CAPTION
Height (cm)
Weight (kg)
Age (Years)
STUB
BODY OF THE TABLE
* Sources: 1. Kailasha Foundation – Fun & Learn Portal LMS Directory *Footnotes: The entire upper part of the table is called BOX HEAD.
3. Diagrammatic Mode of Presentation:
A. Non-Frequency Diagrams: Non-frequency diagrams correspond to the data which are NOT frequency data. (a) Bar Diagrams (b) Line Diagrams (Historiagram) (c) Pie Diagram or Pie Chart
B. Frequency Diagrams: Frequency Data are presented. Mostly class-intervals are presented via this mode. Three most common frequency diagrams are: (a) Histogram (b) Frequency Polygon (c) Ogives: (i) Less than type Ogives (ii) More than type Ogives
Bar Diagrams:
Line Diagram:
Multiple Bar Diagram:
Frequency Polygon:
A smooth join of all vertices of a frequency polygon. This is broadly divided into four shapes:
(i) Bell Shaped (Most Common Shape) (ii) U-Shaped (iii) J – Shaped: Simple J – shaped & Inverted J – Shaped (iv) Mixed Curve (Second Most Common Shape)
Hindi explanation:.
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Statistics deals with the collection, presentation and analysis of the data, as well as drawing meaningful conclusions from the given data. Generally, the data can be classified into two different types, namely primary data and secondary data. If the information is collected by the investigator with a definite objective in their mind, then the data obtained is called the primary data. If the information is gathered from a source, which already had the information stored, then the data obtained is called secondary data. Once the data is collected, the presentation of data plays a major role in concluding the result. Here, we will discuss how to present the data with many solved examples.
As soon as the data collection is over, the investigator needs to find a way of presenting the data in a meaningful, efficient and easily understood way to identify the main features of the data at a glance using a suitable presentation method. Generally, the data in the statistics can be presented in three different forms, such as textual method, tabular method and graphical method.
Now, let us discuss how to present the data in a meaningful way with the help of examples.
Consider the marks given below, which are obtained by 10 students in Mathematics:
36, 55, 73, 95, 42, 60, 78, 25, 62, 75.
Find the range for the given data.
Given Data: 36, 55, 73, 95, 42, 60, 78, 25, 62, 75.
The data given is called the raw data.
First, arrange the data in the ascending order : 25, 36, 42, 55, 60, 62, 73, 75, 78, 95.
Therefore, the lowest mark is 25 and the highest mark is 95.
We know that the range of the data is the difference between the highest and the lowest value in the dataset.
Therefore, Range = 95-25 = 70.
Note: Presentation of data in ascending or descending order can be time-consuming if we have a larger number of observations in an experiment.
Now, let us discuss how to present the data if we have a comparatively more number of observations in an experiment.
Consider the marks obtained by 30 students in Mathematics subject (out of 100 marks)
10, 20, 36, 92, 95, 40, 50, 56, 60, 70, 92, 88, 80, 70, 72, 70, 36, 40, 36, 40, 92, 40, 50, 50, 56, 60, 70, 60, 60, 88.
In this example, the number of observations is larger compared to example 1. So, the presentation of data in ascending or descending order is a bit time-consuming. Hence, we can go for the method called ungrouped frequency distribution table or simply frequency distribution table . In this method, we can arrange the data in tabular form in terms of frequency.
For example, 3 students scored 50 marks. Hence, the frequency of 50 marks is 3. Now, let us construct the frequency distribution table for the given data.
Therefore, the presentation of data is given as below:
| |
---|---|
10 | 1 |
20 | 1 |
36 | 3 |
40 | 4 |
50 | 3 |
56 | 2 |
60 | 4 |
70 | 4 |
72 | 1 |
80 | 1 |
88 | 2 |
92 | 3 |
95 | 1 |
|
|
The following example shows the presentation of data for the larger number of observations in an experiment.
