Visualization
Visualization is the representation of data, information, or concepts in visual form to support inspection, interpretation, analysis, or communication.
D3.js in Action, Third Edition
Data visualization is the representation of data through graphical or visual forms to communicate patterns, relationships, or values.
- How it is used
- Builds web-based displays that communicate data stories through charts, dashboards, scrollytelling, and custom interactive forms.
- How deep it goes
- Develops an end-to-end project workflow from data preparation and encoding through interaction, accessibility, and custom layouts.
- Examples
- Uses customer dashboards, LGBTQ+ scrollytelling, network interactions, musical-score graphics, cetacean scatterplots, and a Van Gogh radial project.
- What you can do after
- Build it from scratch by preparing data, mapping it to visual attributes, rendering graphics, and planning meaningful interactions.
Read how D3.js in Action, Third Edition explains visualization →
Everyday Data Visualization
A graphical technique for representing data so that patterns, comparisons, trends, or other information can be perceived.
- How it is used
- Communicates quantitative information visually so audiences can understand a message, answer questions, and act on it.
- How deep it goes
- Explains design rationale and practical chart guidelines, while deliberately deferring tool implementation and coding tutorials.
- Examples
- Uses game interfaces, Florence Nightingale, maps, quipu knots, Minard’s campaign map, Excel bar charts, and rhino-poaching line charts.
- What you can do after
- Apply it by designing truthful, simple visualizations that hold attention and clearly communicate a point to a defined audience.
Read how Everyday Data Visualization explains visualization →
Starting Data Analytics with Generative AI and Python
Data visualization is the graphical representation of data to communicate patterns, relationships, or distributions.
- How it is used
- Uses charts to inspect time trends and compare business measures that are difficult to interpret in tables.
- How deep it goes
- Works through chart choices and Python plotting steps, then checks failures; does not develop visualization theory.
- Examples
- Shows monthly review counts, stacked review-score proportions, and regional product-sales bar plots and heatmaps.
- What you can do after
- Apply it via pandas, Matplotlib, and seaborn to compare business data, inspect trends, and test generated plotting code.
Read how Starting Data Analytics with Generative AI and Python explains visualization →
Writing for Developers: Blogs that get read
A graphical representation of information or processes intended to make structure, behavior, or relationships easier to understand.
- How it is used
- Uses graphics to clarify engineering systems, algorithms, data, debugging evidence, and performance results.
- How deep it goes
- Explains selection, framing, captions, readability, accessibility, data density, and scale accuracy, while deferring comprehensive data-visualization theory.
- Examples
- Uses architectural diagrams, optimization graphs, debugging screenshots, benchmark charts, flame graphs, dashboards, and maps.
- What you can do after
- Apply it via charts, diagrams, screenshots, and graphs that focus attention, communicate evidence, and remain readable and accessible.
Read how Writing for Developers: Blogs that get read explains visualization →
Statistics Slam Dunk
Data visualization is the graphical representation of data or analytical results to reveal patterns, comparisons, or relationships.
- How it is used
- Uses graphical summaries to explore data, reveal relationships, and communicate actionable findings.
- How deep it goes
- Demonstrates several plot types with mtcars and connects visualization to exploratory analysis, but does not detail a single plotting method exhaustively.
- Examples
- Uses mtcars, correlation plots, faceted boxplots, bar charts, histograms, and a combined ggplot2-patchwork figure.
- What you can do after
- Apply visualization with R, ggplot2, and patchwork to inspect relationships, grouped distributions, counts, and distributions.
Digital Twins in Action
Visualization is the representation of data or system information through graphical or visual forms.
- How it is used
- Makes a digital twin’s system state interpretable when people need to monitor, diagnose, intervene, or decide.
- How deep it goes
- Develops task-driven view selection, sensor aggregation, spatial placement, interaction design, and performance guidance, with a focused sparkline implementation.
- Examples
- Uses daily home-temperature summaries, per-sensor sparklines, floor-plan overlays, and role-specific dashboard questions.
- What you can do after
- Build it from scratch with sensor charts or apply it via Grafana, choosing views by user task and testing responsiveness.
Outlier Detection in Python
A visualization is a visual representation of data designed to reveal distributions, relationships, or unusual observations.
- How it is used
- Supports exploratory outlier detection, detector debugging, and interpretation of visibly unusual records or feature subspaces.
- How deep it goes
- Explains plot selection and interpretation through one- and two-dimensional examples, but stops short of reliably explaining higher-dimensional anomalies.
- Examples
- Uses OpenML distributions, eucalyptus feature pairs, detector-score histograms, baseball statistics, and abalone measurements.
- What you can do after
- Diagnose/debug with it by checking detector flags against one- and two-dimensional distributions and visible feature relationships.
Read how Outlier Detection in Python explains visualization →
WordPress Power Toolkit: Harness AI to build next-level websites
A visualization is a graphical representation of data designed to reveal patterns, comparisons, or other information.
- How it is used
- Positions data visualizations as clear numerical reasoning that supports AI critique and human scanability.
- How deep it goes
- Mentions visualization as a clarity practice without teaching chart selection, data preparation, or construction.
- Examples
- Uses a knowledge graph as a visual representation of relationships.
- What you can do after
- Apply it via data visualizations to make numerical reasoning easier for AI systems to critique.
Read how WordPress Power Toolkit: Harness AI to build next-level websites explains visualization →
Data-Driven Cybersecurity
A chart or graph is a graphical representation of data or relationships used to communicate patterns, trends, comparisons, or changes.
- How it is used
- Turns processed security metrics into displays that support investigation, reporting, and stakeholder decisions.
- How deep it goes
- Explains dashboard design principles and interprets example charts, while leaving a full visualization implementation unspecified.
