next-gen data architecture that takes into account the scale you need for your organization. Executives, architects and engineers, analytics teams, and compliance and governance staff will learn how to build a next-gen data landscape.Author Piethein Strengholt provides blueprints, principles, observations, best practices, and patterns to get you up to speed. Examine data management trends, including regulatory requirements, privacy concerns, and new developments such as data mesh and data fabricGo deep into building a modern data architecture, including cloud data landing zones, domain-driven design, data product design, and moreExplore data governance and data security, master data management, self-service data marketplaces, and the importance of metadata
Price: 59.99 £ | Shipping*: 0.00 £
-
What are generic and dynamic data structures?
Generic data structures are data structures that can hold any type of data, such as integers, strings, or custom objects. They are designed to be flexible and reusable across different data types. Dynamic data structures are data structures that can change in size during program execution, such as dynamic arrays, linked lists, and trees. They allow for efficient memory usage and can adapt to the changing needs of the program. Both generic and dynamic data structures are important in programming as they provide flexibility and efficiency in managing and manipulating data.
-
When does Spotify collect user data?
Spotify collects user data when users interact with the platform, such as when they create an account, listen to music, create playlists, or engage with ads. The platform also collects data when users provide information voluntarily, such as when they fill out their profile or preferences. Additionally, Spotify may collect data from third-party sources or through cookies and similar technologies when users visit the website or use the app.
-
How can user data be moved?
User data can be moved through various methods such as manual transfer, automated migration tools, cloud storage services, and data backup and restore processes. Manual transfer involves copying and pasting files or using external storage devices to move data. Automated migration tools can help transfer data between different systems or platforms seamlessly. Cloud storage services allow users to upload their data to the cloud and access it from anywhere. Data backup and restore processes involve creating backups of user data and restoring them to a new location when needed.
-
How does the processing of data differ between interactive and static applications?
The processing of data differs between interactive and static applications in several ways. In interactive applications, data processing is often ongoing and dynamic, as the application responds to user input in real-time. This requires constant interaction with the data, updating and manipulating it as the user interacts with the application. On the other hand, static applications typically process data in a one-time, pre-determined manner, as the data is presented to the user without any further interaction or manipulation. This means that the processing of data in static applications is often more straightforward and does not require ongoing updates or changes based on user input.
Similar search terms for Data:
-
Deciphering Data Architectures : Choosing Between a Modern Data Warehouse, Data Fabric, Data Lakehouse, and Data Mesh
Data fabric, data lakehouse, and data mesh have recently appeared as viable alternatives to the modern data warehouse.These new architectures have solid benefits, but they're also surrounded by a lot of hyperbole and confusion.This practical book provides a guided tour of each architecture to help data professionals understand its pros and cons. In the process, James Serra, big data and data warehousing solution architect at Microsoft, examines common data architecture concepts, including how data warehouses have had to evolve to work with data lake features.You'll learn what data lakehouses can help you achieve, and how to distinguish data mesh hype from reality.Best of all, you'll be able to determine the most appropriate data architecture for your needs.By reading this book, you'll:Gain a working understanding of several data architecturesKnow the pros and cons of each approachDistinguish data architecture theory from the realityLearn to pick the best architecture for your use caseUnderstand the differences between data warehouses and data lakesLearn common data architecture concepts to help you build better solutionsAlleviate confusion by clearly defining each data architectureKnow what architectures to use for each cloud provider
Price: 63.99 £ | Shipping*: 0.00 £ -
Modern, Minimalist, Creative Catering, Touch Controlled Night Lights, Coffee Rooms, Professional
Modern, Minimalist, Creative Catering, Touch Controlled Night Lights, Coffee Rooms, Professional
Price: 10.29 € | Shipping*: 0 € -
Modern Data Science with R
From a review of the first edition: "Modern Data Science with R… is rich with examples and is guided by a strong narrative voice.What’s more, it presents an organizing framework that makes a convincing argument that data science is a course distinct from applied statistics" (The American Statistician). Modern Data Science with R is a comprehensive data science textbook for undergraduates that incorporates statistical and computational thinking to solve real-world data problems.Rather than focus exclusively on case studies or programming syntax, this book illustrates how statistical programming in the state-of-the-art R/RStudio computing environment can be leveraged to extract meaningful information from a variety of data in the service of addressing compelling questions. The second edition is updated to reflect the growing influence of the tidyverse set of packages.All code in the book has been revised and styled to be more readable and easier to understand.New functionality from packages like sf, purrr, tidymodels, and tidytext is now integrated into the text.All chapters have been revised, and several have been split, re-organized, or re-imagined to meet the shifting landscape of best practice.
