"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."
Methods used to represent data graphically, such as histograms, scatterplots, and boxplots.
Data types and measurement scales: Understanding the different types of data that can be collected and analyzed, such as categorical, numeric, ordinal and interval data.
Descriptive statistics: Methods to summarize and present data, including measures of central tendency, variability, and distribution.
Inferential statistics: Techniques used to make inferences or predictions based on data samples, including hypothesis testing, confidence intervals, and regression analysis.
Graphical representation techniques: Concepts and principles of graphical design and visualization, including chart types, color visualization, and data mapping.
Data visualization tools and techniques: An overview of tools and technologies used in the industry, such as R, Python, Tableau, D3.js, and Excel.
Data cleaning and pre-processing: Techniques and best practices to prepare data for visualization by cleaning, filtering, and transforming the data.
Correlation and causation: Understanding the relationship between variables in data and the concepts used in determining causality.
Spatial data visualization: Techniques used to visualize spatial data, including maps, geospatial data analysis, and GIS.
Time series data visualization: Techniques used to visualize time series data, including line and bar charts, sparklines, and heat maps.
Interactive data visualization: Techniques for creating interactive visualizations, including interactivity design, user-centered design, and animation.
Big data visualization: Techniques and tools used to visualize and analyze large-scale and complex data sets such as those generated by social media, sensors or Internet retail.
Ethics and privacy in data visualization: Issues of ethics and privacy in the design and use of data visualization.
Bar Chart: A chart consisting of vertical or horizontal bars that represents data values.
Line Chart: A chart that displays data values as a series of data points connected by straight lines.
Pie Chart: A chart that displays data values as slices of a circle, with each slice representing a proportion of the total.
Scatter Plot: A chart that displays the relationship between two variables through the use of dots on a graph.
Heat Map: A chart that uses colors to indicate the frequency or intensity of a particular data point or value.
Box Plot: A chart that displays the distribution of data through the use of quartiles, medians, and outliers.
Histogram: A chart that displays the frequency distribution of a set of continuous data values as bars.
Tree Map: A chart that displays data values as nested rectangles or squares, with each rectangle representing a proportion of the total.
Network Visualization: A chart that depicts the relationships between different entities through nodes and edges.
Word Cloud: A chart that displays text data values as a collection of words, with more frequently used words appearing larger.
"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.