Learn to design effective charts and graphs.
Your data is only as good as your ability to understand and communicate it. The right visualization is essential to incite a desired action, whether from customers or colleagues. But most marketers aren’t mathematicians or adept at data visualization. Fortunately, you don’t need a PhD in statistics to crack the data visualization code.
This document discusses creating data visualizations with low-cost tools. It begins by outlining the objectives of understanding the purpose of a visualization, principles of communicating through data, choosing the right visualization, and determining if Excel is suitable. It then covers the eight principles of communicating through data, such as defining the question, using accurate data, and tailoring the visualization to the audience. Next, it discusses choosing the right visualization type based on the purpose, such as line charts, bar charts or tables. The document considers when Excel may not be suitable and introduces specialist tools like Tableau, Microsoft Power BI, and coding options. It concludes with additional resources for data visualization.
North Raleigh Rotarian Katie Turnbull gave a great presentation at our Friday morning extension meeting about data visualization. Katie is a consultant at research and advisory firm, Gartner, Inc.
The document discusses data visualization techniques for visual data mining. It defines key terms like visual, visualization, and visual data mining. Visual data mining uses visualization techniques to discover useful knowledge from large datasets. Benefits include faster understanding of problems, insights, and trends in data. Different graph types like bar charts, histograms, pie charts and scatter plots are suitable for different purposes like comparing values or showing relationships. Effective visualization requires arranging data clearly, identifying important variables, choosing the right graph, keeping it simple, and understanding the audience.
Data Visualization Design Best Practices WorkshopJSI
This document provides guidance on effective data visualization. It emphasizes starting with the audience and their needs, identifying the key story or message in the data, and using simple, clear design principles. Charts should be designed in 5-8 seconds to engage the audience. The document recommends several resources for choosing effective chart types and improving visualization skills. Overall, it stresses the importance of visualization in empowering stakeholders to make informed decisions.
This presentation is an Introduction to the importance of Data Analytics in Product Management. During this talk Etugo Nwokah, former Chief Product Officer for WellMatch, covered how to define Data Analytics why it should be a first class citizen in any software organization
The document discusses principles of data visualization. It provides an overview of Tamara Munzner's framework for visualization design, which involves four levels of analysis: the domain situation, data/task abstraction, visual encoding and interaction idioms, and algorithms. The framework aims to translate real-world problems into visual representations that help users accomplish tasks. The document also outlines different types of data visualization like scientific and information visualization. Finally, it notes discoverability as a key purpose of visualization, to gain new insights from data in an interactive manner.
Data analytics refers to the broad field of using data and tools to make business decisions, while data analysis is a subset that refers to specific actions within the analytics process. Data analysis involves collecting, manipulating, and examining past data to gain insights, while data analytics takes the analyzed data and works with it in a meaningful way to inform business decisions and identify new opportunities. Both are important, with data analysis providing understanding of what happened in the past and data analytics enabling predictions about what will happen in the future.
This document discusses creating data visualizations with low-cost tools. It begins by outlining the objectives of understanding the purpose of a visualization, principles of communicating through data, choosing the right visualization, and determining if Excel is suitable. It then covers the eight principles of communicating through data, such as defining the question, using accurate data, and tailoring the visualization to the audience. Next, it discusses choosing the right visualization type based on the purpose, such as line charts, bar charts or tables. The document considers when Excel may not be suitable and introduces specialist tools like Tableau, Microsoft Power BI, and coding options. It concludes with additional resources for data visualization.
North Raleigh Rotarian Katie Turnbull gave a great presentation at our Friday morning extension meeting about data visualization. Katie is a consultant at research and advisory firm, Gartner, Inc.
The document discusses data visualization techniques for visual data mining. It defines key terms like visual, visualization, and visual data mining. Visual data mining uses visualization techniques to discover useful knowledge from large datasets. Benefits include faster understanding of problems, insights, and trends in data. Different graph types like bar charts, histograms, pie charts and scatter plots are suitable for different purposes like comparing values or showing relationships. Effective visualization requires arranging data clearly, identifying important variables, choosing the right graph, keeping it simple, and understanding the audience.
Data Visualization Design Best Practices WorkshopJSI
This document provides guidance on effective data visualization. It emphasizes starting with the audience and their needs, identifying the key story or message in the data, and using simple, clear design principles. Charts should be designed in 5-8 seconds to engage the audience. The document recommends several resources for choosing effective chart types and improving visualization skills. Overall, it stresses the importance of visualization in empowering stakeholders to make informed decisions.
This presentation is an Introduction to the importance of Data Analytics in Product Management. During this talk Etugo Nwokah, former Chief Product Officer for WellMatch, covered how to define Data Analytics why it should be a first class citizen in any software organization
The document discusses principles of data visualization. It provides an overview of Tamara Munzner's framework for visualization design, which involves four levels of analysis: the domain situation, data/task abstraction, visual encoding and interaction idioms, and algorithms. The framework aims to translate real-world problems into visual representations that help users accomplish tasks. The document also outlines different types of data visualization like scientific and information visualization. Finally, it notes discoverability as a key purpose of visualization, to gain new insights from data in an interactive manner.