Consider the marks obtained by 100 students in a Mathematics subject (out of 100 marks)
95, 67, 28, 32, 65, 65, 69, 33, 98, 96,76, 42, 32, 38, 42, 40, 40, 69, 95, 92, 75, 83, 76, 83, 85, 62, 37, 65, 63, 42, 89, 65, 73, 81, 49, 52, 64, 76, 83, 92, 93, 68, 52, 79, 81, 83, 59, 82, 75, 82, 86, 90, 44, 62, 31, 36, 38, 42, 39, 83, 87, 56, 58, 23, 35, 76, 83, 85, 30, 68, 69, 83, 86, 43, 45, 39, 83, 75, 66, 83, 92, 75, 89, 66, 91, 27, 88, 89, 93, 42, 53, 69, 90, 55, 66, 49, 52, 83, 34, 36.
Now, we have 100 observations to present the data. In this case, we have more data when compared to example 1 and example 2. So, these data can be arranged in the tabular form called the grouped frequency table. Hence, we group the given data like 20-29, 30-39, 40-49, ….,90-99 (As our data is from 23 to 98). The grouping of data is called the “class interval” or “classes”, and the size of the class is called “class-size” or “class-width”.
In this case, the class size is 10. In each class, we have a lower-class limit and an upper-class limit. For example, if the class interval is 30-39, the lower-class limit is 30, and the upper-class limit is 39. Therefore, the least number in the class interval is called the lower-class limit and the greatest limit in the class interval is called upper-class limit.
Hence, the presentation of data in the grouped frequency table is given below:
| |
---|---|
20 – 29 | 3 |
30 – 39 | 14 |
40 – 49 | 12 |
50 – 59 | 8 |
60 – 69 | 18 |
70 – 79 | 10 |
80 – 89 | 23 |
90 – 99 | 12 |
|
|
Hence, the presentation of data in this form simplifies the data and it helps to enable the observer to understand the main feature of data at a glance.
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Methodology
Published on November 8, 2019 by Jack Caulfield . Revised on June 22, 2023.
Textual analysis is a broad term for various research methods used to describe, interpret and understand texts. All kinds of information can be gleaned from a text – from its literal meaning to the subtext, symbolism, assumptions, and values it reveals.
The methods used to conduct textual analysis depend on the field and the aims of the research. It often aims to connect the text to a broader social, political, cultural, or artistic context. Relatedly, it’s good to be careful of confirmation bias when conducting these sorts of analyses, grounding your observations in clear and plausible ways.
What is a text, textual analysis in cultural and media studies, textual analysis in the social sciences, textual analysis in literary studies, other interesting articles.
The term “text” is broader than it seems. A text can be a piece of writing, such as a book, an email, or a transcribed conversation. But in this context, a text can also be any object whose meaning and significance you want to interpret in depth: a film, an image, an artifact, even a place.
The methods you use to analyze a text will vary according to the type of object and the purpose of your analysis:
While textual analysis is most commonly applied to written language, bear in mind how broad the term “text” is and how varied the methods involved can be.
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In the fields of cultural studies and media studies, textual analysis is a key component of research. Researchers in these fields take media and cultural objects – for example, music videos, social media content, billboard advertising – and treat them as texts to be analyzed.
Usually working within a particular theoretical framework (for example, using postcolonial theory, media theory, or semiotics), researchers seek to connect elements of their texts with issues in contemporary politics and culture. They might analyze many different aspects of the text:
Textual analysis in this context is usually creative and qualitative in its approach. Researchers seek to illuminate something about the underlying politics or social context of the cultural object they’re investigating.
In the social sciences, textual analysis is often applied to texts such as interview transcripts and surveys , as well as to various types of media. Social scientists use textual data to draw empirical conclusions about social relations.
Textual analysis in the social sciences sometimes takes a more quantitative approach , where the features of texts are measured numerically. For example, a researcher might investigate how often certain words are repeated in social media posts, or which colors appear most prominently in advertisements for products targeted at different demographics.
