Data Visualization Concepts Quiz

Basic elements of data that represent real-world entities. They can be things like products, customers, or locations.

Characteristics of a data item that describe its properties. Examples include name, age, price, or color.

A collection of data items organized in a way that allows for analysis and understanding.

Attribute values that don't have a natural order, like colors, names, or categories.

Attributes that have a natural order or ranking. Examples include age, temperature, or size.

Visualization is the process of using visual representations to explore, analyze, and present data. It leverages our visual perception system, particularly the high-bandwidth processing of visual information by our brain.

Visualizations are not just pretty pictures; they should be designed to reveal insights, patterns, and trends in data, providing actionable knowledge.

The visualization pipeline represents a structured approach to building effective visualizations. It involves understanding the user's needs, abstracting the data and tasks, choosing appropriate visual encodings, and implementing the algorithm.

The nested model of visualization highlights the importance of iteratively refining the process. It emphasizes understanding the user's domain, abstracting the data and tasks, selecting visual encodings, and ensuring efficient algorithms.

Data visualization aims to explore data when there are no prior assumptions, analyze data to verify or refute hypotheses, and present findings to communicate results effectively.

Visual encoding design refers to the process of choosing visual elements like size, colour, shape, and position to represent data attributes effectively.

Abstract data is data that lacks a physical location in the world and exists conceptually. Examples include financial data or social media interactions.

Information visualization utilizes interactive, computer-supported visual representations of abstract data to enhance cognitive understanding.

A type of chart that is not suitable for comparing precise values or observing trends, as it relies heavily on color, which is a low-ranking visual channel for such comparisons. Comparing angles is difficult, while using length or position (as in bar charts) allows for better comparisons.

A visual representation that is particularly useful for showcasing changes in the parts of a whole over time. It utilizes layers or blocks to represent different categories (keys), with height encoding the quantitative value (e.g., counts). Color is used to distinguish between categories and highlight how they evolve over time.

A visual representation that employs a 2D matrix to display data. The data is arranged in rows and columns, where each cell represents a unique combination of two key attributes. The quantitative attribute of the data is encoded using color.

The ability of a chart to effectively represent large amounts of data without sacrificing clarity or readability. It refers to the capacity to handle a large number of keys, data points, or categories without becoming cluttered or confusing.

The ability to make the chart bigger for a better visual experience.

A visual representation of the distribution of a single quantitative attribute using bars to represent the frequency of data points within specific ranges (bins).

The size of the intervals used to group data points in a histogram, determining the level of detail in the visualization.

A visual representation of the distribution of a single quantitative attribute, summarizing it using five key statistics: Minimum, Maximum, Median, Lower Quartile, and Upper Quartile. It also shows outliers.

Data points that fall outside a specific range, typically defined as 1.5 times the Interquartile Range (IQR) above the upper quartile or below the lower quartile.

A visual representation of the distribution of a single quantitative attribute, combining the features of a boxplot with a density curve, providing a richer understanding of the data distribution.

The ability to explore and interact with data visualizations, allowing for dynamic manipulation and analysis of data.

Visual elements used to represent data in a visualization, such as points, lines, bars, or shapes.

The way data values are mapped to visual attributes of marks, such as size, color, or shape.

Visualizations designed to show changes over time in a network by representing multiple time snapshots in a grid or filmstrip layout.

Visualizations for networks that evolve over time, combining different time points into a single view to reveal global patterns and trends.

A visualization method where time is mapped to time, animating the network's evolution with individual snapshots at each time step.

A type of network that changes over time, also known as a time-stamped network, longitudinal network, evolving network, or temporal network.

A way to visualize time-varying networks by creating a single network that aggregates all time steps, allowing for the identification of overall trends and patterns.

A visualization method for time-varying networks where the network's structure is displayed for each time step, allowing for the identification of specific changes.

A method for visualizing dynamic networks that maps time to space, allowing for the identification of global and local patterns in the network's evolution.

A comprehensive visualization that illustrates the network's entire time span, revealing global patterns and allowing for identifying changes over time.

A type of data visualization that uses points on a grid to represent data with two quantitative attributes, showing trends, outliers and correlations.

A visualization for displaying multiple quantitative variables by representing each data point with a line that intersects a set of parallel axes.

A specialized tool for visualizing data that includes multiple scatterplots, showing relationships between different pairs of variables.

A multi-dimensional visualization where different variables are represented as spokes radiating from a central point, forming a polygon.

A technique for representing multidimensional data using graphical elements like size, shape, color, and orientation.

A data reduction technique that involves removing data elements based on their values with respect to specific attributes, without changing the number of attributes.

A data reduction technique that involves removing attributes based on their importance or relevance, without changing the number of data items.

A measure used to determine the relative importance or similarity between attributes, often based on the distribution of values across data items.

The process of arranging attributes in a dataset based on their similarity or importance, often using a similarity measure.

Exploring data in a way that helps to understand and discover relationships and patterns between multiple variables.