what is an graphical representation

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Graphical Representation

Graphical Representation is a way of analysing numerical data. It exhibits the relation between data, ideas, information and concepts in a diagram. It is easy to understand and it is one of the most important learning strategies. It always depends on the type of information in a particular domain. There are different types of graphical representation. Some of them are as follows:

Line Graphs – Line graph or the linear graph is used to display the continuous data and it is useful for predicting future events over time.
Bar Graphs – Bar Graph is used to display the category of data and it compares the data using solid bars to represent the quantities.
Histograms – The graph that uses bars to represent the frequency of numerical data that are organised into intervals. Since all the intervals are equal and continuous, all the bars have the same width.
Line Plot – It shows the frequency of data on a given number line. ‘ x ‘ is placed above a number line each time when that data occurs again.
Frequency Table – The table shows the number of pieces of data that falls within the given interval.
Circle Graph – Also known as the pie chart that shows the relationships of the parts of the whole. The circle is considered with 100% and the categories occupied is represented with that specific percentage like 15%, 56%, etc.
Stem and Leaf Plot – In the stem and leaf plot, the data are organised from least value to the greatest value. The digits of the least place values from the leaves and the next place value digit forms the stems.
Box and Whisker Plot – The plot diagram summarises the data by dividing into four parts. Box and whisker show the range (spread) and the middle ( median) of the data.

General Rules for Graphical Representation of Data

There are certain rules to effectively present the information in the graphical representation. They are:

Suitable Title: Make sure that the appropriate title is given to the graph which indicates the subject of the presentation.
Measurement Unit: Mention the measurement unit in the graph.
Proper Scale: To represent the data in an accurate manner, choose a proper scale.
Index: Index the appropriate colours, shades, lines, design in the graphs for better understanding.
Data Sources: Include the source of information wherever it is necessary at the bottom of the graph.
Keep it Simple: Construct a graph in an easy way that everyone can understand.
Neat: Choose the correct size, fonts, colours etc in such a way that the graph should be a visual aid for the presentation of information.

Graphical Representation in Maths

In Mathematics, a graph is defined as a chart with statistical data, which are represented in the form of curves or lines drawn across the coordinate point plotted on its surface. It helps to study the relationship between two variables where it helps to measure the change in the variable amount with respect to another variable within a given interval of time. It helps to study the series distribution and frequency distribution for a given problem. There are two types of graphs to visually depict the information. They are:

Time Series Graphs – Example: Line Graph
Frequency Distribution Graphs – Example: Frequency Polygon Graph

Principles of Graphical Representation

Algebraic principles are applied to all types of graphical representation of data. In graphs, it is represented using two lines called coordinate axes. The horizontal axis is denoted as the x-axis and the vertical axis is denoted as the y-axis. The point at which two lines intersect is called an origin ‘O’. Consider x-axis, the distance from the origin to the right side will take a positive value and the distance from the origin to the left side will take a negative value. Similarly, for the y-axis, the points above the origin will take a positive value, and the points below the origin will a negative value.

Generally, the frequency distribution is represented in four methods, namely

Smoothed frequency graph
Pie diagram
Cumulative or ogive frequency graph
Frequency Polygon

Merits of Using Graphs

Some of the merits of using graphs are as follows:

The graph is easily understood by everyone without any prior knowledge.
It saves time
It allows us to relate and compare the data for different time periods
It is used in statistics to determine the mean, median and mode for different data, as well as in the interpolation and the extrapolation of data.

Example for Frequency polygonGraph

Here are the steps to follow to find the frequency distribution of a frequency polygon and it is represented in a graphical way.

Obtain the frequency distribution and find the midpoints of each class interval.
Represent the midpoints along x-axis and frequencies along the y-axis.
Plot the points corresponding to the frequency at each midpoint.
Join these points, using lines in order.
To complete the polygon, join the point at each end immediately to the lower or higher class marks on the x-axis.

Draw the frequency polygon for the following data

	10-20	20-30	30-40	40-50	50-60	60-70	70-80	80-90
	4	6	8	10	12	14	7	5

Mark the class interval along x-axis and frequencies along the y-axis.

Let assume that class interval 0-10 with frequency zero and 90-100 with frequency zero.

Now calculate the midpoint of the class interval.


0-10	5	0
10-20	15	4
20-30	25	6
30-40	35	8
40-50	45	10
50-60	55	12
60-70	65	14
70-80	75	7
80-90	85	5
90-100	95	0

Using the midpoint and the frequency value from the above table, plot the points A (5, 0), B (15, 4), C (25, 6), D (35, 8), E (45, 10), F (55, 12), G (65, 14), H (75, 7), I (85, 5) and J (95, 0).

To obtain the frequency polygon ABCDEFGHIJ, draw the line segments AB, BC, CD, DE, EF, FG, GH, HI, IJ, and connect all the points.

Frequently Asked Questions

What are the different types of graphical representation.

Some of the various types of graphical representation include:

Line Graphs
Frequency Table
Circle Graph, etc.

What are the Advantages of Graphical Method?

Some of the advantages of graphical representation are:

It makes data more easily understandable.
It saves time.
It makes the comparison of data more efficient.

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Graphical Representation

Graphical representation definition.

Graphical representation refers to the use of charts and graphs to visually display, analyze, clarify, and interpret numerical data, functions, and other qualitative structures. ‍

What is Graphical Representation?

Graphical representation refers to the use of intuitive charts to clearly visualize and simplify data sets. Data is ingested into graphical representation of data software and then represented by a variety of symbols, such as lines on a line chart, bars on a bar chart, or slices on a pie chart, from which users can gain greater insight than by numerical analysis alone.

Representational graphics can quickly illustrate general behavior and highlight phenomenons, anomalies, and relationships between data points that may otherwise be overlooked, and may contribute to predictions and better, data-driven decisions. The types of representational graphics used will depend on the type of data being explored.

Types of Graphical Representation

Data charts are available in a wide variety of maps, diagrams, and graphs that typically include textual titles and legends to denote the purpose, measurement units, and variables of the chart. Choosing the most appropriate chart depends on a variety of different factors -- the nature of the data, the purpose of the chart, and whether a graphical representation of qualitative data or a graphical representation of quantitative data is being depicted. There are dozens of different formats for graphical representation of data. Some of the most popular charts include:

Bar Graph -- contains a vertical axis and horizontal axis and displays data as rectangular bars with lengths proportional to the values that they represent; a useful visual aid for marketing purposes
Choropleth -- thematic map in which an aggregate summary of a geographic characteristic within an area is represented by patterns of shading proportionate to a statistical variable
Flow Chart -- diagram that depicts a workflow graphical representation with the use of arrows and geometric shapes; a useful visual aid for business and finance purposes
Heatmap -- a colored, two-dimensional matrix of cells in which each cell represents a grouping of data and each cell’s color indicates its relative value
Histogram – frequency distribution and graphical representation uses adjacent vertical bars erected over discrete intervals to represent the data frequency within a given interval; a useful visual aid for meteorology and environment purposes
Line Graph – displays continuous data; ideal for predicting future events over time; a useful visual aid for marketing purposes
Pie Chart -- shows percentage values as a slice of pie; a useful visual aid for marketing purposes
Pointmap -- CAD & GIS contract mapping and drafting solution that visualizes the location of data on a map by plotting geographic latitude and longitude data
Scatter plot -- a diagram that shows the relationship between two sets of data, where each dot represents individual pieces of data and each axis represents a quantitative measure
Stacked Bar Graph -- a graph in which each bar is segmented into parts, with the entire bar representing the whole, and each segment representing different categories of that whole; a useful visual aid for political science and sociology purposes
Timeline Chart -- a long bar labelled with dates paralleling it that display a list of events in chronological order, a useful visual aid for history charting purposes
Tree Diagram -- a hierarchical genealogical tree that illustrates a family structure; a useful visual aid for history charting purposes
Venn Diagram -- consists of multiple overlapping usually circles, each representing a set; the default inner join graphical representation

Proprietary and open source software for graphical representation of data is available in a wide variety of programming languages. Software packages often provide spreadsheets equipped with built-in charting functions.