Some common methods of analyzing texts in the social sciences include content analysis , thematic analysis , and discourse analysis .
Textual analysis is the most important method in literary studies. Almost all work in this field involves in-depth analysis of texts – in this context, usually novels, poems, stories or plays.
Because it deals with literary writing, this type of textual analysis places greater emphasis on the deliberately constructed elements of a text: for example, rhyme and meter in a poem, or narrative perspective in a novel. Researchers aim to understand and explain how these elements contribute to the text’s meaning.
However, literary analysis doesn’t just involve discovering the author’s intended meaning. It often also explores potentially unintended connections between different texts, asks what a text reveals about the context in which it was written, or seeks to analyze a classic text in a new and unexpected way.
Some well-known examples of literary analysis show the variety of approaches that can be taken:
If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.
Research bias
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Caulfield, J. (2023, June 22). Textual Analysis | Guide, 3 Approaches & Examples. Scribbr. Retrieved July 22, 2024, from https://www.scribbr.com/methodology/textual-analysis/
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Imagine the statistical data without a definite presentation, will be burdensome! Data presentation is one of the important aspects of Statistics. Presenting the data helps the users to study and explain the statistics thoroughly. We are going to discuss this presentation of data and know-how information is laid down methodically.
In this context, we are going to present the topic - Presentation of Data which is to be referred to by the students and the same is to be studied in regard to the types of presentations of data.
Statistics is all about data. Presenting data effectively and efficiently is an art. You may have uncovered many truths that are complex and need long explanations while writing. This is where the importance of the presentation of data comes in. You have to present your findings in such a way that the readers can go through them quickly and understand each and every point that you wanted to showcase. As time progressed and new and complex research started happening, people realized the importance of the presentation of data to make sense of the findings.
Data presentation is defined as the process of using various graphical formats to visually represent the relationship between two or more data sets so that an informed decision can be made based on them.
Broadly speaking, there are three methods of data presentation:
Diagrammatic
Out of the different methods of data presentation, this is the simplest one. You just write your findings in a coherent manner and your job is done. The demerit of this method is that one has to read the whole text to get a clear picture. Yes, the introduction, summary, and conclusion can help condense the information.
To avoid the complexities involved in the textual way of data presentation, people use tables and charts to present data. In this method, data is presented in rows and columns - just like you see in a cricket match showing who made how many runs. Each row and column have an attribute (name, year, sex, age, and other things like these). It is against these attributes that data is written within a cell.
This kind of data presentation and analysis method says a lot with dramatically short amounts of time.
Diagrammatic Presentation has been divided into further categories:
Geometric Diagram
When a Diagrammatic presentation involves shapes like a bar or circle, we call that a Geometric Diagram. Examples of Geometric Diagram
Bar Diagram
Simple Bar Diagram
Simple Bar Diagram is composed of rectangular bars. All of these bars have the same width and are placed at an equal distance from each other. The bars are placed on the X-axis. The height or length of the bars is used as the means of measurement. So, on the Y-axis, you have the measurement relevant to the data.
Suppose, you want to present the run scored by each batsman in a game in the form of a bar chart. Mark the runs on the Y-axis - in ascending order from the bottom. So, the lowest scorer will be represented in the form of the smallest bar and the highest scorer in the form of the longest bar.
(Image will be uploaded soon)
In many states of India, electric bills have bar diagrams showing the consumption in the last 5 months. Along with these bars, they also have bars that show the consumption that happened in the same months of the previous year. This kind of Bar Diagram is called Multiple Bar Diagrams.
(image will be uploaded soon)
Sometimes, a bar is divided into two or more parts. For example, if there is a Bar Diagram, the bars of which show the percentage of male voters who voted and who didn’t and the female voters who voted and who didn’t. Instead of creating separate bars for who did and who did not, you can divide one bar into who did and who did not.
