"Data and information visualization (data viz or info viz) is the practice of designing and creating easy-to-communicate and easy-to-understand graphic or visual representations of a large amount of complex quantitative and qualitative data and information with the help of static, dynamic or interactive visual items."
Creating visuals such as graphs, charts, and maps to better understand and explain historical data.
Types of Data Visualization: Different ways to visually represent data, such as bar charts, line graphs, and maps.
Data Sources and Formats: The various sources of data and the formats in which they can be accessed, such as CSV, JSON, and SQL.
Data Cleaning and Preparation: How to clean, transform, and prepare data for visualization.
Visualization Tools and Software: Popular tools and software used for visualization, such as Tableau, Power BI, and D3.js.
Design Principles: Basic principles of design to create effective visualizations, such as color theory, typography, and layout.
Interactive Visualization: Interactive visualization allows viewers to interact with the visualizations and explore the data from different angles.
Storytelling: Effective data visualization should tell a story and communicate insights that can be easily understood by the viewer.
Dashboards: How to create dynamic dashboards that provide real-time analysis and the ability to make informed decisions.
Geographic Information Systems (GIS): How to create geospatial visualizations and analyze geospatial data.
Data Analysis and Statistics: Understanding basic statistical concepts and data analysis techniques to create meaningful and informative visualizations.
Data Visualization Ethics: Understanding ethical dimensions in data visualization, such as data privacy, transparency, and biases.
Big Data Visualization: Techniques and tools to visualize big data and handle large datasets.
Data Visualization for Social and Cultural History: Specific techniques and considerations required for data visualization in domains like social and cultural history.
Data Visualization in Digital Humanities: Applications of data visualization in digital humanities research.
Data Journalism: Data Visualization's role in data-driven journalism.
Information Visualization: The visual representation of information in a way that simplifies understanding the given dataset.
Graphical Perception: The ability of humans to interpret and discern different forms of visual representation.
Human-Computer Interaction: How to design interfaces to help users interact with the visualizations effectively.
Visual Encoding: The mapping of data to visual elements, such as colors, shapes, and sizes, used in the development of graphic visualizations.
3D Data Visualization: The creation of 3D visualizations to represent data in a more immersive way.
Line Graph: A line graph displays data trends over time using a series of points connected by lines.
Scatter Plot: A scatter plot uses dots to represent data points and can show correlations between two or more variables.
Bar Graph: A bar graph displays frequency or percentage data by representing each category as a vertical or horizontal bar.
Pie Chart: A pie chart shows the relative size of categories in relation to the whole using a circle divided into slices.
Heat Map: A heat map displays data values as colors on a grid and is often used to show patterns in geographic or temporal data.
Network Diagram: A network diagram shows relationships between objects, people, or events using nodes and edges.
Tree Map: A tree map displays hierarchical data using nested rectangles, with larger rectangles representing higher levels of data.
Bubble Chart: A bubble chart displays 3D data points using circles of varying sizes and colors for each data point.
Choropleth Map: A choropleth map displays data values as colors on a geographic map, typically for a specific region or country.
Word Cloud: A word cloud displays the frequency of words in a text corpus as a visual arrangement of words, with size denoting frequency.
"Intended for a broader audience to help them visually explore and discover, quickly understand, interpret and gain important insights into otherwise difficult-to-identify structures, relationships, correlations, local and global patterns, trends, variations, constancy, clusters, outliers and unusual groupings within data."
"Information visualization deals with multiple, large-scale and complicated datasets which contain quantitative (numerical) data as well as qualitative (non-numerical, i.e. verbal or graphical) and primarily abstract information."
"Tables, charts and graphs (e.g., pie charts, bar charts, line charts, area charts, cone charts, pyramid charts, donut charts, histograms, spectrograms, cohort charts, waterfall charts, funnel charts, bullet graphs, etc.), diagrams, plots (e.g., scatter plots, distribution plots, box-and-whisker plots), geospatial maps."
"Maps (such as tree maps), animations, infographics, Sankey diagrams, flow charts, network diagrams, semantic networks, entity-relationship diagrams, Venn diagrams, timelines, mind maps, etc."
"Emerging technologies like virtual, augmented and mixed reality have the potential to make information visualization more immersive, intuitive, interactive and easily manipulable and thus enhance the user's visual perception and cognition."
"Properly sourced, contextualized, simple and uncluttered. The underlying data is accurate and up-to-date to make sure that insights are reliable."
"Graphical items are well-chosen for the given datasets and aesthetically appealing, with shapes, colors and other visual elements used deliberately in a meaningful and non-distracting manner."
"Effective information visualization is aware of the needs and concerns and the level of expertise of the target audience, deliberately guiding them to the intended conclusion."
"Used by domain experts and executives for making decisions, monitoring performance, generating new ideas and stimulating research."
"Check the quality of data, find errors, unusual gaps and missing values in data, clean data, explore the structures and features of data and assess outputs of data-driven models."
"Data and information visualization can constitute a part of data storytelling, where they are paired with a coherent narrative structure or storyline to contextualize the analyzed data and communicate the insights gained from analyzing the data clearly and memorably."
"The neighboring field of visual analytics marries statistical data analysis, data and information visualization and human analytical reasoning through interactive visual interfaces to help human users reach conclusions, gain actionable insights and make informed decisions."
"Descriptive statistics, visual communication, graphic design, cognitive science and, more recently, interactive computer graphics and human-computer interaction."
"It is argued by authors such as Gershon and Page that it is both an art and a science."
"Research into how people read and misread various types of visualizations is helping to determine what types and features of visualizations are most understandable and effective in conveying information."
"Unintentionally poor or intentionally misleading and deceptive visualizations (misinformative visualization) can function as powerful tools which disseminate misinformation, manipulate public perception and divert public opinion toward a certain agenda."
"Data visualization literacy has become an important component of data and information literacy in the information age akin to the roles played by textual, mathematical and visual literacy in the past." (Note: Not all questions could be answered directly from the provided paragraph.