Advantages and Disadvantages of Graphical Representation of Data

Tabular and graphical representation of data are a vital component in analyzing and understanding large quantities of numerical data and the relationship between data points. Data visualization is one of the most fundamental approaches to data analysis, providing an intuitive and universal means to visualize, abstract, and share complex data patterns. The primary advantages of graphical representation of data are:

Facilitates and improves learning: graphics make data easy to understand and eliminate language and literacy barriers
Understanding content: visuals are more effective than text in human understanding
Flexibility of use: graphical representation can be leveraged in nearly every field involving data
Increases structured thinking: users can make quick, data-driven decisions at a glance with visual aids
Supports creative, personalized reports for more engaging and stimulating visual presentations
Improves communication: analyzing graphs that highlight relevant themes is significantly faster than reading through a descriptive report line by line
Shows the whole picture: an instantaneous, full view of all variables, time frames, data behavior and relationships

Disadvantages of graphical representation of data typically concern the cost of human effort and resources, the process of selecting the most appropriate graphical and tabular representation of data, greater design complexity of visualizing data, and the potential for human bias.

Why Graphical Representation of Data is Important

Graphic visual representation of information is a crucial component in understanding and identifying patterns and trends in the ever increasing flow of data. Graphical representation enables the quick analysis of large amounts of data at one time and can aid in making predictions and informed decisions. Data visualizations also make collaboration significantly more efficient by using familiar visual metaphors to illustrate relationships and highlight meaning, eliminating complex, long-winded explanations of an otherwise chaotic-looking array of figures.

Data only has value once its significance has been revealed and consumed, and its consumption is best facilitated with graphical representation tools that are designed with human cognition and perception in mind. Human visual processing is very efficient at detecting relationships and changes between sizes, shapes, colors, and quantities. Attempting to gain insight from numerical data alone, especially in big data instances in which there may be billions of rows of data, is exceedingly cumbersome and inefficient.

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Introduction to Graphs

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15 December 2020

Read time: 6 minutes

Introduction

What are graphs?

What are the different types of data?

What are the different types of graphical representations?

The graph is nothing but an organized representation of data. It helps us to understand the data. Data are the numerical information collected through observation.

The word data came from the Latin word Datum which means “something given”

After a research question is developed, data is being collected continuously through observation. Then it is organized, summarized, classified, and then represented graphically.

Differences between Data and information: Data is the raw fact without any add on but the information is the meaning derived from data.

Data	Information
Raw facts of things	Data with exact meaning
No contextual meaning	Processed data and organized context
Just numbers and text

Introduction to Graphs-PDF

The graph is nothing but an organized representation of data. It helps us to understand the data. Data are the numerical information collected through observation. Here is a downloadable PDF to explore more.

📥

Line and Bar Graphs Application
Graphs in Mathematics & Statistics

What are the different Types of Data?

There are two types of Data :

Quantitative

The data which are statistical or numerical are known as Quantitive data. Quantitive data is generated through. Quantitative data is also known as Structured data. Experiments, Tests, Surveys, Market Report.

Quantitive data is again divided into Continuous data and Discrete data.

Continuous Data

Continuous data is the data which can have any value. That means Continuous data can give infinite outcomes so it should be grouped before representing on a graph.

The speed of a vehicle as it passes a checkpoint
The mass of a cooking apple
The time taken by a volunteer to perform a task

Discrete Data

Discrete data can have certain values. That means only a finite number can be categorized as discrete data.

Numbers of cars sold at a dealership during a given month
Number of houses in certain block
Number of fish caught on a fishing trip
Number of complaints received at the office of airline on a given day
Number of customers who visit at bank during any given hour
Number of heads obtained in three tosses of a coin

Differences between Discrete and Continuous data

Numerical data could be either discrete or continuous
Continuous data can take any numerical value (within a range); For example, weight, height, etc.
There can be an infinite number of possible values in continuous data
Discrete data can take only certain values by finite ‘jumps’, i.e., it ‘jumps’ from one value to another but does not take any intermediate value between them (For example, number of students in the class)

Qualitative

Data that deals with description or quality instead of numbers are known as Quantitative data. Qualitative data is also known as unstructured data. Because this type of data is loosely compact and can’t be analyzed conventionally.

Different Types of Graphical Representations

There are many types of graph we can use to represent data. They are as follows,

A bar graph or chart is a way to represent data by rectangular column or bar. The heights or length of the bar is proportional to the values.

A line graph is a type of graph where the information or data is plotted as some dots which are known as markers and then they are added to each other by a straight line.

The line graph is normally used to represent the data that changes over time.

A histogram graph is a graph where the information is represented along with the height of the rectangular bar. Though it does look like a bar graph, there is a fundamental difference between them. With the histogram, each column represents a range of quantitative data when a bar graph represents categorical variables.

The other name of the pie chart is a circle graph. It is a circular chart where numerical information represents as slices or in fractional form or percentage where the whole circle is 100%.

Stem and leaf plot

The stem and leaf plot is a way to represents quantitative data according to frequency ranges or frequency distribution.

In the stem and leaf plot, each data is split into stem and leaf, which is 32 will be split into 3 stems and 2 leaves.

Frequency table: Frequency means the number of occurrences of an event. A frequency distribution table is a graph or chart which shows the frequency of events. It is denoted as ‘f’ .

Pictograph or Pictogram is the earliest way to represents data in a pictorial form or by using symbols or images. And each image represents a particular number of things.

According to the above-mentioned Pictograph, the number of Appels sold on Monday is 6x2=12.

Scatter diagrams

Scatter diagram or scatter plot is a way of graphical representation by using cartesian coordinates of two variables. The plot shows the relationship between two variables. Below there is a data table as well as a Scattergram as per the given data.


ºc
14.2º	$215
16.4º	$325
11.9º	$185
15.2º	$332
18.5º	$406
22.1º	$522
19.4º	$412
25.1º	$614

What is the meaning of Graphical representation?

Graphical representation is a way to represent and analyze quantitive data. A graph is a kind of a chart where data are plotted as variables across the coordinate. It became easy to analyze the extent of change of one variable based on the change of other variables.

Principles of graphical representation

The principles of graphical representation are algebraic. In a graph, there are two lines known as Axis or Coordinate axis. These are the X-axis and Y-axis. The horizontal axis is the X-axis and the vertical axis is the Y-axis. They are perpendicular to each other and intersect at O or point of Origin.

On the right side of the Origin, the Xaxis has a positive value and on the left side, it has a negative value. In the same way, the upper side of the Origin Y-axis has a positive value where the down one is with a negative value.

When X-axis and y-axis intersected each other at the origin it divides the plane into four parts which are called Quadrant I, Quadrant II, Quadrant III, Quadrant IV.

The location on the coordinate plane is known as the ordered pair and it is written as (x,y). That means the first value will be on the x-axis and the second one is on the y-axis. When we will plot any coordinate, we always have to start counting from the origin and have to move along the x-axis, if it is positive then to the right side, and if it is negative then to the left side. Then from the x-axis, we have to plot the y’s value, which means we have to move up for positive value or down if the value is negative along with the y-axis.

In the following graph, 1st ordered pair (2,3) where both the values of x and y are positive and it is on quadrant I. 2nd ordered pair (-3,1), here the value of x is negative and value of y is positive and it is in quadrant II. 3rd ordered pair (-1.5, -2.5), here the value of x as well as y both are Negative and in quadrant III.

Methods of representing a frequency distribution

There are four methods to represent a frequency distribution graphically. These are,

Smoothed Frequency graph
Cumulative frequency graph or Ogive.
Pie diagram.

Advantages and Disadvantages of Graphical representation of data

It improves the way of analyzing and learning as the graphical representation makes the data easy to understand.
It can be used in almost all fields from mathematics to physics to psychology and so on.
It is easy to understand for its visual impacts.
It shows the whole and huge data in an instance.

The main disadvantage of graphical representation of data is that it takes a lot of effort as well as resources to find the most appropriate data and then represents it graphically.

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Frequently Asked Questions (FAQs)

What is data.

Data are characteristics or information, usually numerical, that are collected through observation.

How do you differentiate between data and information?

Data is the raw fact without any add on but the information is the meaning derived from data.

What are the types of data?

There are two types of Data:

What are the ways to represent data?

Tables, charts and graphs are all ways of representing data , and they can be used for two broad purposes. The first is to support the collection, organisation and analysis of data as part of the process of a scientific study.