Data analytics refers to the broad field of using data and tools to make business decisions, while data analysis is a subset that refers to specific actions within the analytics process. Data analysis involves collecting, manipulating, and examining past data to gain insights, while data analytics takes the analyzed data and works with it in a meaningful way to inform business decisions and identify new opportunities. Both are important, with data analysis providing understanding of what happened in the past and data analytics enabling predictions about what will happen in the future.
Data Visualization in Exploratory Data AnalysisEva Durall
This document outlines activities for exploring equity in science education outside the classroom using data visualization. It introduces exploratory data analysis and how data visualization can help generate hypotheses from data. The activities include analyzing an interactive map of science education organizations, and creating visualizations to explore equity indicators like access, diversity, and inclusion. Effective visualization requires defining goals, finding relevant data, and experimenting with different chart types to answer questions arising from the data.
OLTP systems emphasize short, frequent transactions with a focus on data integrity and query speed. OLAP systems handle fewer but more complex queries involving data aggregation. OLTP uses a normalized schema for transactional data while OLAP uses a multidimensional schema for aggregated historical data. A data warehouse stores a copy of transaction data from operational systems structured for querying and reporting, and is used for knowledge discovery, consolidated reporting, and data mining. It differs from operational systems in being subject-oriented, larger in size, containing historical rather than current data, and optimized for complex queries rather than transactions.
Exploratory data analysis data visualization:
Exploratory Data Analysis (EDA) is an approach/philosophy for data analysis that employs a variety of techniques (mostly graphical) to
Maximize insight into a data set.
Uncover underlying structure.
Extract important variables.
Detect outliers and anomalies.
Test underlying assumptions.
Develop parsimonious models.
Determine optimal factor settings
This document provides an overview of Tableau, a data visualization software. It outlines the agenda for the presentation, which will cover connecting to data, visual analytics with Tableau, dashboards and stories, calculations, and mapping capabilities. Tableau allows users to connect to various data sources, transform raw data into interactive visualizations, and share dashboards or publish them online. It is a leading tool for data analysis and visualization.
Slides for the 2016/2017 edition of the Data Mining and Text Mining Course at the Politecnico di Milano. The course is also part of the joint program with the University of Illinois at Chicago.
Data visualization in data science: exploratory EDA, explanatory. Anscobe's quartet, design principles, visual encoding, design engineering and journalism, choosing the right graph, narrative structures, technology and tools.
This is a presentation I gave on Data Visualization at a General Assembly event in Singapore, on January 22, 2016. The presso provides a brief history of dataviz as well as examples of common chart and visualization formatting mistakes that you should never make.
The document provides an introduction to data analytics, including defining key terms like data, information, and analytics. It outlines the learning outcomes which are the basic definition of data analytics concepts, different variable types, types of analytics, and the analytics life cycle. The analytics life cycle is described in detail and involves problem identification, hypothesis formulation, data collection, data exploration, model building, and model validation/evaluation. Different variable types like numerical, categorical, and ordinal variables are also defined.
This document discusses various approaches for finding, loading, and cleaning data. It provides examples of public data sources like government websites and catalogs. It also discusses different file formats for storing data and databases for processing it. The document outlines common data issues like missing values, invalid data types and incorrect structure that require cleaning. It provides examples of how to fix such issues through techniques like standardizing values, filtering rows and columns, and validating data.
The document discusses data visualization tools. It begins with an overview of data visualization, describing how visualizing data can help identify patterns and trends. It then discusses advantages like aiding quick understanding. Five types of data visualization are mentioned but not described. The document primarily focuses on reviewing popular data visualization tools like Tableau, FusionCharts, Datawrapper, Highcharts, Excel, Sisense, Plotly, and others. It provides brief descriptions of each tool's features and capabilities. In closing, it references additional resources on the topic.
The document discusses data visualization and analytics. It defines data visualization as the graphical representation of information and data using visual elements like charts and graphs. This provides an accessible way to see trends, outliers, and patterns in data. Data visualization sits at the intersection of analysis and visual storytelling, helping make data understandable and informing decisions. The document also covers types of visualizations, examples, tools for data visualization like Tableau and Excel, and factors to consider when choosing analytics tools.
The document discusses various techniques for visualizing data, from basic charts to approaches for big data. It covers common basic chart types like line graphs, bar charts, scatter plots, and pie charts. For big data, it addresses challenges like large data volumes, different data varieties, visualization velocity, and filtering. The document recommends understanding your data and goals to select the best visualizations, and introduces SAS Visual Analytics as a tool that performs automatic charting to help users visualize big data.
