Interactive Learning Through Visualization
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Interactive Learning Through Visualization
Author: Steve Cunningham
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-12-06
This book contains a selection of papers presented at the Computer Graphics and Education '91 Conference, held from 4th to 6th Apri11991, in Begur, Spain. The conference was organised under the auspices of the International Federation for Information Processing (IPIP) Working Group 5.10 on Computer Graphics. The goal of the organisers was to take a forward look at the impact on education of anticipated developments in graphics and related technologies, such as multimedia, in the next five years. We felt that at a time when many educational establishments are facing financial stringency and when major changes are taking place in patterns of education and training, this could be valuable for both educators and companies developing the technology: for educators, because they are often too bogged down in day-to-day problems to undertake adequate forward planning, and for companies, to see some of the problems faced by educators and to see what their future requirements might be.
Learning Through Visual Displays
The purpose of the volume is to explore the theory, development and use of visual displays and graphic organizers to improve instruction, learning and research. We anticipate five sections that address (1) frameworks for understanding different types of displays, (2) research-tested guidelines for constructing displays, (3) empirically-based instructional applications, (4) using displays to promote research and theory development, and (5) using displays to report test and research data to improve consumer understanding. Authors represent a variety of perspectives and areas of expertise, including instructional psychology, information technology, and research methodologies. The volume is divided into four sections. Section 1 provides a conceptual overview of previous research, as well as the contents of the current volume. Section 2 includes theoretical perspectives on the design and instructional uses of visual displays from major theorists in the field. These chapters discuss ways that visual displays enhance general cognition and information processing. Section 3 provides eight chapters that address the use of visual displays to enhance student learning. These chapters provide examples of how to organize content and use visual displays in a variety of ways in the real and virtual classroom. Section 4 includes three chapters that discuss ways that visual displays may enhance the research process, but especially improved data display.
Interactive Data Visualization with Python
Author: Abha Belorkar
language: en
Publisher: Packt Publishing Ltd
Release Date: 2020-04-14
Create your own clear and impactful interactive data visualizations with the powerful data visualization libraries of Python Key FeaturesStudy and use Python interactive libraries, such as Bokeh and PlotlyExplore different visualization principles and understand when to use which oneCreate interactive data visualizations with real-world dataBook Description With so much data being continuously generated, developers, who can present data as impactful and interesting visualizations, are always in demand. Interactive Data Visualization with Python sharpens your data exploration skills, tells you everything there is to know about interactive data visualization in Python. You'll begin by learning how to draw various plots with Matplotlib and Seaborn, the non-interactive data visualization libraries. You'll study different types of visualizations, compare them, and find out how to select a particular type of visualization to suit your requirements. After you get a hang of the various non-interactive visualization libraries, you'll learn the principles of intuitive and persuasive data visualization, and use Bokeh and Plotly to transform your visuals into strong stories. You'll also gain insight into how interactive data and model visualization can optimize the performance of a regression model. By the end of the course, you'll have a new skill set that'll make you the go-to person for transforming data visualizations into engaging and interesting stories. What you will learnExplore and apply different interactive data visualization techniquesManipulate plotting parameters and styles to create appealing plotsCustomize data visualization for different audiencesDesign data visualizations using interactive librariesUse Matplotlib, Seaborn, Altair and Bokeh for drawing appealing plotsCustomize data visualization for different scenariosWho this book is for This book intends to provide a solid training ground for Python developers, data analysts and data scientists to enable them to present critical data insights in a way that best captures the user's attention and imagination. It serves as a simple step-by-step guide that demonstrates the different types and components of visualization, the principles, and techniques of effective interactivity, as well as common pitfalls to avoid when creating interactive data visualizations. Students should have an intermediate level of competency in writing Python code, as well as some familiarity with using libraries such as pandas.