- Tables, charts and graphs are all ways of representing data, and they can be used for two broad purposes. The first is to support the collection, organisation and analysis of data as part of the process of a scientific study.

What are the different types of graphs?

Different types of graphs include:

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"There is a magic in graphs."

"the proﬁle of a curve reveals in a ﬂash a whole situation — the life history of an epidemic, a panic, or an era of prosperity. the curve informs the mind, awakens the imagination, convinces .", - henry d. hubbard, national bureau of standards, data visualization is the graphical representation of data for understanding and communication. this encompasses two primary classes of visualization:, information visualization - visualization of data. this can either be: exploratory: you are trying to explore and understand patterns and trends within your data. explanatory: there is something in your data you would like to communicate to your audience., scientific visualization - scientific visualization involves the visualization of data with an inherent spatial component. this can be the visualization of scalar, vector, and tensor fields. common areas of scientific visualization include computational fluid dynamics, medical imaging and analysis and weather data analysis..

Good data visualizations allow us to reason and think effectively about our data. By presenting information visually, it allows us offload internal cognition to the perceptual system. If we see numerical data in a table, we may be able to find a trend, but it will take a significant amount of work on our part to recognize and conceptualize that trend. By plotting that data visually, that trend becomes immediately clear to our mind through our perceptual system.

A good example of this is "Anscombe's quartet", four datasets that share the same descriptive statistics, including mean, variance, and correlation.

Upon visual inspection, it becomes immediately clear that these datasets, while seemingly identical according to common summary statistics, are each unique. This is the power of effective data visualization: it allows us to bypass cognition by communicating directly with our perceptual system.

Anscombe's Quartet plot published under the terms of "Creative Commons Attribution-Share Alike", source: http://commons.wikimedia.org/wiki/File:Anscombe%27s_quartet_3.svg Anscombe's Quartet table source: https://multithreaded.stitchfix.com/assets/images/blog/anscombes_quartet_table.png

These materials are licensed under a Creative Commons , attributable to , Johns Hopkins University.

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Guide On Graphical Representation of Data – Types, Importance, Rules, Principles And Advantages

What are Graphs and Graphical Representation?

Graphs, in the context of data visualization, are visual representations of data using various graphical elements such as charts, graphs, and diagrams. Graphical representation of data , often referred to as graphical presentation or simply graphs which plays a crucial role in conveying information effectively.

Principles of Graphical Representation

Effective graphical representation follows certain fundamental principles that ensure clarity, accuracy, and usability:Clarity : The primary goal of any graph is to convey information clearly and concisely. Graphs should be designed in a way that allows the audience to quickly grasp the key points without confusion.

Simplicity: Simplicity is key to effective data visualization. Extraneous details and unnecessary complexity should be avoided to prevent confusion and distraction.
Relevance: Include only relevant information that contributes to the understanding of the data. Irrelevant or redundant elements can clutter the graph.
Visualization: Select a graph type that is appropriate for the supplied data. Different graph formats, like bar charts, line graphs, and scatter plots, are appropriate for various sorts of data and relationships.

Rules for Graphical Representation of Data

Creating effective graphical representations of data requires adherence to certain rules:

Select the Right Graph: Choosing the appropriate type of graph is essential. For example, bar charts are suitable for comparing categories, while line charts are better for showing trends over time.
Label Axes Clearly: Axis labels should be descriptive and include units of measurement where applicable. Clear labeling ensures the audience understands the data’s context.
Use Appropriate Colors: Colors can enhance understanding but should be used judiciously. Avoid overly complex color schemes and ensure that color choices are accessible to all viewers.
Avoid Misleading Scaling: Scale axes appropriately to prevent exaggeration or distortion of data. Misleading scaling can lead to incorrect interpretations.
Include Data Sources: Always provide the source of your data. This enhances transparency and credibility.

Importance of Graphical Representation of Data

Graphical representation of data in statistics is of paramount importance for several reasons:

Enhances Understanding: Graphs simplify complex data, making it more accessible and understandable to a broad audience, regardless of their statistical expertise.
Helps Decision-Making: Visual representations of data enable informed decision-making. Decision-makers can easily grasp trends and insights, leading to better choices.
Engages the Audience: Graphs capture the audience’s attention more effectively than raw data. This engagement is particularly valuable when presenting findings or reports.
Universal Language: Graphs serve as a universal language that transcends linguistic barriers. They can convey information to a global audience without the need for translation.

Advantages of Graphical Representation

The advantages of graphical representation of data extend to various aspects of communication and analysis:

Clarity: Data is presented visually, improving clarity and reducing the likelihood of misinterpretation.
Efficiency: Graphs enable the quick absorption of information. Key insights can be found in seconds, saving time and effort.
Memorability: Visuals are more memorable than raw data. Audiences are more likely to retain information presented graphically.
Problem-Solving: Graphs help in identifying and solving problems by revealing trends, correlations, and outliers that may require further investigation.

Use of Graphical Representations

Graphical representations find applications in a multitude of fields:

Business: In the business world, graphs are used to illustrate financial data, track performance metrics, and present market trends. They are invaluable tools for strategic decision-making.
Science: Scientists employ graphs to visualize experimental results, depict scientific phenomena, and communicate research findings to both colleagues and the general public.
Education: Educators utilize graphs to teach students about data analysis, statistics, and scientific concepts. Graphs make learning more engaging and memorable.
Journalism: Journalists rely on graphs to support their stories with data-driven evidence. Graphs make news articles more informative and impactful.

Types of Graphical Representation

There exists a diverse array of graphical representations, each suited to different data types and purposes. Common types include:

1.Bar Charts:

Used to compare categories or discrete data points, often side by side.

2. Line Charts:

Ideal for showing trends and changes over time, such as stock market performance or temperature fluctuations.

3. Pie Charts:

Display parts of a whole, useful for illustrating proportions or percentages.

4. Scatter Plots:

Reveal relationships between two variables and help identify correlations.

5. Histograms:

Depict the distribution of data, especially in the context of continuous variables.

In conclusion, the graphical representation of data is an indispensable tool for simplifying complex information, aiding in decision-making, and enhancing communication across diverse fields. By following the principles and rules of effective data visualization, individuals and organizations can harness the power of graphs to convey their messages, support their arguments, and drive informed actions.

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FAQs on Graphical Representation of Data

What is the purpose of graphical representation.

Graphical representation serves the purpose of simplifying complex data, making it more accessible and understandable through visual means.

Why are graphs and diagrams important?

Graphs and diagrams are crucial because they provide visual clarity, aiding in the comprehension and retention of information.

How do graphs help learning?

Graphs engage learners by presenting information visually, which enhances understanding and retention, particularly in educational settings.

Who uses graphs?

Professionals in various fields, including scientists, analysts, educators, and business leaders, use graphs to convey data effectively and support decision-making.

Where are graphs used in real life?

Graphs are used in real-life scenarios such as business reports, scientific research, news articles, and educational materials to make data more accessible and meaningful.

Why are graphs important in business?

In business, graphs are vital for analyzing financial data, tracking performance metrics, and making informed decisions, contributing to success.

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Graphical Representation: Advantages, Types & Examples

Graphical Representation: A graph is a categorised representation of data. It helps us understand the data easily. Data is a collection of numerical figures collected through surveying. The word data came from the Latin word ‘Datum’, which means ‘something given’. After developing a research question, data is being collected constantly through observation. Then the data collected is arranged, summarised, classified, and finally represented graphically. This is the concept of graphical representation of data.

Let’s study different kinds of graphical representations with examples, the types of graphical representation, and graphical representation of data in statistics, in this article.

What Are Graphical Representations?

Graphical representation refers to the use of intuitive charts to visualise clearly and simplify data sets. Data obtained from surveying is ingested into a graphical representation of data software. Then it is represented by some symbols, such as lines on a line graph, bars on a bar chart, or slices of a pie chart. In this way, users can achieve much more clarity and understanding than by numerical study alone.

Advantages of Graphical Representation

Some of the advantages of using graphs are listed below:

The graph helps us understand the data or information even when we have no idea about it.
It saves time.
It makes it easier for us to compare the data for different time periods or different kinds.
It is mainly used in statistics to determine the mean, median and mode for different data and interpolation and extrapolation of data.

Use of Graphical Representations

The main agenda of presenting scientific data into graphs is to provide information efficiently to utilise the power of visual display while avoiding confusion or deception. This is important in communicating our findings to others and our understanding and analysis of the data.