A pie chart is a chart where you divide a pie (a circle) into different parts based on the data. Each of the data is first transformed into a percentage and then that percentage figure is multiplied by 3.6 degrees. The result that you get is the angular degree of that corresponding data to be drawn in the pie chart. So, for example, you get 30 degrees as the result, on the pie chart you draw that angle from the center.
Suppose you want to present data that shows how many students have 1 to 2 pens, how many have 3 to 5 pens, how many have 6 to 10 pens (grouped frequency) you do that with the help of a Frequency Diagram. A Frequency Diagram can be of many kinds:
Where the grouped frequency of pens (from the above example) is written on the X-axis and the numbers of students are marked on the Y-axis. The data is presented in the form of bars.
When you join the midpoints of the upper side of the rectangles in a histogram, you get a Frequency Polygon
When you draw a freehand line that passes through the points of the Frequency Polygon, you get a Frequency Curve.
Suppose 2 students got 0-20 marks in maths, 5 students got 20-30 marks and 4 students got 30-50 marks in Maths. So how many students got less than 50 marks? Yes, 5+2=7. And how many students got more than 20 marks? 5+4=9. This type of more than and less than data are represented in the form of the ogive. The meeting point of the less than and more than line will give you the Median.
If you want to see the trend of Corona infection vs the number of recoveries from January 2020 to December 2020, you can do that in the form of an Arithmetic Line Graph. The months should be marked on the X-axis and the number of infections and recoveries are marked on the Y-axis. You can compare if the recovery is greater than the infection and if the recovery and infection are going at the same rate or not with the help of this Diagram.
Sir Ronald Aylmer Fisher is known as the father of modern statistics.
1. What are the 4 types of Tabular Presentation?
The tabular presentation method can be further divided into 4 categories:
Qualitative
Quantitative
Qualitative classification is done when the attributes in the table are some kind of ‘quality’ or feature. Suppose you want to make a table where you would show how many batsmen made half-centuries and how many batsmen made centuries in IPL 2020. Notice that the data would have only numbers - no age, sex, height is needed. This type of tabulation is called quantitative tabulation.
If you want to make a table that would inform which year’s world cup, which team won. The classifying variable, here, is year or time. This kind of classification is called Temporal classification.
If you want to list the top 5 coldest places in the world. The classifying variable here would be a place in each case. This kind of classification is called Spatial Classification.
2. Are bar charts and histograms the Same?
No, they are not the same. With a histogram, you measure the frequency of quantitative data. With bar charts, you compare categorical data.
3. What is the definition of Data Presentation?
When research work is completed, the data gathered from it can be quite large and complex. Organizing the data in a coherent, easy-to-understand, quick to read and graphical way is called data presentation.
Manually analyzing hundreds of unstructured text-based data sources is tedious and time-consuming. What's worse is that since it's in text format, you know this process can be made easier, but you're unsure how to do it.
If you’re in a data-driven company that relies on such data sources to make critical decisions to optimize and improve your customer experience processes, keep reading.
“The syndrome of Data-Rich Information-Poor (DRIP) is often encountered when a business tracks a lot of Key Performance Indicators (KPIs), and so, in theory, they have a lot of data on the operations of the business, but in practice, this KPI tracking does not lead to sustainable process improvements.” says Daniel Shapiro , CTO & co-founder of Lemay.ai.
And he's right. Too often, we get stuck in the DRIP syndrome where we're happily generating data—but can't generate meaningful insights from them. Also, when you consider that 80% to 90% of data is unstructured , there’s so much potential waiting to be unlocked.
In this article, we’ll discuss the concept of textual data and how you can use it to extract valuable insights for your customer support operations.
Textual data is information that is stored and written in a text format. It can be anything from emails to blog posts to social media posts and online forum comments. In short, it's any data that has been expressed in words.
As more people communicate online, the amount of textual data available grows exponentially—offering unprecedented opportunities for businesses and researchers alike. For instance, a customer support agent can extract real-time insights into their customers' wants by analyzing email tickets, chatbot conversations, and social media feedback. They have to process using a machine learning-powered (ML) tool that can do this for them.
