"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."
The process of presenting data in a visual format, such as tables, charts, or graphs, to help researchers and decision-makers understand complex data sets.
Basic graphics principles: This includes understanding the different types of visualizations, such as bar charts, line charts, scatterplots, and heat maps. It also involves understanding the principles of color, typography, and readability.
Data manipulation: Before creating visualizations, you need to be able to clean and manipulate your data using tools like Excel or Python. This includes techniques like filtering, sorting, and aggregating data.
Data visualization tools: There are many different tools you can use to create visualizations, such as Excel, Tableau, R, and Python. Each has its own strengths and weaknesses that you need to be aware of.
Choosing the right visualization: Depending on your data and your research questions, you may need to choose different types of visualizations. You need to understand the advantages and disadvantages of each type and know which works best for your specific purpose.
Designing effective visualizations: Good visualizations are more than just pretty pictures. They should communicate complex information clearly and effectively. This includes understanding how to use labels, titles, and annotations to convey meaning.
Interactive visualizations: Interactive visualizations allow users to explore data in more depth by interacting with the visualizations. This includes knowing how to use tools like filters, sliders, and tooltips.
Data storytelling: Data storytelling involves using data visualizations to tell a compelling story about your data. This includes understanding how to structure your narrative and use visualizations to support your argument.
Ethical considerations: Visualizations can be powerful tools, but they can also be misleading if not used appropriately. You need to be aware of the ethical considerations involved in data visualization, such as data privacy and data bias.
Testing and evaluation: Once you have created visualizations, you need to test them to make sure they are effective. This includes getting feedback from others and conducting testing to ensure that the visualizations meet your research objectives.
Advanced techniques: Once you have mastered the basics of data visualization, you can explore more advanced techniques like network analysis, GIS, and using machine learning algorithms to create visualizations.
Line Charts: Line charts are graphs that display information as a series of data points connected by straight lines. They are useful for visualizing trends over time.
Bar Charts: Bar charts are graphs that display information using rectangular bars. They are useful for comparing different sets of data.
Pie Charts: Pie charts are circular graphs that represent data as slices of a pie. They are useful for showing parts of a whole.
Scatter Plots: Scatter plots display the relationship between two variables in a dataset, plotting each data point on a graph.
Heatmaps: Heatmaps use color to represent data values, displaying the concentration of data in specific areas of a graph.
Treemaps: Treemaps are graphs that use hierarchies to represent data values, displaying variables as nested rectangles.
Choropleth Maps: Choropleth maps use color and shading to represent data on a geographic map, displaying statistics based on specific regions.
Bubble Charts: Bubble charts represent data values as circles, using the size of each circle to represent its value.
Sankey Diagrams: Sankey diagrams use arrows to represent the flow of data between different categories.
Word Clouds: Word clouds use a cluster of words to visually represent data, with the more frequently used words displayed more prominently.
"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.