Graphical data representation is crucial in understanding and identifying trends and patterns in the ever-increasing data flow. Graphical representation helps in quick analysis of large quantities and can support making predictions and informed decisions.

General Rules for Graphical Representation of Data

The following are a few rules to present the information in the graphical representation:

Suitable title: The title of the graph should be appropriate that indicates the subject of the presentation.
Measurement unit: The measurement unit in the graph should be mentioned.
Proper scale: Choose a proper scale to represent the data accurately.
Index: For better understanding, index the appropriate colours, shades, lines, and designs in the graphs.
Data sources: Data should be included wherever it is necessary at the bottom of the graph.
Keep it simple: The construction of a graph should be such a way that it is effortlessly understood.
Neat: The correct size, fonts, colours etc., should be chosen so that the graph should be a visual aid for presenting the information.

Types of Graphical Representation

1. Line graph 2. Histogram 3. Bar graph 4. Pie chart 5. Frequency polygon 6. Ogives or Cumulative frequency graphs

1. Line Graph

A line graph is a chart used to show information that changes over time. We plot line graphs by connecting several points with straight lines. Another name is a line chart. The line graph contains two axes: $x-$axis and $y-$axis.

The horizontal axis is the $x-$axis.
The vertical axis is the $y-$axis.

Example: The following graph shows the number of motorbikes sold on different days of the week.

2. Histogram

Continuous data represented on the two-dimensional graph is called a histogram. In the histogram, the bars are placed continuously side by side without a gap between consecutive bars. In other words, rectangles are erected on the class intervals of the distribution. The areas of the rectangles formed by bars are proportional to the frequencies.

Example: Following is an example of a histogram showing the average pass percentage of students.

3. Bar Graph

Bar graphs can be of two types – horizontal bar graphs and vertical bar graphs. While a horizontal bar graph is applied for qualitative data or data varying over space, the vertical bar graph is associated with quantitative data or time-series data.

Bars are rectangles of varying lengths and of equal width usually are drawn either horizontally or vertically. We consider multiple or grouped bar graphs to compare related series. Component or sub-divided bar diagrams are applied for representing data divided into several components.

Example: The following graph is an example of a bar graph representing the money spent month-wise

4. Pie Chart

The sector of a circle represents various observations or components, and the whole circle represents the sum of the value of all the components. The total central angle of a circle is ${360^{\rm{o}}}$ and is divided according to the values of the components.

The central angle of a component$ = \frac{{{\rm{ value}}\,{\rm{of}}\,{\rm{the}}\,{\rm{component }}}}{{{\rm{total}}\,{\rm{value}}}} \times {360^{\rm{o}}}$

Sometimes, the value of the components is expressed in percentages. In such cases, The central angle of a component$ = \frac{{{\rm{ percentage}}\,{\rm{value}}\,{\rm{of}}\,{\rm{the}}\,{\rm{component }}}}{{100}} \times {360^{\rm{o}}}$

Example: The following figure represents a pie-chart

5. Frequency Polygon

A frequency polygon is another way of representing frequency distribution graphically. Follow the steps below to make a frequency polygon:

(i) Calculate and obtain the frequency distribution and the mid-points of each class interval. (ii) Represent the mid-points along the $x-$axis and the frequencies along the $y-$axis. (iii) Mark the points corresponding to the frequency at each midpoint. (iv) Now join these points in straight lines. (v) To finish the frequency polygon, join the consecutive points at each end (as the case may be at zero frequency) on the $x-$axis.

Example: The following graph is the frequency polygon showing the road race results.

6. Ogives or Cumulative Frequency Graphs

By plotting cumulative frequency against the respective class intervals, we obtain ogives. There are two ogives – less than type ogives and more than type.

Less than type ogives is obtained by taking less than cumulative frequency on the vertical axis. We can obtain more than type ogives by plotting more than type cumulative frequency on the vertical axis and joining the plotted points successively by line segments.

Example: The below graph represents the less than and more than ogives for the entrance examination scores of $60$ students.

Solved Examples – Basic Graphical Representation

Q.1. The wildlife population in the following years, $2013, 2014, 2015, 2016, 2017, 2018,$ and $2019$ were $300, 200, 400, 600, 500, 400$ and $500,$ respectively. Represent these data using a line graph. Ans: We can represent the population for seven consecutive years by drawing a line diagram as given below. Let us consider years on the horizontal axis and population on the vertical axis.

For the year $2013,$ the population was $300.$ It can be written as a point $(2013, 300)$ Similarly, we can write the points for the succeeding years as follows: $(2014, 200), (2015, 400), (2016, 600), (2017, 500), (2018, 400)$ and $(2019, 500)$

We can obtain the line graph by plotting all these points and joining them using a ruler. The following line diagram shows the population of wildlife from $2013$ to $2019.$

Q.2. Draw a histogram for the following data that represents the marks scored by $120$ students in an examination:

	$0-20$	$20-40$	$40-60$	$60-80$	$80-100$
	$5$	$10$	$40$	$45$	$20$

Ans: The class intervals are of an equal length of $20$ marks. Let us indicate the class intervals along the $x-$axis and the number of students along the $y-$axis, with the appropriate scale. The histogram is given below.

Q.3. The total number of scoops of vanilla ice cream in the different months of a year is given below:


	$240$	$400$	$440$	$320$	$200$

For the above data, draw a bar graph. Ans: The following graph represents the number of vanilla ice cream scoops sold from March to July. The month is indicated along the $x-$axis, and the number of scoops sold is represented along the $y-$axis.

Q.4. The number of hours spent by a working woman on various activities on a working day is given below. Using the angle measurement, draw a pie chart.


	$3$	$7$	$2$	$9$	$1$	$2$

Ans: The central angle of a component$ = \frac{{{\rm{ value}}\,{\rm{of}}\,{\rm{the}}\,{\rm{component }}}}{{{\rm{total}}\,{\rm{value}}}} \times {360^{\rm{o}}}$. We may calculate the central angles for various components as follow:


Household	$3$	$\frac{3}{{24}} \times {360^{\rm{o}}} = {45^{\rm{o}}}$
Sleep	$7$	$\frac{7}{{24}} \times {360^{\rm{o}}} = {105^{\rm{o}}}$
Cooking	$2$	$\frac{2}{{24}} \times {360^{\rm{o}}} = {30^{\rm{o}}}$
Office	$9$	$\frac{9}{{24}} \times {360^{\rm{o}}} = {135^{\rm{o}}}$
TV	$1$	$\frac{1}{{24}} \times {360^{\rm{o}}} = {15^{\rm{o}}}$
Other	$2$	$\frac{2}{{24}} \times {360^{\rm{o}}} = {30^{\rm{o}}}$
Total	$24$	${360^{\rm{o}}}$

By knowing the central angle, a pie chart is drawn,

Q.5. Draw a frequency polygon for the following data using a histogram.

	$140-145$	$145-150$	$150-155$	$155-160$	$160-165$	$165-170$	$170-175$
	$35$	$40$	$55$	$50$	$40$	$35$	$20$

Ans: To draw a frequency polygon, we take the imagined classes $135-140$ at the beginning and $175-180$ at the end, each with frequency zero. The following is the frequency table tabulated for the given data


$140-145$	$142.5$	$35$
$145-150$	$147.5$	$40$
$150-155$	$152.5$	$55$
$155-160$	$157.5$	$50$
$160-165$	$162.5$	$40$
$165-170$	$167.5$	$35$
$170-175$	$172.5$	$20$

Let’s mark the class intervals along the $x-$axis and the frequency along the $y-$axis.

Using the above table, plot the points on the histogram: $(137.5, 0), (142.5, 35), (147.5, 40), (152.5, 55), (157.5, 50), (162.5, 40),$ $(167.5, 35), (172.5, 20)$ and $(177.5, 0).$

We join these points one after the other to obtain the required frequency polygon.

In this article, we have studied the details of the graphical representation of data. We learnt the meaning, uses, and advantages of using graphs . Then we studied the different types of graphs with examples. Lastly, we solved examples to help students understand the concept in a better way.

Frequently Asked Questions (FAQs) on Basic Graphical Representation

Q.1: What are graphical representations? Ans: Graphical representations represent given data using charts or graphs numerically and then visually analyse and interpret the information.

