"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."
Data visualization involves using graphical representations of data to help communicate information and insights.
Types of data visualization: Understanding the different types of data visualization such as charts, graphs, diagrams, and maps, can help you identify the best way to represent your data.
Data sources and collection: Knowing where the data comes from, how it's collected, and its reliability can help you create accurate visualizations.
Data preparation: Getting data into a format that's easy to analyze and visualize requires data preparation. This can include cleaning, transforming, and structuring the data.
Statistical analysis: Understanding statistical concepts is crucial when analyzing data and creating visualizations. Topics such as correlation, standard deviation, and regression can help you identify patterns and trends in your data.
Visual perception: The way humans perceive and interpret visual information can impact how you design your visualizations. Topics such as color theory, layout, and typography can help you create effective and engaging visualizations.
Data storytelling: Creating a narrative around your data visualization can help you communicate your findings and insights effectively. Topics such as data interpretation, critical thinking, and narrative structure can help you create compelling data stories.
Tools and technologies: Knowing how to use data visualization tools and technologies such as Tableau, Excel, Power BI, and Python can help you create more complex and interactive data visualizations.
Data ethics: As data visualization becomes more prevalent, it's important to consider the ethical implications of using data. Topics such as data privacy, bias, and transparency can help you create responsible and ethical visualizations.
Data-driven decision-making: Understanding how data visualization can inform decision-making processes can help you create effective and actionable visualizations. Topics such as decision-making frameworks, risk management, and problem-solving can help you use data to make better decisions.
Bar chart: A graphic representation of data that uses bars to compare values.
Time Series: Shows data changes over time in a line graph.
Area chart: A graph that uses the area below the line to represent the data.
Scatterplot: A graphic representation of data that shows the relationship between two variables.
Bubble chart: A type of scatterplot that displays circles of different sizes to represent the data.
Heatmap: A visual representation of data where colors are used to represent numerical values.
Box plot: A graph that displays the distribution of data, showing the median, interquartile range, and outliers.
Graph Matrix: A matrix of smaller graphs that display the relationships between different variables.
Network visualization: A graph that displays connections between nodes, often used to represent social networks.
Cartogram: A map that uses areas to represent data, rather than geographic space.
Tree Map: A graphic representation of hierarchical data, often used to display nested categories.
Violin plot: A graph that shows the distribution of data as a smoothed density plot.
Choropleth Map: A map that uses colors to show variations in data across geographic regions.
Sankey Diagram: A graphic representation of data flow, often used to show transition from one state to another.
Word cloud: A data visualization method that displays words in different sizes to indicate frequency or importance.
Spider chart: A chart used to compare data using multiple variables.
Parallel Coordinates Plot: A plot that is used to compare several variables at the same time.
Flowchart: A diagram that shows the flow of data, often used in decision-making processes.
Sunburst Chart: A chart that is used to show hierarchical data, often used in data mining.
Funnel Chart: A chart that is used to show the progressive stages of a process, often used in marketing.
"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.