- Examples
- Shows failed-login reasons and incident-type frequencies as charts, with dashboard screenshots suggested for security reports.
- What you can do after
- Apply it via dashboards and charts to select audience-relevant metrics, spot patterns, and guide follow-up investigations.
Delivering Machine Learning Projects: From design to deployment
A visualization is a graphical representation of data intended to reveal structure, patterns, relationships, or anomalies.
- How it is used
- Supports exploratory data analysis by revealing overall data characteristics and testing whether training data represents the target domain.
- How deep it goes
- Contrasts visual maps with summary statistics and model-based tools, then uses plots to expose distributions, dispersion, and deceptive relationships.
- Examples
- Uses Anscombe’s Quartet, tips-versus-bills data, and sensor-reading distributions to demonstrate hidden structure and uneven dispersion.
- What you can do after
- Evaluate/understand data properties by plotting distributions and relationships before designing datasets for modeling.
Read how Delivering Machine Learning Projects: From design to deployment explains visualization →
Effective Data Analysis
Data visualization is the graphical representation of data to communicate patterns, comparisons, or relationships.
- How it is used
- Supports stakeholder interpretation through static deliverables and reproducible interactive dashboards.
- How deep it goes
- Distinguishes static and dynamic forms, tool choices, workflows, and limitations without detailing visual design principles.
- Examples
- Uses charts, graphs, reports, presentations, dashboards, Tableau, Power BI, R, Python, and spreadsheets.
- What you can do after
- Apply via spreadsheets, R, Python, Tableau, or Power BI to create stakeholder-facing visualizations and dashboards.
Collaborative Software Design: How to facilitate domain modeling decisions
Visualization is the representation of concepts, relationships, or processes in a visual form to support reasoning and communication.
- How it is used
- Uses visual models to analyze complex, conflict-laden decisions and direct stakeholder conversations toward shared understanding.
- How deep it goes
- Explains cognitive and social benefits, gives facilitation heuristics, and stops short of prescribing detailed notation.
- Examples
- Uses EventStorming for a movie-streaming discussion, plus dependency graphs, state tables, examples, and deliberately imperfect models.
- What you can do after
- Evaluate/understand business problems, expose misunderstandings, compare alternatives, and support informed group design decisions.
The Well-Grounded Data Analyst
Data visualization is the graphical representation of data to reveal patterns, comparisons, or trends.
- How it is used
- Supports data exploration, finding patterns, comparing categories or periods, and communicating analytical findings.
- How deep it goes
- Covers chart selection and production within analysis workflows, but defers specialized statistical and forecasting methods to optional or separate treatment.
- Examples
- Uses bar charts, line charts, scatter plots, categorical-data charts, dashboards, and interactive applications as concrete outputs.
- What you can do after
- Apply it with Python, pandas, matplotlib, seaborn, Streamlit, R, Tableau, Power BI, or another suitable toolkit.
Read how The Well-Grounded Data Analyst explains visualization →
Statistics Every Programmer Needs
Data visualization is the graphical representation of data or information to communicate patterns, comparisons, trends, and other insights.
- How it is used
- Uses graphical displays to communicate analytical findings and monitor process quality.
- How deep it goes
- Defines graphical communication and explains control-chart interpretation, without supplying formulas or implementation details.
- Examples
- Uses control charts, p-charts, and np-charts to illustrate process monitoring and defect detection.
- What you can do after
- Evaluate process behavior and identify trends, outliers, and potential quality problems through visual displays.
Read how Statistics Every Programmer Needs explains visualization →
Build Python Web Apps with Streamlit
Data visualization is the graphical representation of data to support exploration, interpretation, and communication.
- How it is used
- Supports exploration, interpretation, and communication by turning business metrics into readable dashboard stories.
- How deep it goes
- Builds from chart selection and layout requirements through pandas preparation, Plotly construction, and Streamlit display.
- Examples
- Uses histograms, line charts, pie charts, KPI dashboards, editable dataframes, filters, and date-range selectors.
- What you can do after
- Build it from scratch by preparing data, selecting suitable charts, adding controls, and rendering visualizations in Streamlit.
Read how Build Python Web Apps with Streamlit explains visualization →
MongoDB 8.0 in Action, Third Edition
A data visualization is a graphical representation of data designed to reveal patterns, comparisons, or other analytical results.
- How it is used
- Supports analyzing MongoDB data, communicating findings, detecting anomalies, and tracking operational or business metrics.
- How deep it goes
- Explains data sources, chart types, aggregation, embedding, and generated pipelines, but stops short of detailed chart construction commands.
- Examples
- Uses movie-statistics dashboards and borough-level restaurant cuisine charts as concrete visualization examples.
- What you can do after
- Apply it via Atlas Charts to build, share, embed, and inspect MongoDB-backed charts and dashboards.
Read how MongoDB 8.0 in Action, Third Edition explains visualization →
Related concepts
- bar chartincludes · A bar chart is a visualization that compares quantitative values for discrete categories using rectangular bars whose lengths or heights encode magnitude.
- collaborative discovery and modelingincludes · Collaborative discovery and modeling is a group technique in which stakeholders jointly create and refine representations to establish shared understanding of a domain or system.
- line plotincludes · A line plot is a chart that connects ordered data values with line segments to show changes, relationships, or trends.
- Matplotlibused by · Matplotlib is an open-source Python library for creating static, animated, and interactive data visualizations.
- data explorationused by · Data exploration is the systematic examination and summarization of data to understand its structure, quality, distributions, relationships, patterns, and anomalies.
- Plotlyproduced by · Plotly is a software library and visualization platform for creating interactive, customizable charts and data visualizations.
These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.