Q.2: What are the 6 types of graphs used? Ans: The following are the types of graphs we use commonly: 1. Line graph 2. Histogram 3. Bar graph 4. Pie chart 5. Frequency polygon 6. Ogives or cumulative frequency graphs

Q.3: What are the advantages of the graphical method? Ans: The advantages of using a graphical method are: 1. Facilitates improved learning 2. Knowing the content 3. Usage of flexibility 4. Increases thinking 5. Supports creative, personalised reports for more engaging and stimulating visual presentations 6. Better communication 7. It shows the whole picture

Q.4: What is the graphical representation of an idea? Ans: The graphical representations exhibit relationships between ideas, data, information and concepts in a visual graph or map. Graphical representations are effortless to acknowledge.

Q.5: How do you do frequency polygon? Ans: Frequency distribution is first obtained, and the midpoints of each class interval are found. Mark the midpoints along the $x-$axis and frequencies along the $y-$axis. Plot the points corresponding to the frequency. Join the points, using line segments in order.

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Graphical Representation

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Graphical representations encompass a wide variety of techniques that are used to clarify, interpret and analyze data by plotting points and drawing line segments, surfaces and other geometric forms or symbols.

The purpose of a graph is a rapid visualization of a data set. For instance, it should clearly illustrate the general behavior of the phenomenon investigated and highlight any important factors. It can be used, for example, as a means to translate or to complete a frequency table .

Therefore, graphical representation is a form of data representation.

The concept of plotting a point in coordinate space dates back to at least the ancient Greeks, but we had to wait until the work of Descartes, René for mathematicians to investigate this concept.

According to Royston, E. (1970), a German mathematician named Crome, A.W. was among the first to use graphical representation in statistics . He initially used it as a teaching tool.

In his works Geographisch-statistische Darstellung...

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Crome, A.F.W.: Ueber die Grösse und Bevölkerung der sämtlichen Europäischen Staaten. Weygand, Leipzig (1785)

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Crome, A.F.W.: Geographisch-statistische Darstellung der Staatskräfte. Weygand, Leipzig (1820)

Fienberg, S.E.: Graphical method in statistics. Am. Stat. 33 , 165–178 (1979)

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Guerry, A.M.: Essai sur la statistique morale de la France. Crochard, Paris (1833)

Playfair, W.: The Commercial and Political Atlas. Playfair, London (1786)

Royston, E.: A note on the history of the graphical presentation of data. In: Pearson, E.S., Kendall, M. (eds.) Studies in the History of Statistics and Probability, vol. I. Griffin, London (1970)

Schmid, C.F.: Handbook of Graphic Presentation. Ronald Press, New York (1954)

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(2008). Graphical Representation. In: The Concise Encyclopedia of Statistics. Springer, New York, NY. https://doi.org/10.1007/978-0-387-32833-1_174

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Graphs and Graphical Representation

What are Graphs and Graphical Representation?

Graphical representation refers to the use of charts and graphs to visually analyze and display, interpret numerical value, clarify the qualitative structures. The data is represented by a variety of symbols such as line charts, bars, circles, ratios. Through this, greater insight is stuck in the mind while analyzing the information.

Graphs can easily illustrate the behavior, highlight changes, and can study data points that may sometimes be overlooked. The type of data presentation depends upon the type of data being used.

Graphical Representation of Data

The graphical representation is simply a way of analyzing numerical data. It comprises a relation between data, information, and ideas in a diagram. Anything portrayed in a graphical manner is easy to understand and is also termed as the most important learning technique. The graphical presentation is always dependent on the type of information conveyed. There are different types of graphical representation. These are as follows:

Line Graphs:

Also denoted as linear graphs are used to examine continuous data and are also useful in predicting future events in time.

Histograms:

This graph uses bars to represent the information. The bars represent the frequency of numerical data. All intervals are equal and hence, the width of each bar is also equal.

Bar Graphs:

These are used to display the categories and compare the data using solid bars. These bars represent the quantities.

Frequency Table:

This table shows the frequency of data that falls within that given time interval.

Line Plot:

It shows the frequency of data on a given line number.

Circle Graph:

It is also known as a pie chart and shows the relationship between the parts of the whole. The circle consists of 100% and other parts shown are in different proportions.

Scatter Plot:

The diagram shows the relationship between two sets of data. Each dot represents individual information of the data.

Venn Diagram:

It consists of overlapping circles, each depicting a set. The inner-circle made is a graphical representation.

Stem and Leaf Plot:

The data is organized from the least value to the highest value. The digits of the least place value form the leaf and that of the highest place value form the stem.

Box and Whisker Plot:

The data is summarised by dividing it into four parts. Box and whisker show the spread and median of the data.

Graphical Presentation of Data - Definition

It is a way of analyzing numerical data. It is a sort of chart which shows statistical data in the form of lines or curves which are plotted on the surface. It enables studying the cause and effect relationships between two variables . It helps to measure the extent of change in one variable when another variable changes.

Principles of Graphical Representation

The variables in the graph are represented using two lines called coordinate axes. The horizontal and vertical axes are denoted by x and y respectively. Their point of intersection is called an origin ‘O’. Considering x-axes, the distance from the origin to the right will take a positive value, and the distance from the origin to the left will take a negative value. Taking the same procedure on y-axes. The points above origin will take the positive values and the points below origin will take negative values. As discussed in the earlier section about the types of graphical representation. There are four most widely used graphs namely histogram, pie diagram, frequency polygon, and ogive frequency graph.

Rules for Graphical Representation of Data

There are certain rules to effectively represent the information in graphical form. Certain rules are discussed below:

Title: One has to make sure that a suitable title is given to the graph which indicates the presentation subject.

Scale: It should be used efficiently to represent data in an accurate manner.

Measurement unit: It is used to calculate the distance between the box

Index: Differentiate appropriate colors, shades, and design I graph for a better understanding of the information conveyed.

Data sources: Include the source of information at the bottom graph wherever necessary. It adds to the authenticity of the information.

Keep it simple: Construct the graph in an easy to understand manner and keep it simple for the reader to understand. Looking at the graph the information portrayed is easily understandable.

Importance of Graphical Representation of Data

Some of the importance and advantages of using graphs to interpret data are listed below:

The graph is easiest to understand as the information portrayed is in facts and figures. Any information depicted in facts, figures, comparison grabs our attention, due to which they are memorizable for the long term.

It allows us to relate and compare data for different time periods.

It is used in statistics to determine the mean , mode, and median of different data.

It saves a lot of time as it covers most of the information in facts and figures. This in turn compacts the information.

FAQs on Graphs and Graphical Representation

Q1. State the Advantages and Disadvantages of Graphical Representation of Data?

Ans: These graphical presentations of data are vital components in analyzing the information. Data visualization is one of the most fundamental approaches to data representation. Its advantages include the following points:

Facilitates and improves learning

Flexibility of use

Understands content

Increase structure thinking

Supports creative thinking

Portrays the whole picture

Improves communication

With advantages, certain disadvantages are also linked to the graphical representation. The disadvantages concern the high cost of human effort, the process of selecting the most appropriate graphical and tabular presentation, creative thinking, greater design to interpret information, visualizing data, and as human resource is used. The potential for human bias plays a huge role.

Q2. What is the Graphical Representation of Data in Statistics?

Graphs are powerful data evaluation tools. They provide a quick visual summary of the information. In statistics information depicted is of mean, mode, and median. Box plots, histograms are used to depict the information. These graphs provide information about ranges, shapes, concentration, extreme values, etc. It studies information between different sets and trends whether increasing or decreasing. Since graphical methods are qualitative, they are not only the basis of comparison and information.

How to tell what you know well

What is a Graphical Representation

The proposed of graphical representations is to exhibit relationships between ideas, data , information and concepts in a visual map or diagram . Graphical representations are easy to understand. They can also be edited and shared. The type of graphical representations will depend on the type of information shown and learning domain. They are one of the most effective learning strategies.

A structured representation of information is closer to the workings of the human brain (structure of neurons) than other ways e.g. notes, highlights main ideas.