This document provides an introduction to data visualization. It discusses the importance of data visualization for clearly communicating complex ideas in reports and statements. The document outlines the data visualization process and different types of data and relationships that can be visualized, including quantitative and qualitative data. It also discusses various formats for visualizing data, with the goal of helping readers understand data visualization and how to create interactive visuals and analyze data.
This document provides information about Tableau, a data visualization software. It discusses Tableau's prerequisites, products, and architecture. Tableau allows users to easily connect to various data sources and transform data into interactive visualizations and dashboards. Key Tableau concepts covered include data sources, worksheets, dashboards, stories, filters, marks, color and size properties. The document also explains Tableau's desktop and server products, and the stages of importing data, analyzing it, and sharing results.
The document discusses exploratory data analysis and provides examples of how it can be used. It summarizes two case studies: one where an energy utility detected billing fraud by analyzing meter reading patterns, and another where month of birth was found to correlate with exam scores for students in Tamil Nadu. The document then outlines the exploratory data analysis process and provides a high-level overview of U.S. and Indian birth date patterns identified through analysis of large datasets.
Just finished a basic course on data science (highly recommend it if you wish to explore what data science is all about). Here are my takeaways from the course.
This presentation introduces big data and explains how to generate actionable insights using analytics techniques. The deck explains general steps involved in a typical analytics project and provides a brief overview of the most commonly used predictive analytics methods and their business applications.
Vijay Adamapure is a Data Science Enthusiast with extensive experience in the field of data mining, predictive modeling and machine learning. He has worked on numerous analytics projects ranging from healthcare, business analytics, renewable energy to IoT.
Vijay presented these slides during the Internet of Everything Meetup event 'Predictive Analytics - An Overview' that took place on Jan. 9, 2015 in Mumbai. To join the Meetup group, register here: http://bit.ly/1A7T0A1
Data visualization is the graphical representation of information and data using visual elements like charts, graphs, and maps to provide an accessible way to see and understand trends and patterns in data. It allows massive amounts of information to be analyzed and data-driven decisions to be made. Data visualization tells a story by removing noise from data and highlighting useful information. Common types include charts, graphs, maps, and infographics, with tools ranging from simple online options to more complex offline programs. The key is to focus on best practices and developing a personal style when creating visualizations.
Guía sobre como diseñar graficos para tus informesGroupM Spain
This document provides guidance on designing effective data visualizations. It discusses the main types of charts and graphs including bar charts, pie charts, line charts, area charts, scatter plots, bubble charts and heat maps. For each type, it describes best practices for design, such as starting the y-axis at 0, using consistent colors, and limiting the number of data series shown. The goal is to help readers understand their data and tell their intended story through clear, impactful visual representations.
Data Visualization in Exploratory Data AnalysisEva Durall
This document outlines activities for exploring equity in science education outside the classroom using data visualization. It introduces exploratory data analysis and how data visualization can help generate hypotheses from data. The activities include analyzing an interactive map of science education organizations, and creating visualizations to explore equity indicators like access, diversity, and inclusion. Effective visualization requires defining goals, finding relevant data, and experimenting with different chart types to answer questions arising from the data.
OLTP systems emphasize short, frequent transactions with a focus on data integrity and query speed. OLAP systems handle fewer but more complex queries involving data aggregation. OLTP uses a normalized schema for transactional data while OLAP uses a multidimensional schema for aggregated historical data. A data warehouse stores a copy of transaction data from operational systems structured for querying and reporting, and is used for knowledge discovery, consolidated reporting, and data mining. It differs from operational systems in being subject-oriented, larger in size, containing historical rather than current data, and optimized for complex queries rather than transactions.
Exploratory data analysis data visualization:
Exploratory Data Analysis (EDA) is an approach/philosophy for data analysis that employs a variety of techniques (mostly graphical) to
Maximize insight into a data set.
Uncover underlying structure.
Extract important variables.
Detect outliers and anomalies.
Test underlying assumptions.
Develop parsimonious models.
Determine optimal factor settings
This document provides an overview of Tableau, a data visualization software. It outlines the agenda for the presentation, which will cover connecting to data, visual analytics with Tableau, dashboards and stories, calculations, and mapping capabilities. Tableau allows users to connect to various data sources, transform raw data into interactive visualizations, and share dashboards or publish them online. It is a leading tool for data analysis and visualization.
Slides for the 2016/2017 edition of the Data Mining and Text Mining Course at the Politecnico di Milano. The course is also part of the joint program with the University of Illinois at Chicago.
Data visualization in data science: exploratory EDA, explanatory. Anscobe's quartet, design principles, visual encoding, design engineering and journalism, choosing the right graph, narrative structures, technology and tools.
This is a presentation I gave on Data Visualization at a General Assembly event in Singapore, on January 22, 2016. The presso provides a brief history of dataviz as well as examples of common chart and visualization formatting mistakes that you should never make.
