"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."
This is the presentation of data in the form of charts, graphs, and other visual aids to help users interpret complex data.
Data Types: Data visualization is the representation of data in a visualized form. Before you can begin visualizing data, you need to understand the types of data you have.
Data Analysis: Data analysis is a critical step in the data visualization process, where you examine your data to gain insights from patterns, trends, and relationships.
Business Analytics: Business analytics involves the use of data and statistical methods to make better business decisions. It includes techniques such as descriptive and predictive analytics, optimization, and simulation.
Types of Data Visualization: This topic covers the various types of visualizations available for representing data, such as bar charts, pie charts, scatterplots, heat maps, tree diagrams, and more.
Data Visualization Tools: There are many data visualization tools available, and each has its strengths and weaknesses. Understanding the features and capabilities of different tools can help you to choose the right one for your project.
Data Visualization Best Practices: There are some general best practices for creating effective data visualizations, such as keeping the design simple, using appropriate colors, avoiding clutter, and using clear labels and annotations.
Storytelling with Data: Effective data visualization should tell a story that conveys insights and meaningful insights. This topic explores how to create compelling data stories that engage your audience.
Data Visualization in Business: Business analysts use data visualization to effectively communicate insights from data to stakeholders such as managers, executives, and shareholders. This topic covers some of the ways data visualization can be used in business settings.
Data Visualization Case Studies: Examining data visualization case studies can provide insight into how others have successfully used visuals to represent data, identify insights, and communicate findings.
Data Visualization Ethics: Ethical considerations are important when working with data, especially when visualization is involved. This topic explores some of the ethical challenges and considerations associated with data visualization.
Bar Graphs: A graph that represents data as bars, where the length of each bar is proportional to the value of the data it represents.
Line Graphs: A graph that displays data as a series of points connected by lines, showing trends over time.
Scatterplots: A graph that displays the relationship between two variables, with each data point represented as a dot.
Pie Charts: A chart that displays data as slices of a pie, where each slice represents a percentage of the whole.
Histograms: A chart that displays the distribution of a variable's values, where the x-axis represents the range of values and the y-axis represents the frequency of values in that range.
Heat Maps: A chart that displays data as colors on a grid, with each cell representing a value within a range.
Geographic Maps: A map that displays data based on geographical regions, with different regions represented by different colors or shades.
Sankey Diagrams: A diagram that shows the flow of data between different stages, with the thickness of each branch representing the amount of data flowing through that stage.
Network Diagrams: A diagram that shows the relationships between different entities, with nodes representing entities and edges representing connections between them.
Bubble Charts: A chart that displays data as bubbles, where the size of each bubble represents the value of the data it represents.
"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.