Advantages of graphical representations:

Facilitate and improve learning
Helps to understand content
Can be used in a variety of topics (lessons, articles, literature)
Increase structured thinking
Support creation of suggestive presentations
Helps to communicate
Useful for learning assessment and information evaluation
Increase creativity and helps to brainstorming

Types of graphical representations:

Concept Map
Info graphics

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17 Data Visualization Techniques All Professionals Should Know

17 Sep 2019

There’s a growing demand for business analytics and data expertise in the workforce. But you don’t need to be a professional analyst to benefit from data-related skills.

Becoming skilled at common data visualization techniques can help you reap the rewards of data-driven decision-making , including increased confidence and potential cost savings. Learning how to effectively visualize data could be the first step toward using data analytics and data science to your advantage to add value to your organization.

Several data visualization techniques can help you become more effective in your role. Here are 17 essential data visualization techniques all professionals should know, as well as tips to help you effectively present your data.

Access your free e-book today.

What Is Data Visualization?

Data visualization is the process of creating graphical representations of information. This process helps the presenter communicate data in a way that’s easy for the viewer to interpret and draw conclusions.

There are many different techniques and tools you can leverage to visualize data, so you want to know which ones to use and when. Here are some of the most important data visualization techniques all professionals should know.

Data Visualization Techniques

The type of data visualization technique you leverage will vary based on the type of data you’re working with, in addition to the story you’re telling with your data .

Here are some important data visualization techniques to know:

Gantt Chart
Box and Whisker Plot
Waterfall Chart
Scatter Plot
Pictogram Chart
Highlight Table
Bullet Graph
Choropleth Map
Network Diagram
Correlation Matrices

1. Pie Chart

Pie charts are one of the most common and basic data visualization techniques, used across a wide range of applications. Pie charts are ideal for illustrating proportions, or part-to-whole comparisons.

Because pie charts are relatively simple and easy to read, they’re best suited for audiences who might be unfamiliar with the information or are only interested in the key takeaways. For viewers who require a more thorough explanation of the data, pie charts fall short in their ability to display complex information.

2. Bar Chart

The classic bar chart , or bar graph, is another common and easy-to-use method of data visualization. In this type of visualization, one axis of the chart shows the categories being compared, and the other, a measured value. The length of the bar indicates how each group measures according to the value.

One drawback is that labeling and clarity can become problematic when there are too many categories included. Like pie charts, they can also be too simple for more complex data sets.

3. Histogram

Unlike bar charts, histograms illustrate the distribution of data over a continuous interval or defined period. These visualizations are helpful in identifying where values are concentrated, as well as where there are gaps or unusual values.

Histograms are especially useful for showing the frequency of a particular occurrence. For instance, if you’d like to show how many clicks your website received each day over the last week, you can use a histogram. From this visualization, you can quickly determine which days your website saw the greatest and fewest number of clicks.

4. Gantt Chart

Gantt charts are particularly common in project management, as they’re useful in illustrating a project timeline or progression of tasks. In this type of chart, tasks to be performed are listed on the vertical axis and time intervals on the horizontal axis. Horizontal bars in the body of the chart represent the duration of each activity.

Utilizing Gantt charts to display timelines can be incredibly helpful, and enable team members to keep track of every aspect of a project. Even if you’re not a project management professional, familiarizing yourself with Gantt charts can help you stay organized.

5. Heat Map

A heat map is a type of visualization used to show differences in data through variations in color. These charts use color to communicate values in a way that makes it easy for the viewer to quickly identify trends. Having a clear legend is necessary in order for a user to successfully read and interpret a heatmap.

There are many possible applications of heat maps. For example, if you want to analyze which time of day a retail store makes the most sales, you can use a heat map that shows the day of the week on the vertical axis and time of day on the horizontal axis. Then, by shading in the matrix with colors that correspond to the number of sales at each time of day, you can identify trends in the data that allow you to determine the exact times your store experiences the most sales.

6. A Box and Whisker Plot

A box and whisker plot , or box plot, provides a visual summary of data through its quartiles. First, a box is drawn from the first quartile to the third of the data set. A line within the box represents the median. “Whiskers,” or lines, are then drawn extending from the box to the minimum (lower extreme) and maximum (upper extreme). Outliers are represented by individual points that are in-line with the whiskers.

This type of chart is helpful in quickly identifying whether or not the data is symmetrical or skewed, as well as providing a visual summary of the data set that can be easily interpreted.

7. Waterfall Chart

A waterfall chart is a visual representation that illustrates how a value changes as it’s influenced by different factors, such as time. The main goal of this chart is to show the viewer how a value has grown or declined over a defined period. For example, waterfall charts are popular for showing spending or earnings over time.

8. Area Chart

An area chart , or area graph, is a variation on a basic line graph in which the area underneath the line is shaded to represent the total value of each data point. When several data series must be compared on the same graph, stacked area charts are used.

This method of data visualization is useful for showing changes in one or more quantities over time, as well as showing how each quantity combines to make up the whole. Stacked area charts are effective in showing part-to-whole comparisons.

9. Scatter Plot

Another technique commonly used to display data is a scatter plot . A scatter plot displays data for two variables as represented by points plotted against the horizontal and vertical axis. This type of data visualization is useful in illustrating the relationships that exist between variables and can be used to identify trends or correlations in data.

Scatter plots are most effective for fairly large data sets, since it’s often easier to identify trends when there are more data points present. Additionally, the closer the data points are grouped together, the stronger the correlation or trend tends to be.

10. Pictogram Chart

Pictogram charts , or pictograph charts, are particularly useful for presenting simple data in a more visual and engaging way. These charts use icons to visualize data, with each icon representing a different value or category. For example, data about time might be represented by icons of clocks or watches. Each icon can correspond to either a single unit or a set number of units (for example, each icon represents 100 units).

In addition to making the data more engaging, pictogram charts are helpful in situations where language or cultural differences might be a barrier to the audience’s understanding of the data.

11. Timeline

Timelines are the most effective way to visualize a sequence of events in chronological order. They’re typically linear, with key events outlined along the axis. Timelines are used to communicate time-related information and display historical data.

Timelines allow you to highlight the most important events that occurred, or need to occur in the future, and make it easy for the viewer to identify any patterns appearing within the selected time period. While timelines are often relatively simple linear visualizations, they can be made more visually appealing by adding images, colors, fonts, and decorative shapes.

12. Highlight Table

A highlight table is a more engaging alternative to traditional tables. By highlighting cells in the table with color, you can make it easier for viewers to quickly spot trends and patterns in the data. These visualizations are useful for comparing categorical data.

Depending on the data visualization tool you’re using, you may be able to add conditional formatting rules to the table that automatically color cells that meet specified conditions. For instance, when using a highlight table to visualize a company’s sales data, you may color cells red if the sales data is below the goal, or green if sales were above the goal. Unlike a heat map, the colors in a highlight table are discrete and represent a single meaning or value.

13. Bullet Graph

A bullet graph is a variation of a bar graph that can act as an alternative to dashboard gauges to represent performance data. The main use for a bullet graph is to inform the viewer of how a business is performing in comparison to benchmarks that are in place for key business metrics.

In a bullet graph, the darker horizontal bar in the middle of the chart represents the actual value, while the vertical line represents a comparative value, or target. If the horizontal bar passes the vertical line, the target for that metric has been surpassed. Additionally, the segmented colored sections behind the horizontal bar represent range scores, such as “poor,” “fair,” or “good.”

14. Choropleth Maps

A choropleth map uses color, shading, and other patterns to visualize numerical values across geographic regions. These visualizations use a progression of color (or shading) on a spectrum to distinguish high values from low.

Choropleth maps allow viewers to see how a variable changes from one region to the next. A potential downside to this type of visualization is that the exact numerical values aren’t easily accessible because the colors represent a range of values. Some data visualization tools, however, allow you to add interactivity to your map so the exact values are accessible.

15. Word Cloud

A word cloud , or tag cloud, is a visual representation of text data in which the size of the word is proportional to its frequency. The more often a specific word appears in a dataset, the larger it appears in the visualization. In addition to size, words often appear bolder or follow a specific color scheme depending on their frequency.

Word clouds are often used on websites and blogs to identify significant keywords and compare differences in textual data between two sources. They are also useful when analyzing qualitative datasets, such as the specific words consumers used to describe a product.

16. Network Diagram

Network diagrams are a type of data visualization that represent relationships between qualitative data points. These visualizations are composed of nodes and links, also called edges. Nodes are singular data points that are connected to other nodes through edges, which show the relationship between multiple nodes.