The document provides an introduction to data analytics, including defining key terms like data, information, and analytics. It outlines the learning outcomes which are the basic definition of data analytics concepts, different variable types, types of analytics, and the analytics life cycle. The analytics life cycle is described in detail and involves problem identification, hypothesis formulation, data collection, data exploration, model building, and model validation/evaluation. Different variable types like numerical, categorical, and ordinal variables are also defined.
This document discusses various approaches for finding, loading, and cleaning data. It provides examples of public data sources like government websites and catalogs. It also discusses different file formats for storing data and databases for processing it. The document outlines common data issues like missing values, invalid data types and incorrect structure that require cleaning. It provides examples of how to fix such issues through techniques like standardizing values, filtering rows and columns, and validating data.
The document discusses data visualization tools. It begins with an overview of data visualization, describing how visualizing data can help identify patterns and trends. It then discusses advantages like aiding quick understanding. Five types of data visualization are mentioned but not described. The document primarily focuses on reviewing popular data visualization tools like Tableau, FusionCharts, Datawrapper, Highcharts, Excel, Sisense, Plotly, and others. It provides brief descriptions of each tool's features and capabilities. In closing, it references additional resources on the topic.
The document discusses data visualization and analytics. It defines data visualization as the graphical representation of information and data using visual elements like charts and graphs. This provides an accessible way to see trends, outliers, and patterns in data. Data visualization sits at the intersection of analysis and visual storytelling, helping make data understandable and informing decisions. The document also covers types of visualizations, examples, tools for data visualization like Tableau and Excel, and factors to consider when choosing analytics tools.
The document discusses various techniques for visualizing data, from basic charts to approaches for big data. It covers common basic chart types like line graphs, bar charts, scatter plots, and pie charts. For big data, it addresses challenges like large data volumes, different data varieties, visualization velocity, and filtering. The document recommends understanding your data and goals to select the best visualizations, and introduces SAS Visual Analytics as a tool that performs automatic charting to help users visualize big data.
This document provides an introduction to data visualization. It discusses the importance of data visualization for clearly communicating complex ideas in reports and statements. The document outlines the data visualization process and different types of data and relationships that can be visualized, including quantitative and qualitative data. It also discusses various formats for visualizing data, with the goal of helping readers understand data visualization and how to create interactive visuals and analyze data.
This document provides information about Tableau, a data visualization software. It discusses Tableau's prerequisites, products, and architecture. Tableau allows users to easily connect to various data sources and transform data into interactive visualizations and dashboards. Key Tableau concepts covered include data sources, worksheets, dashboards, stories, filters, marks, color and size properties. The document also explains Tableau's desktop and server products, and the stages of importing data, analyzing it, and sharing results.
The document discusses exploratory data analysis and provides examples of how it can be used. It summarizes two case studies: one where an energy utility detected billing fraud by analyzing meter reading patterns, and another where month of birth was found to correlate with exam scores for students in Tamil Nadu. The document then outlines the exploratory data analysis process and provides a high-level overview of U.S. and Indian birth date patterns identified through analysis of large datasets.
Just finished a basic course on data science (highly recommend it if you wish to explore what data science is all about). Here are my takeaways from the course.
This presentation introduces big data and explains how to generate actionable insights using analytics techniques. The deck explains general steps involved in a typical analytics project and provides a brief overview of the most commonly used predictive analytics methods and their business applications.
Vijay Adamapure is a Data Science Enthusiast with extensive experience in the field of data mining, predictive modeling and machine learning. He has worked on numerous analytics projects ranging from healthcare, business analytics, renewable energy to IoT.
Vijay presented these slides during the Internet of Everything Meetup event 'Predictive Analytics - An Overview' that took place on Jan. 9, 2015 in Mumbai. To join the Meetup group, register here: http://bit.ly/1A7T0A1
Data visualization is the graphical representation of information and data using visual elements like charts, graphs, and maps to provide an accessible way to see and understand trends and patterns in data. It allows massive amounts of information to be analyzed and data-driven decisions to be made. Data visualization tells a story by removing noise from data and highlighting useful information. Common types include charts, graphs, maps, and infographics, with tools ranging from simple online options to more complex offline programs. The key is to focus on best practices and developing a personal style when creating visualizations.
Guía sobre como diseñar graficos para tus informesGroupM Spain
This document provides guidance on designing effective data visualizations. It discusses the main types of charts and graphs including bar charts, pie charts, line charts, area charts, scatter plots, bubble charts and heat maps. For each type, it describes best practices for design, such as starting the y-axis at 0, using consistent colors, and limiting the number of data series shown. The goal is to help readers understand their data and tell their intended story through clear, impactful visual representations.
This document provides guidance on creating and using figures, graphs, and tables in a PowerPoint presentation. It discusses the purpose and structure of different types of graphs like bar graphs, line graphs, pie charts, and histograms. It also covers best practices for labeling axes, formatting data clearly, and addressing limitations of certain visual representations. Graphs are useful for showing relationships between variables and trends in data, but they do not provide all contextual details of an investigation.
