"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."
Refers to the creation of graphical representations of data to facilitate understanding and identify trends and patterns.
Data types and sources: Learning about the different types of data (qualitative, quantitative, continuous, categorical) and where to find them (survey data, electronic medical records, administrative databases).
Principles of data visualization: Understanding the key principles of data visualization such as selecting the right chart type, presenting data accurately, avoiding chartjunk, and using color appropriately.
Chart types: Understanding the different types of charts such as bar charts, line charts, scatter plots, histograms, and box plots, and when to use them.
Visualization tools: Learning about the different tools for creating data visualizations, ranging from simple Excel charts to more sophisticated tools like Tableau, Power BI, and R.
Pre-processing data: Understanding the importance of pre-processing data, including data cleaning, formatting, and aggregation, before creating visualizations.
Design considerations: Identifying design considerations such as the target audience, purpose of the visualization, data complexity, and data patterns.
Visual storytelling: Learning how to create data visualizations that tell a story and communicate insights effectively.
Ethics and privacy: Understanding ethical and privacy considerations related to medical research data when creating visualizations, including the importance of de-identification and informed consent.
Interactive visualizations: Exploring interactive visualization techniques, such as user-controlled filters, zooming, clicking, and hovering, that enable users to explore data and reveal insights.
Data interpretation: Learning to interpret data visualizations and identify patterns, trends, and unusual observations that might be indicative of potential research findings.
Box plot: A box plot is a graph that displays the five-number summary of a dataset using a box and whisker diagram.
Scatter plot: A scatter plot is a two-dimensional graph that displays the relationship between two variables.
Line chart: A line chart depicts changes in data over time, with data points connected by a line.
Bar graph: A bar graph is a chart that displays categorical data using rectangular bars.
Histogram: A histogram is a collection of rectangles that display the distribution of data.
Heat map: A heat map is a color-coordinated grid that represents a matrix of values.
Bubble chart: A bubble chart is a variation of a scatter plot in which the data points are represented by bubbles.
Stacked bar chart: A stacked bar chart is a type of bar graph that displays proportions of subgroups within a category.
Choropleth map: A choropleth map is a color-coded map that represents data using geographic regions or shapes.
Spider chart: A spider chart is a radial chart that displays multiple variables plotted on a common axis.
Tree map: A tree map is a nested visualization that displays hierarchical data using rectangular boxes.
Sankey diagram: A Sankey diagram is a flow chart that displays the movement or transfer of energy, material, or data.
Anatomical diagram: An anatomical diagram represents various structures of organs using different colors, shapes, or lines.
Network diagram: A network diagram displays the relational data between different entities.
Pie chart: A pie chart is a circular chart that displays the contribution of each component in a dataset.
Word cloud: A word cloud is a graphical representation of text data using words arranged in random order and size.
"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.