Gartner defines dark data as "Information assets organizations collect, process and store during regular business activities, but generally fail to use for other purposes (for example, analytics, business relationships, and direct monetizing)."
Drawing a parallel from dark matter in Physics, it means that there's a lot of untapped potential in this type of data—but organizations fail to realize it. It usually happens when they don't have the resources to analyze this type of unstructured data.
But how does textual data fit into this? Here are a few examples:
Here are a few ways to analyze and extract critical insights from textual data:
Textual data visualization is the process of representing text data visually. It makes it easier to find patterns, trends, and similarities between different pieces of text. It uses topic modeling, linguistic analysis, and sentiment analysis techniques to analyze the data and create visual representations depending on the context.
For instance, Dashbot's Conversational Data Cloud helps you analyze thousands of customer conversations to discover how your customers feel about your product, what prompted them to purchase, and what issues they face. Using that data, you can avoid problems while visualizing the entire customer journey in one place.
Identify important or trending topics from email tickets.
Tools like Dashbot can use textual data such as email tickets to track customer trends, issues, and inquiries. Doing this lets you stay one step ahead of customer needs and problems by quickly identifying trending topics. Plus, it gives customers the impression that their needs are being taken seriously, resulting in increased satisfaction and brand loyalty.
You can also use Dashbot to understand customers' feelings and deal with negative conversations more strategically. For instance, if many customers are having an issue with a specific feature, the software will highlight that and the sentiment associated with it. Based on that, you can create specific resolution guides or allot more agents to tackle the incoming tickets. It also allows you to tailor the conversation in real-time and provide personalized solutions.
You can use sentiment analysis to understand your customers and can uncover critical issues to take action on them. You can do this via the Key Activities feature, where topics are categorized by source, type of conversation, resolution rate, and sentiment. It helps you train support agents with concrete data—resulting in improved customer service and experience.
Dashbot can keep you ahead of the curve with its sophisticated unstructured data extraction capabilities. This means it continuously monitors customer support data to determine customer issues in real time. It ensures your team won't get caught off guard—and they'll be prepared for any sudden increase in user inquiries.
Textual data helps organizations unlock their dark data insights , make better decisions, and gain real-time visibility. However, extracting valuable insights from textual data is a complex process. It requires advanced technological solutions such as AI and ML models to analyze it.
Additionally, employing textual data visualization tools ensures that findings are presented in easy-to-understand visualization charts and graphs so you can share them across departments or organizations. You can use a tool like Dashbot to analyze and visualize it in one dashboard—making it an invaluable asset for any organization with access to it.
Interested in seeing how you can use Dashbot to analyze textual data? Book a demo with us today!
Textual data refers to any information or message conveyed in written or printed form. It can include everything from books, articles, and surveys to social media posts, chat conversations, and emails. It is processed using NLP techniques that analyze the text's words, syntax, and context to extract meaningful insights.
Several ways to collect textual data include surveys, chat transcripts, and social media monitoring. Here's how they work:
An example of textual analysis is when a researcher analyzes written communication, such as books, magazines, or websites, to identify themes and patterns in the text. For instance, a tool like Dashbot can analyze customer conversations from multiple sources (chat transcripts, audio calls, support tickets) and highlight sentiment, trending topics, and critical issues using its machine learning-powered model.
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It’s a data-driven business world out there, which is great, right? But with so much input now available, presenting data effectively has become its own challenge. You need to do more than just display numbers and charts. You need to convey a clear, compelling story that communicates the key takeaways and resonates with your audiences. This article’s best practices can help ensure your data presentation communicates your intended message.
Any communication is more effective if you understand who you are presenting to. By considering the audience’s background, knowledge level, interests, and objectives you can better tailor your presentation to match their needs and expectations.