This document provides guidance on creating and using figures, graphs, and tables in a PowerPoint presentation. It discusses the purpose and structure of different types of graphs like bar graphs, line graphs, pie charts, and histograms. It also covers best practices for labeling axes, scaling data appropriately, and ensuring graphs are readable. The document notes that while graphs are useful for showing trends and relationships in data, they have limitations and don't provide all contextual details of an investigation. Tables are also discussed as an organizer for presenting data but can be less clear for showing numerical patterns than graphs.
This document provides guidance on making sense of data and effectively communicating insights through visualizations. It discusses challenges organizations face in analyzing large amounts of data and offers tips for selecting appropriate chart types to analyze and present different types of data. Examples include using histograms to show variation, Pareto charts for identifying priorities, and line and moving average charts for trends over time. The goal is to help organizations and individuals at all levels better understand and make decisions based on data.
This document provides an overview of different types of charts used for data visualization, including column charts, bar charts, pie charts, doughnut charts, line charts, area charts, scatter charts, spider/radar charts, gauge charts, and comparison charts. It describes the purpose and use of each chart type, highlighting when each type is most effective to visualize different kinds of data relationships. The document aims to help readers select the most appropriate chart type based on their data and visualization goals.
This document provides an overview of data visualization techniques that can help non-technical audiences understand and make sense of data. It discusses the importance of selecting the right chart type for the data, such as using histograms to show variation, line graphs for trends over time, and Pareto charts to identify the vital few causes of issues. The document also covers techniques for smoothing time series data, such as moving averages, to identify underlying trends. The goal is to help organizations at all levels make better decisions and improve performance through effective data communication and interpretation.
This document discusses various methods for graphically displaying data in statistics, including time series graphs, bar charts, histograms, circle graphs, dot plots, stem plots, ogives, and indicators of misleading graphs. It provides examples and descriptions of how to properly interpret and construct each type of graph. Key points include showing change over time with time series graphs, comparing categories with bar charts, displaying continuous or binned data with histograms, showing percentages with circle graphs, listing all values with dot and stem plots, and calculating cumulative frequencies with ogives. Misleading graphs are identified as those that distort scale, lack labels, omit data, or have uneven bins.
The document discusses different types of charts including column charts, bar charts, pie charts, line charts, area charts, stock charts, radar charts, bubble charts, scatter charts, and combo charts. For each chart type, the document outlines typical uses, advantages, and disadvantages. It provides an example of each chart type to illustrate how the chart can be constructed and interpreted.
Introduction to Business analytics unit3jayarellirs
This document discusses various methods for visualizing and summarizing data. It describes different types of charts like column charts, line charts, pie charts, and scatter plots that can be used to visualize quantitative data. It also discusses tools in Excel for filtering, sorting, and summarizing data in tables and how techniques like Pareto analysis can help identify key factors.
This document defines and provides guidance on different types of data visualization charts. It discusses bar charts, line charts, pie charts, waterfall charts, funnel charts and area charts. For each type of chart, it provides an overview of what the chart is used for visually, as well as recommendations for when to use and not use each specific chart type based on the type of data being visualized. The goal is to help users select the most appropriate chart to clearly represent their data.
This document discusses various methods for describing data statistically, including tables, figures, and text. It provides examples and guidelines for different types of tables and figures (line graphs, area graphs, bar graphs, pie charts) and discusses when each is most appropriate. The key methods covered are tabular presentations, line graphs to show changes over time, bar graphs for comparisons, and pie charts to show proportions of a whole. It concludes by providing recommendations for choosing between tables, figures, and text based on the type of data being presented and the goals of the analysis.
Visuals should be used to present ideas completely, find relationships between concepts, emphasize important material, and present information compactly with less repetition. When selecting visuals, choose the type of visual that best matches the story or relationship you want to convey in the data. Different visual types like pie charts, bar graphs, and tables are better suited for certain types of stories and relationships. It is important to design visuals following conventions like clear titles, labels, sources and fit the visual to the story or relationship in the data.
The document discusses various methods for representing and summarizing geographical data, including:
1. Random sampling techniques like random number tables that avoid bias when selecting sample locations.
2. Methods for presenting data like line graphs, bar charts, histograms, pie charts and scatter plots, and considerations for each type.
3. Measures of central tendency like the mean, median and mode, and measures of spread like the standard deviation, interquartile range and range, to summarize and describe data sets.
The document discusses using visuals to present information and find stories within data. It explains that visuals make ideas more vivid and help emphasize important points. Different types of visuals like tables, charts, graphs and flowcharts are best suited for certain types of stories and relationships within data. The document provides guidance on how to select the appropriate visual to match the story, design visuals clearly, and find stories within data by looking for relationships and changes over time.
The document discusses using visuals to present information and stories in data. It explains that visuals help make ideas more complete, find relationships, make points vivid, emphasize key material, and present information compactly. Different types of visuals like tables, charts, graphs and flowcharts are best suited for certain types of stories and relationships. Design conventions like clear titles, labels, legends and sources should be followed. The best visual depends on whether the reader needs exact values or to see relationships and changes. Matching the right visual to the story is important for effective communication.
