"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."
Representing complex data in a visual manner through charts, graphs, maps, and diagrams.
Data types and formats: Understanding the different types of data and their formats is essential for effective data visualization.
Data preparation and cleaning: Before visualizing data, it is critical to gather, clean, and prepare the data for proper presentation.
Data visualization tools: Knowledge of various data visualization tools and software, such as Tableau, Excel, and Python, can help create effective data visualizations.
Principles of design: Knowledge of design principles such as color theory, typography, and layout is essential to create visually appealing and effective data visualizations.
Types of charts and graphs: Understanding the different types of charts and graphs, such as bar charts, line charts, scatter plots, and pie charts, will help you choose the best format for data presentation.
Visualization techniques: Knowledge of various visualization techniques like heat maps, treemaps and network diagrams can help to better understand data patterns and explore them.
Data storytelling: Understanding how to use visualizations to tell a story with data can help you communicate your findings effectively.
Interactivity: Knowledge of how to create interactive visualizations that allow users to explore data sets further can provide more insight into the data.
Data coding and programming: Understanding and using coding and programming languages like R, Python, Java etc. can help you create customized and more dynamic visualizations.
Communication and collaboration: Collaboration and effective communication amongst team members involved in the data visualization initiative is essential to success.
Line charts: A type of chart that displays information as a series of data points connected by straight lines.
Bar charts: A type of chart that represents data with rectangular bars with heights proportional to the values that they represent.
Pie charts: A circular chart used to display percentages that add up to 100.
Scatterplots: A graph of plotted points that show the relationship between two variables.
Heatmaps: A graphical representation of data where values are represented by colors, usually used to show the distribution of data in a two-dimensional space.
Tree maps: A way of representing hierarchical data visually by using nested rectangles, with each rectangle representing a node and its size proportional to its value.
Network diagrams: A graphical representation of the connections between nodes, commonly used to represent complex information.
Sankey diagrams: A type of diagram that displays flows of information or energy, with lines of varying thickness representing the quantity of flow.
Chord diagrams: A type of graph used to show the relationships between different entities, with arcs connecting nodes and the width of each arc representing the value of the relationship.
Bubble charts: A type of chart that displays data points as bubbles, where the size of the bubble represents a third variable.
Word clouds: A type of visual representation of text data, where the most frequent words are displayed larger and central compared to others.
Geographic maps: Used to show spatial patterns of data and often includes interactive features.
Gantt charts: A type of bar chart used for project management, showing the duration of tasks against the progress of the project.
Radar charts: A type of chart that displays data in a circular pattern, useful for comparing multiple data points.
Box plots: A type of chart that shows the distribution of a dataset by displaying the median, quartiles, and outliers.
"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.