Price: 84.99 £ | Shipping*: 0.00 £ -
Modern Data Visualization with R
Modern Data Visualization with R describes the many ways that raw and summary data can be turned into visualizations that convey meaningful insights.It starts with basic graphs such as bar charts, scatter plots, and line charts, but progresses to less well-known visualizations such as tree maps, alluvial plots, radar charts, mosaic plots, effects plots, correlation plots, biplots, and the mapping of geographic data.Both static and interactive graphics are described and the use of color, shape, shading, grouping, annotation, and animations are covered in detail.The book moves from a default look and feel for graphs, to graphs with customized colors, fonts, legends, annotations, and organizational themes. FeaturesContains a wide breadth of graph types including newer and less well-known approachesConnects each graph type to the characteristics of the data and the goals of the analysisMoves the reader from simple graphs describing one variable to building visualizations that describe complex relationships among many variablesProvides newer approaches to creating interactive web graphics via JavaScript librariesDetails how to customize each graph type to meet users’ needs and those of their audiencesGives methods for creating visualizations that are publication ready for print (in color or black and white) and the webSuggests best practicesOffers examples from a wide variety of fieldsThe book is written for those new to data analysis as well as the seasoned data scientist.It can be used for both teaching and research, and will particularly appeal to anyone who needs to describe data visually and wants to find and emulate the most appropriate method quickly.The reader should have some basic coding experience, but expertise in R is not required.Some of the later chapters (e.g., visualizing statistical models) assume exposure to statistical inference at the level of analysis of variance and regression.
Price: 63.99 £ | Shipping*: 0.00 £
-
Is my user account user-friendly?
To determine if your user account is user-friendly, consider factors such as ease of navigation, clear instructions, and accessibility features. Evaluate if users can easily find the information they need, if the interface is intuitive, and if there are options for customization. Conduct user testing to gather feedback on the account's usability and make improvements based on the results. Overall, a user-friendly account should prioritize the user experience and make it simple for users to accomplish their tasks efficiently.
-
What is the formula for a dynamic data range in Excel?
To create a dynamic data range in Excel, you can use a formula that utilizes the OFFSET function. The formula typically looks like this: =OFFSET(starting cell, 0, 0, COUNTA(column range), number of columns). This formula allows the range to automatically adjust as new data is added or removed from the specified column range. By using the OFFSET function in this way, you can ensure that your data range always includes the most up-to-date information.
-
Does Opera actually sell user data to third parties?
No, Opera does not sell user data to third parties. Opera has a strong commitment to user privacy and states that they do not sell personal information to anyone. They have a strict privacy policy in place to protect user data and ensure that it is not shared with any third parties without consent.
-
Where does the Microsoft Edge browser store user data?
Microsoft Edge browser stores user data in a variety of locations on the device. This includes browsing history, cookies, and cached files which are stored in the user's profile folder. Additionally, bookmarks and settings are stored in the user's AppData folder. Microsoft Edge also syncs user data to the cloud if the user is signed in with a Microsoft account.
* All prices are inclusive of VAT and, if applicable, plus shipping costs. The offer information is based on the details provided by the respective shop and is updated through automated processes. Real-time updates do not occur, so deviations can occur in individual cases.