The document provides guidelines for creating effective data visualizations. It discusses techniques like emphasizing important data, orienting views for legibility, organizing views, avoiding overloading views, and limiting colors and shapes. It also discusses designing holistic dashboards, including general guidelines, interactivity, highlighting, filtering, hyperlinking, sizing visualizations, and making visualizations more effective. Examples are provided to illustrate good versus great visualizations.
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2. TABLE OF
CONTENTS
INTRO
Bar Chart
Pie Chart
Line Chart
Area Chart
Scatter Plot
Bubble Chart
Heat Map
6
9
11
13
15
17
19
1
FINDING THE STORY IN
YOUR DATA
KNOW YOUR DATA
GUIDE TO CHART TYPES
10 DATA DESIGN
DO’S AND DONT’S
2
3
5
21
3. If your data is misrepresented or presented
ineffectively, key insights and understanding
are lost, which hurts both your message and
your reputation. The good news is that you
don’t need a PhD in statistics to crack the data
visualization code. This guide will walk you
through the most common charts and
visualizations, help you choose the right
presentation for your data, and give you
practical design tips and tricks to make sure
you avoid rookie mistakes. It’s everything you
need to help your data make a big impact.
What’s the ideal distance
between columns in a bar chart?
Your data is only as good as
your ability to understand and
communicate it, which is why
choosing the right visualization
is essential.
You’re about to find out.
1
4. FINDING THE STORY
IN YOUR DATA
Information can be visualized in a number of ways, each of which can
provide a specific insight. When you start to work with your data, it’s
important to identify and understand the story you are trying to tell and
the relationship you are looking to show. Knowing this information will
help you select the proper visualization to best deliver your message.
When analyzing data, search for patterns or interesting insights that can
be a good starting place for finding your story, such as:
TRENDS CORRELATIONS OUTLIERS
Example:
Ice cream sales
over time
Example:
Ice cream sales vs.
temperature
Example:
Ice cream sales in an
unusual region 2
5. CONTINUOUS
DISCRETE
CATEGORICAL
QUANTITATIVE
Before understanding visualizations, you
must understand the types of data that
can be visualized and their relationships
to each other. Here are some of the most
common you are likely to encounter.
Data that can be sorted according to group or
category. Example: Types of products sold.
Numerical data that has a finite number of
possible values. Example: Number of
employees in the office.
Data that is measured and has a value within a
range. Example: Rainfall in a year.
Data that can be counted or measured;
all values are numerical.
KNOW YOUR
DATA
DATA TYPES
3
6. PART-TO-WHOLE
RELATIONSHIPS
This shows a subset of data compared to the
larger whole. Example: Percentage of
customers purchasing specific products.
DISTRIBUTION
This shows data distribution, often around a
central value. Example: Heights of players on a
basketball team.
TIME-SERIES
This tracks changes in values of a consistent
metric over time. Example: Monthly sales.
RANKING
This shows how two or more values compare
to each other in relative magnitude. Example:
Historic weather patterns, ranked from the
hottest months to the coldest.
DEVIATION
This examines how data points relate to each
other, particularly how far any given data point
differs from the mean. Example: Amusement
park tickets sold on a rainy day vs. a regular day.
CORRELATION
This is data with two or more variables that may
demonstrate a positive or negative correlation
to each other. Example: Salaries according to
education level.
Now that you’ve got a handle on the most common data
types and relationships you’ll most likely have to work
with, let’s dive into the different ways you can visualize
that data to get your point across.
NOMINAL COMPARISON
This is a simple comparison of the quantitative
values of subcategories. Example: Number of
visitors to various websites.
DATA RELATIONSHIPS
4
7. GUIDE TO
CHART TYPES
In this section, we’ll cover the uses, variations,
and best practices for some of the most common
data visualizations:
BAR CHART
PIE CHART
LINE CHART
AREA CHART
SCATTER PLOT
BUBBLE CHART
HEAT MAP
5
8. CONTENT PUBLISHED, BY CATEGORY
VARIATIONS OF BAR CHARTS
Bar charts are very versatile. They are best used
to show change over time, compare different
categories, or compare parts of a whole.
BAR CHART
VERTICAL
(COLUMN CHART)
Best used for chronological data (time-series
should always run left to right), or when
visualizing negative values below the x-axis.
6
HORIZONTAL
Best used for data with long category labels.
PAGE VIEWS, BY MONTH
9. 100% STACKED
Best used when the total value of each category
is unimportant and percentage distribution of
subcategories is the primary message.
VARIATIONS OF BAR CHARTS (CONT.)
BAR CHART
7
STACKED
Best used when there is a need to compare
multiple part-to-whole relationships. These can
use discrete or continuous data, oriented either
vertically or horizontally.