There are many use cases for network diagrams, including depicting social networks, highlighting the relationships between employees at an organization, or visualizing product sales across geographic regions.

17. Correlation Matrix

A correlation matrix is a table that shows correlation coefficients between variables. Each cell represents the relationship between two variables, and a color scale is used to communicate whether the variables are correlated and to what extent.

Correlation matrices are useful to summarize and find patterns in large data sets. In business, a correlation matrix might be used to analyze how different data points about a specific product might be related, such as price, advertising spend, launch date, etc.

Other Data Visualization Options

While the examples listed above are some of the most commonly used techniques, there are many other ways you can visualize data to become a more effective communicator. Some other data visualization options include:

Bubble clouds
Circle views
Dendrograms
Dot distribution maps
Open-high-low-close charts
Polar areas
Radial trees
Ring Charts
Sankey diagram
Span charts
Streamgraphs
Wedge stack graphs
Violin plots

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Tips For Creating Effective Visualizations

Creating effective data visualizations requires more than just knowing how to choose the best technique for your needs. There are several considerations you should take into account to maximize your effectiveness when it comes to presenting data.

Related : What to Keep in Mind When Creating Data Visualizations in Excel

One of the most important steps is to evaluate your audience. For example, if you’re presenting financial data to a team that works in an unrelated department, you’ll want to choose a fairly simple illustration. On the other hand, if you’re presenting financial data to a team of finance experts, it’s likely you can safely include more complex information.

Another helpful tip is to avoid unnecessary distractions. Although visual elements like animation can be a great way to add interest, they can also distract from the key points the illustration is trying to convey and hinder the viewer’s ability to quickly understand the information.

Finally, be mindful of the colors you utilize, as well as your overall design. While it’s important that your graphs or charts are visually appealing, there are more practical reasons you might choose one color palette over another. For instance, using low contrast colors can make it difficult for your audience to discern differences between data points. Using colors that are too bold, however, can make the illustration overwhelming or distracting for the viewer.

Related : Bad Data Visualization: 5 Examples of Misleading Data

Visuals to Interpret and Share Information

No matter your role or title within an organization, data visualization is a skill that’s important for all professionals. Being able to effectively present complex data through easy-to-understand visual representations is invaluable when it comes to communicating information with members both inside and outside your business.

There’s no shortage in how data visualization can be applied in the real world. Data is playing an increasingly important role in the marketplace today, and data literacy is the first step in understanding how analytics can be used in business.

Are you interested in improving your analytical skills? Learn more about Business Analytics , our eight-week online course that can help you use data to generate insights and tackle business decisions.

This post was updated on January 20, 2022. It was originally published on September 17, 2019.

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Illustration with collage of pictograms of clouds, pie chart, graph pictograms on the following

Data visualization is the representation of data through use of common graphics, such as charts, plots, infographics and even animations. These visual displays of information communicate complex data relationships and data-driven insights in a way that is easy to understand.

Data visualization can be utilized for a variety of purposes, and it’s important to note that is not only reserved for use by data teams. Management also leverages it to convey organizational structure and hierarchy while data analysts and data scientists use it to discover and explain patterns and trends. Harvard Business Review (link resides outside ibm.com) categorizes data visualization into four key purposes: idea generation, idea illustration, visual discovery, and everyday dataviz. We’ll delve deeper into these below:

Idea generation

Data visualization is commonly used to spur idea generation across teams. They are frequently leveraged during brainstorming or Design Thinking sessions at the start of a project by supporting the collection of different perspectives and highlighting the common concerns of the collective. While these visualizations are usually unpolished and unrefined, they help set the foundation within the project to ensure that the team is aligned on the problem that they’re looking to address for key stakeholders.

Idea illustration

Data visualization for idea illustration assists in conveying an idea, such as a tactic or process. It is commonly used in learning settings, such as tutorials, certification courses, centers of excellence, but it can also be used to represent organization structures or processes, facilitating communication between the right individuals for specific tasks. Project managers frequently use Gantt charts and waterfall charts to illustrate workflows . Data modeling also uses abstraction to represent and better understand data flow within an enterprise’s information system, making it easier for developers, business analysts, data architects, and others to understand the relationships in a database or data warehouse.

Visual discovery

Visual discovery and every day data viz are more closely aligned with data teams. While visual discovery helps data analysts, data scientists, and other data professionals identify patterns and trends within a dataset, every day data viz supports the subsequent storytelling after a new insight has been found.

Data visualization

Data visualization is a critical step in the data science process, helping teams and individuals convey data more effectively to colleagues and decision makers. Teams that manage reporting systems typically leverage defined template views to monitor performance. However, data visualization isn’t limited to performance dashboards. For example, while text mining an analyst may use a word cloud to to capture key concepts, trends, and hidden relationships within this unstructured data. Alternatively, they may utilize a graph structure to illustrate relationships between entities in a knowledge graph. There are a number of ways to represent different types of data, and it’s important to remember that it is a skillset that should extend beyond your core analytics team.

Use this model selection framework to choose the most appropriate model while balancing your performance requirements with cost, risks and deployment needs.

The earliest form of data visualization can be traced back the Egyptians in the pre-17th century, largely used to assist in navigation. As time progressed, people leveraged data visualizations for broader applications, such as in economic, social, health disciplines. Perhaps most notably, Edward Tufte published The Visual Display of Quantitative Information (link resides outside ibm.com), which illustrated that individuals could utilize data visualization to present data in a more effective manner. His book continues to stand the test of time, especially as companies turn to dashboards to report their performance metrics in real-time. Dashboards are effective data visualization tools for tracking and visualizing data from multiple data sources, providing visibility into the effects of specific behaviors by a team or an adjacent one on performance. Dashboards include common visualization techniques, such as:

Tables: This consists of rows and columns used to compare variables. Tables can show a great deal of information in a structured way, but they can also overwhelm users that are simply looking for high-level trends.
Pie charts and stacked bar charts: These graphs are divided into sections that represent parts of a whole. They provide a simple way to organize data and compare the size of each component to one other.
Line charts and area charts: These visuals show change in one or more quantities by plotting a series of data points over time and are frequently used within predictive analytics. Line graphs utilize lines to demonstrate these changes while area charts connect data points with line segments, stacking variables on top of one another and using color to distinguish between variables.
Histograms: This graph plots a distribution of numbers using a bar chart (with no spaces between the bars), representing the quantity of data that falls within a particular range. This visual makes it easy for an end user to identify outliers within a given dataset.
Scatter plots: These visuals are beneficial in reveling the relationship between two variables, and they are commonly used within regression data analysis. However, these can sometimes be confused with bubble charts, which are used to visualize three variables via the x-axis, the y-axis, and the size of the bubble.
Heat maps: These graphical representation displays are helpful in visualizing behavioral data by location. This can be a location on a map, or even a webpage.
Tree maps, which display hierarchical data as a set of nested shapes, typically rectangles. Treemaps are great for comparing the proportions between categories via their area size.

Access to data visualization tools has never been easier. Open source libraries, such as D3.js, provide a way for analysts to present data in an interactive way, allowing them to engage a broader audience with new data. Some of the most popular open source visualization libraries include:

D3.js: It is a front-end JavaScript library for producing dynamic, interactive data visualizations in web browsers. D3.js (link resides outside ibm.com) uses HTML, CSS, and SVG to create visual representations of data that can be viewed on any browser. It also provides features for interactions and animations.
ECharts: A powerful charting and visualization library that offers an easy way to add intuitive, interactive, and highly customizable charts to products, research papers, presentations, etc. Echarts (link resides outside ibm.com) is based in JavaScript and ZRender, a lightweight canvas library.
Vega: Vega (link resides outside ibm.com) defines itself as “visualization grammar,” providing support to customize visualizations across large datasets which are accessible from the web.
deck.gl: It is part of Uber's open source visualization framework suite. deck.gl (link resides outside ibm.com) is a framework, which is used for exploratory data analysis on big data. It helps build high-performance GPU-powered visualization on the web.

