Learning Excel for Economics & Business: Data Visualization Topics

This book closes the gap between what people think Excel can do and what they can achieve in the tool., by providing a step-by-step guide to creating more advanced and often more effective data visualizations in Excel. It is a guide for anyone who wants to create better, more effective, and more engaging data visualizations.

This book describes how to apply fundamental color concepts to digital media and visualization solutions. It highlights specific color concepts such as color harmony and data color schemes, and gives examples of generative AI solutions for color scheme suggestion. A five-stage colorization process is defined and applied to case study examples.

The author introduces a set of basic principles for writing about numbers, then presents a toolkit of techniques that can be applied to prose, tables, charts, and presentations. Throughout the book, she emphasizes flexibility, showing writers that different approaches work for different kinds of data and different types of audiences.

The author is the director of visual and data journalism for the Financial Times, a source well-known for its graphic communication. Charts, graphs and tables are essential devices in business, but all too often they present information poorly. This book will help you feel confident understanding different types of charts, graphs and table and how to read them. You’ll gain skill recognizing the true story behind the data presented and what the information really shows. The content provides background principles and rules of how best to represent information, with the goal for you to be able to design visuals that people engage with, understand and act upon.

This book offers a comprehensive review of a wide array of technical and analytical concepts that will help users create meaningful reports and dashboards. After reading this book, the reader will be able to: analyze large amounts of data and report their data in a meaningful way; get better visibility into data from different perspectives; quickly slice data into various views on the fly; automate redundant reporting and analyses; create impressive dashboards and What-If analyses; understand the fundamentals of effective visualization; visualize performance comparisons; and visualize changes and trends over time.

The authors document the deeply creative process behind 24 unique data visualization projects, and they combine this with powerful technical insights which reveal the mindset behind coding creatively. The book addresses processes like data gathering, sketching, and coding data visualizations for the web, with screenshots of works-in-progress and reproductions from the authors' notebooks. This book is perfect for anyone interested or working in data visualization and information design, and especially those who want to take their work to the next level and are inspired by unique and compelling data-driven storytelling.

This item examines the interdependent relationship between method and practice. Data Verbalization is about communicating and disseminating research data using performance approaches and techniques such as; spoken word, jazz, hip-hop, and reggae theatrics. Martin Glynn calls for the development of performance-driven research dissemination that seeks to bring urgent attention to minority, excluded, and marginalized perspectives within research dissemination as a whole. Employing the data verbalization method creates an exciting new proposition that can give progressive researchers a unique and distinct voice, alongside generating significant reach and impact beyond the academy, conference, and peer reviewed journal. The book will be an invaluable resource for researchers, scholars, and related practitioners who want to strengthen their ability to communicate and disseminate research data using live performance / spoken word approaches and techniques. It will also provide guidance for students and researchers wanting to generate wider environmental, social, and cultural impact using research data creatively.

Effective visualization is the best way to communicate information from the increasingly large and complex datasets in the natural and social sciences. This book illustrates commonly encountered visualization problems, and it provides guidelines on how to turn large datasets into clear and compelling figures. What visualization type is best for the story you want to tell? Topics covered include use of color, redundant coding, good and bad examples, using figures to tell a compelling story. The book also contains a visualizations directory, a graphic guide to commonly-used types of visualizations.

Edward Tufte 'wrote the book' on data visualization; this book is one of several that have been influential.

Overview of using R given by experienced data analyst, consultant and professor.

Making good charts is a must-have skill for managers today. This book shows how anyone who invests a little time getting better at visual communication can create an outsized impact--both in their career and in their organization. The author teaches how to produce quality visualizations using effective information design. Topics include a framework for making better charts in minutes, the building blocks for storytelling with your data, and much more.

This book highlights methodological developments in the way that data analytics is both learned and taught. Featuring extensive coverage on emerging relevant topics such as data complexity, statistics education, and curriculum development, this publication is geared toward teachers, academicians, students, engineers, professionals, and researchers that are interested in expanding their knowledge of data examination and analysis.

Data literacy is the ability to “read” and “write” effectively with data, and it has become a critical skill. How to teach it? This open access publication emerged from an IMLS-funded project Supporting Librarians in Adding Data Literacy Skills to Information Literacy Instruction. There are over 20 chapters for educators and librarians about teaching aspects of data literacy that touch on themes of personal data management, big data, citizen science and ethical data use.