MONTHLY TRAFFIC, BY SOURCE PERCENTAGE OF CONTENT PUBLISHED, BY
MONTH
10. DESIGN BEST PRACTICES
START THE Y-AXIS VALUE AT 0
Starting at a value above zero truncates the bars
and doesn’t accurately reflect the full value.
USE HORIZONTAL LABELS
Avoid steep diagonal or vertical type, as it can
be difficult to read.
ORDER DATA APPROPRIATELY
Order categories alphabetically, sequentially, or
by value.
SPACE BARS APPROPRIATELY
Space between bars should be ½ bar width.
USE CONSISTENT COLORS
Use one color for bar charts. You may use an
accent color to highlight a significant data point.
BAR CHART
8
11. VARIATIONS OF PIE CHARTS
THE CASE AGAINST
THE PIE CHART
Pie charts are best used for making part-to-whole
comparisons with discrete or continuous data. They
are most impactful with a small data set.
The pie chart is one of the most popular
chart types. However, some critics, such
as data visualization expert Stephen Few,
are not fans. They argue that we are really
only able to gauge the size of pie slices
if they are in familiar percentages (25%,
50%, 75%, 100%) and positions, because
they are common angles. We interpret
other angles inconsistently, making it
difficult to compare relative sizes and
therefore less effective.
STANDARD
Used to show part-to-whole relationships.
DONUT
Stylistic variation that enables the inclusion of a
total value or design element in the center.
PIE CHART
9
12. DESIGN BEST PRACTICES
VISUALIZE NO MORE THAN
5 CATEGORIES PER CHART
It is difficult to differentiate between small
values; depicting too many slices decreases the
impact of the visualization. If needed, you can
group smaller values into an “other” or
“miscellaneous” category, but make sure it does
not hide interesting or significant information.
DON’T USE MULTIPLE PIE CHARTS
FOR COMPARISON
Slice sizes are very difficult to compare
side-by-side. Use a stacked bar chart instead.
ORDER SLICES
CORRECTLY
There are two ways to
order sections, both
of which are meant to
aid comprehension:
OPTION 1
Place the largest section at
12 o’clock, going clockwise.
Place the second largest
section at 12 o’clock,
going counterclockwise. The
remaining sections can be
placed below, continuing
counterclockwise.
OPTION 2
Start the largest section at
12 o’clock, going clockwise.
Place remaining sections in
descending order, going
clockwise.
MAKE SURE ALL DATA ADDS UP TO 100%
Verify that values total 100% and that pie slices
are sized proportionate to their corresponding
value.
PIE CHART
10
1
5
4
3
2 1
2
3
4
5
13. Line charts are used to show time-series relationships
with continuous data. They help show trend, acceleration,
deceleration, and volatility.
LINE CHART
11
14. DON’T PLOT MORE THAN 4 LINES
If you need to display more, break them out into
separate charts for better comparison.
USE SOLID LINES ONLY
Dashed and dotted lines can be distracting.
USE THE RIGHT HEIGHT
Plot all data points so that the line chart
takes up approximately two-thirds of the y-axis’
total scale.
INCLUDE A ZERO BASELINE IF POSSIBLE
Although a line chart does not have to start at a
zero baseline, it should be included if possible.
If relatively small fluctuations in data are
meaningful (e.g., in stock market data), you may
truncate the scale to showcase these variances.
LABEL THE LINES DIRECTLY
This lets readers quickly identify lines and
corresponding labels instead of referencing
a legend.
LINE CHART
DESIGN BEST PRACTICES
12
15. AREA CHART STACKED AREA 100% STACKED AREA
Area charts depict a time-series relationship, but they are
different than line charts in that they can represent volume.
Best used to show or compare a quantitative
progression over time.
Best used to visualize part-to-whole
relationships, helping show how each
category contributes to the cumulative total.
Best used to show distribution of categories as
part of a whole, where the cumulative total is
unimportant.
AREA CHART
VARIATIONS OF AREA CHARTS
13
16. DON’T DISPLAY MORE THAN
4 DATA CATEGORIES
Too many will result in a cluttered visual that is
difficult to decipher.
MAKE IT EASY TO READ
In stacked area charts, arrange data to position
categories with highly variable data on the top
of the chart and low variability on the bottom.
START Y-AXIS VALUE AT 0
Starting the axis above zero truncates the
visualization of values.
USE TRANSPARENT COLORS
In standard area charts, ensure data isn’t
obscured in the background by ordering
thoughtfully and using transparency.
DON’T USE AREA CHARTS TO
DISPLAY DISCRETE DATA
The connected lines imply intermediate values,
which only exist with continuous data.
AREA CHART
DESIGN BEST PRACTICES
14
17. Scatter plots show the relationship between items
based on two sets of variables. They are best used to
show correlation in a large amount of data.
SCATTER PLOT
15
18. START Y-AXIS VALUE AT 0
Starting the axis above zero truncates the
visualization of values.