With so many data visualization tools readily available, there has also been a rise in ineffective information visualization. Visual communication should be simple and deliberate to ensure that your data visualization helps your target audience arrive at your intended insight or conclusion. The following best practices can help ensure your data visualization is useful and clear:

Set the context: It’s important to provide general background information to ground the audience around why this particular data point is important. For example, if e-mail open rates were underperforming, we may want to illustrate how a company’s open rate compares to the overall industry, demonstrating that the company has a problem within this marketing channel. To drive an action, the audience needs to understand how current performance compares to something tangible, like a goal, benchmark, or other key performance indicators (KPIs).

Know your audience(s): Think about who your visualization is designed for and then make sure your data visualization fits their needs. What is that person trying to accomplish? What kind of questions do they care about? Does your visualization address their concerns? You’ll want the data that you provide to motivate people to act within their scope of their role. If you’re unsure if the visualization is clear, present it to one or two people within your target audience to get feedback, allowing you to make additional edits prior to a large presentation.

Choose an effective visual: Specific visuals are designed for specific types of datasets. For instance, scatter plots display the relationship between two variables well, while line graphs display time series data well. Ensure that the visual actually assists the audience in understanding your main takeaway. Misalignment of charts and data can result in the opposite, confusing your audience further versus providing clarity.

Keep it simple: Data visualization tools can make it easy to add all sorts of information to your visual. However, just because you can, it doesn’t mean that you should! In data visualization, you want to be very deliberate about the additional information that you add to focus user attention. For example, do you need data labels on every bar in your bar chart? Perhaps you only need one or two to help illustrate your point. Do you need a variety of colors to communicate your idea? Are you using colors that are accessible to a wide range of audiences (e.g. accounting for color blind audiences)? Design your data visualization for maximum impact by eliminating information that may distract your target audience.

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Graph and its representations

What is graph data structure.

A Graph is a non-linear data structure consisting of vertices and edges. The vertices are sometimes also referred to as nodes and the edges are lines or arcs that connect any two nodes in the graph. More formally a Graph is composed of a set of vertices( V ) and a set of edges( E ). The graph is denoted by G(V, E) .

Representations of Graph

Here are the two most common ways to represent a graph : For simplicity, we are going to consider only unweighted graphs in this post.

Adjacency Matrix

Adjacency list.

An adjacency matrix is a way of representing a graph as a matrix of boolean (0’s and 1’s)

Let’s assume there are n vertices in the graph So, create a 2D matrix adjMat[n][n] having dimension n x n.

If there is an edge from vertex i to j , mark adjMat[i][j] as 1 . If there is no edge from vertex i to j , mark adjMat[i][j] as 0 .

Representation of Undirected Graph as Adjacency Matrix:

The below figure shows an undirected graph. Initially, the entire Matrix is initialized to 0 . If there is an edge from source to destination, we insert 1 to both cases ( adjMat[destination] and adjMat [ destination]) because we can go either way.

Undirected Graph to Adjacency Matrix

Representation of Directed Graph as Adjacency Matrix:

The below figure shows a directed graph. Initially, the entire Matrix is initialized to 0 . If there is an edge from source to destination, we insert 1 for that particular adjMat[destination] .

Directed Graph to Adjacency Matrix

An array of Lists is used to store edges between two vertices. The size of array is equal to the number of vertices (i.e, n) . Each index in this array represents a specific vertex in the graph. The entry at the index i of the array contains a linked list containing the vertices that are adjacent to vertex i .

Let’s assume there are n vertices in the graph So, create an array of list of size n as adjList[n].

adjList[0] will have all the nodes which are connected (neighbour) to vertex 0 . adjList[1] will have all the nodes which are connected (neighbour) to vertex 1 and so on.

Representation of Undirected Graph as Adjacency list:

The below undirected graph has 3 vertices. So, an array of list will be created of size 3, where each indices represent the vertices. Now, vertex 0 has two neighbours (i.e, 1 and 2). So, insert vertex 1 and 2 at indices 0 of array. Similarly, For vertex 1, it has two neighbour (i.e, 2 and 0) So, insert vertices 2 and 0 at indices 1 of array. Similarly, for vertex 2, insert its neighbours in array of list.

Undirected Graph to Adjacency list

Representation of Directed Graph as Adjacency list:

The below directed graph has 3 vertices. So, an array of list will be created of size 3, where each indices represent the vertices. Now, vertex 0 has no neighbours. For vertex 1, it has two neighbour (i.e, 0 and 2) So, insert vertices 0 and 2 at indices 1 of array. Similarly, for vertex 2, insert its neighbours in array of list.

Directed Graph to Adjacency list

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Computer Science > Machine Learning

Title: debiasing graph representation learning based on information bottleneck.

Abstract: Graph representation learning has shown superior performance in numerous real-world applications, such as finance and social networks. Nevertheless, most existing works might make discriminatory predictions due to insufficient attention to fairness in their decision-making processes. This oversight has prompted a growing focus on fair representation learning. Among recent explorations on fair representation learning, prior works based on adversarial learning usually induce unstable or counterproductive performance. To achieve fairness in a stable manner, we present the design and implementation of GRAFair, a new framework based on a variational graph auto-encoder. The crux of GRAFair is the Conditional Fairness Bottleneck, where the objective is to capture the trade-off between the utility of representations and sensitive information of interest. By applying variational approximation, we can make the optimization objective tractable. Particularly, GRAFair can be trained to produce informative representations of tasks while containing little sensitive information without adversarial training. Experiments on various real-world datasets demonstrate the effectiveness of our proposed method in terms of fairness, utility, robustness, and stability.

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DOI: 10.1109/SP54263.2024.00139
Corpus ID: 272431594

Flash: A Comprehensive Approach to Intrusion Detection via Provenance Graph Representation Learning

Mati Ur Rehman , Hadi Ahmadi , Wajih Ul Hassan
Published in IEEE Symposium on Security… 19 May 2024
Computer Science, Engineering

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Incorporating gradients to rules: towards lightweight, adaptive provenance-based intrusion detection.

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From sands to mansions: enabling automatic full-life-cycle cyberattack construction with llm, 75 references, sometimes, you aren't what you do: mimicry attacks against provenance graph host intrusion detection systems.

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	\(0-20\)	\(20-40\)	\(40-60\)	\(60-80\)	\(80-100\)
	\(5\)	\(10\)	\(40\)	\(45\)	\(20\)


	\(240\)	\(400\)	\(440\)	\(320\)	\(200\)


	\(3\)	\(7\)	\(2\)	\(9\)	\(1\)	\(2\)


Household	\(3\)	\(\frac{3}{{24}} \times {360^{\rm{o}}} = {45^{\rm{o}}}\)
Sleep	\(7\)	\(\frac{7}{{24}} \times {360^{\rm{o}}} = {105^{\rm{o}}}\)
Cooking	\(2\)	\(\frac{2}{{24}} \times {360^{\rm{o}}} = {30^{\rm{o}}}\)
Office	\(9\)	\(\frac{9}{{24}} \times {360^{\rm{o}}} = {135^{\rm{o}}}\)
TV	\(1\)	\(\frac{1}{{24}} \times {360^{\rm{o}}} = {15^{\rm{o}}}\)
Other	\(2\)	\(\frac{2}{{24}} \times {360^{\rm{o}}} = {30^{\rm{o}}}\)
Total	\(24\)	\({360^{\rm{o}}}\)

	\(140-145\)	\(145-150\)	\(150-155\)	\(155-160\)	\(160-165\)	\(165-170\)	\(170-175\)
	\(35\)	\(40\)	\(55\)	\(50\)	\(40\)	\(35\)	\(20\)

Graphical Representation

General Rules for Graphical Representation of Data

Graphical Representation in Maths

Principles of Graphical Representation

Merits of Using Graphs

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"There is a magic in graphs."

A good example of this is "Anscombe's quartet", four datasets that share the same descriptive statistics, including mean, variance, and correlation.

Upon visual inspection, it becomes immediately clear that these datasets, while seemingly identical according to common summary statistics, are each unique. This is the power of effective data visualization: it allows us to bypass cognition by communicating directly with our perceptual system.

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2. Line Charts:

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1 Million Means: 1 Million in Rupees, Lakhs and Crores

Ways To Improve Learning Outcomes: Learn Tips & Tricks

The Three States of Matter: Solids, Liquids, and Gases

Types of Motion: Introduction, Parameters, Examples

Understanding Frequency Polygon: Detailed Explanation

Uses of Silica Gel in Packaging?