USE TREND LINES
These help draw correlation between the
variables to show trends.
DON’T COMPARE MORE THAN
2 TREND LINES
Too many lines make data difficult to interpret.
INCLUDE MORE VARIABLES
Use size and dot color to encode additional
data variables.
SCATTER PLOT
DESIGN BEST PRACTICES
16
19. BUBBLE PLOT BUBBLE MAP
Bubble charts are good for displaying nominal
comparisons or ranking relationships.
This is a scatter plot with bubbles, best used to
display an additional variable.
Best used for visualizing values for specific
geographic regions.
BUBBLE CHART
VARIATIONS OF
BUBBLE CHARTS
17
20. SIZE BUBBLES APPROPRIATELY DON’T USE ODD SHAPESMAKE SURE LABELS ARE VISIBLE
Bubbles should be scaled according to area,
not diameter.
Avoid adding too much detail or using shapes
that are not entirely circular; this can lead to
inaccuracies.
All labels should be unobstructed and easily
identified with the corresponding bubble.
DESIGN BEST PRACTICES
BUBBLE CHART
18
21. Heat maps display categorical data, using intensity
of color to represent values of geographic areas or
data tables.
HEAT MAP
19
22. USE A SIMPLE MAP OUTLINE SELECT COLORS APPROPRIATELY
USE PATTERNS SPARINGLY CHOOSE APPROPRIATE DATA RANGES
These lines are meant to frame the data, not
distract.
Some colors stand out more than others, giving
unnecessary weight to that data. Instead, use a
single color with varying shade or a spectrum
between two analogous colors to show
intensity. Also remember to intuitively code
color intensity according to values.
A pattern overlay that indicates a second
variable is acceptable, but using multiple is
overwhelming and distracting.
Select 3-5 numerical ranges that enable fairly
even distribution of data between them. Use +/-
signs to extend high and low ranges.
DESIGN BEST PRACTICES
FL
TX
NM
AZ
AK
CA
NV
UT
CO
OR
WA
ID
HI
OK
MT
WY
ND
SD
NE
KS
MN
IA
MO
AR
LA
MS AL GA
SC
IL
WI
MI
IN
OH
TN
KY
NC
WV VA
PA
NY
ME
VT NH
RI
CT
NJ
DE
MD
MA
DC
HEAT MAP
20
23. 1 | DO USE ONE COLOR TO
REPRESENT EACH CATEGORY.
2 | DO ORDER DATA SETS USING
LOGICAL HEIRARCHY.
3 | DO USE CALLOUTS TO
HIGHLIGHT IMPORTANT OR
INTERESTING INFORMATION.
4 | DO VISUALIZE DATA IN A WAY
THAT IS EASY FOR READERS TO
COMPARE VALUES.
5 | DO USE ICONS TO ENHANCE
COMPREHENSION AND REDUCE
UNNECESSARY LABELING.
6 | DON’T USE HIGH CONTRAST
COLOR COMBINATIONS SUCH AS
RED/GREEN OR BLUE/YELLOW.
7 | DON’T USE 3D CHARTS. THEY
CAN SKEW PERCEPTION OF THE
VISUALIZATION.
8 | DON’T ADD CHART JUNK.
UNNECESSARY ILLUSTRATIONS,
DROP SHADOWS, OR
ORNAMENTATIONS DISTRACT
FROM THE DATA.
9 | DON’T USE MORE THAN 6
COLORS IN A SINGLE LAYOUT.
10 | DON’T USE DISTRACTING
FONTS OR ELEMENTS (SUCH AS
BOLD, ITALIC, OR UNDERLINED
TEXT).
Designing your data doesn’t have to be
overwhelming. With a basic understanding of
how different data sets should be visualized,
along with a few fundamental design tips and
best practices, you can create more accurate,
more effective data visualizations. Follow these
10 tips to ensure your design does your data
justice.
10 DATA DESIGN
DOS AND
DON’TS
21
25. HubSpot is the world’s leading inbound marketing and sales platform. Over 10,000 customers in
65 countries use HubSpot’s award-winning software, services, and support to create an inbound
experience that will attract, engage, and delight customers. To find out how HubSpot can grow
your business, watch this video overview, get a demo, or schedule a free inbound marketing
assessment with one of our consultants.
SOURCES: Infographics: The Power of Visual Storytelling by Ross Crooks, Jason Lankow and Josh Ritchie (Wiley 2012); The Wall Street Journal Guide to Information
Graphics by Dona Wong (Dow Jones & Company 2010); Visualize This by Nathan Yau (Wiley 2011)
Visage was created because we believe that good design should be available to everyone, not
just organizations that can afford design agency premiums. Our unique web-based software
enables non-designers to create beautiful, on-brand data visualizations and visual
content. Learn more and schedule a demo at visage.co.
A COLLABORATION BETWEEN:
ALL CHARTS AND GRAPHS THAT APPEAR IN THIS BOOK WERE CREATED WITH VISAGE.