It’s your job to simplify and focus the data around a main message. Your entire presentation should revolve around this central theme. Understanding your audience can help you choose the most relevant and impactful data points that support the main message.
It’s best to use the data to tell a story. A picture may be worth a thousand words, but as one data analyst notes in the Harvard Business Review , “Few forms of communication are as persuasive as a compelling narrative.”
Data storytelling constructs a story from the insights hidden in the data. Taking a storytelling approach forces you to interpret and simplify complex information in an engaging way that offers value to your audience. This empowers you to offer a narrative which makes the data more relatable and engaging, thereby helping build your credibility as well.
An oft-cited study statistic from the National Statistics Council tells us the average employee spends 37% of their time in meetings. Help your audience benefit from the time spent meeting about your data by beginning your presentation with an executive summary.
Executive summaries provide a concise overview offering a quick understanding of the key takeaways without delving into all the details. When you craft an effective executive summary , you capture your main message and distill complex concepts into simple, easy-to-understand language to help your audience grasp the main points.
Data visualization is an essential part of data presentation. Visual elements like charts, graphs, and infographics are powerful tools for effectively presenting and sharing data. Still, you’ll want to choose the right type of data visualization to ensure that you are communicating your findings for maximum impact:
It’s also critical to avoid clutter and ensure that your visuals are easy to read. Using descriptive headlines and labels can help your audience quickly grasp what the data is about and what it’s showing.
It can also help to use color, annotation, or emphasis to draw attention to important data points or trends. This guides your audience’s focus to the most critical aspects of your message. At the same time, don’t undervalue the power of white space to reduce clutter and improve readability,
Without proper context, data can be interpreted in various ways. Be sure to include background information, explanations, and comparisons that help your audience understand the significance of the data .
Providing context by explaining the background and circumstances, strengthens the conclusions you draw from the data. To effectively situate your audience in the data:
Effective presenting and sharing of your data takes effort. You should build in time to rehearse your delivery. Practice your pacing, tone, and transitions between different parts of your story.
It can help also to anticipate potential questions your audience might have about the data. Being prepared to address queries demonstrates your expertise and enhances credibility.
Remember, your goal is to make your data not only informative but also engaging and memorable. By following these best practices for your data presentations, you can increase the likelihood that your audience will understand and remember the insights you’re sharing.
At Sogolytics we know your data isn’t very valuable if you can’t make sense of it. Our powerful reporting tools give you many ways to share and present your data, even in real-time. Learn more .
If you’re looking for a platform that helps you decode the meaning behind your data with powerful analytics and presentation-ready reports, Sogolytics has your back!
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Data can be represented in countless ways. The format for the presentation of data will depend on the target audience and the information that needs to be relayed. In the end, data should be presented in such a way that interpretation and analysis is made easy. Let us see some ways in which we represent data in economics.
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Blog Data Visualization 10 Data Presentation Examples For Strategic Communication
Written by: Krystle Wong Sep 28, 2023
Knowing how to present data is like having a superpower.
Data presentation today is no longer just about numbers on a screen; it’s storytelling with a purpose. It’s about captivating your audience, making complex stuff look simple and inspiring action.
To help turn your data into stories that stick, influence decisions and make an impact, check out Venngage’s free chart maker or follow me on a tour into the world of data storytelling along with data presentation templates that work across different fields, from business boardrooms to the classroom and beyond. Keep scrolling to learn more!
Click to jump ahead:
What should be included in a data presentation, what are some common mistakes to avoid when presenting data, faqs on data presentation examples, transform your message with impactful data storytelling.
Data presentation is a vital skill in today’s information-driven world. Whether you’re in business, academia, or simply want to convey information effectively, knowing the different ways of presenting data is crucial. For impactful data storytelling, consider these essential data presentation methods:
Ideal for comparing data across categories or showing trends over time.
Bar graphs, also known as bar charts are workhorses of data presentation. They’re like the Swiss Army knives of visualization methods because they can be used to compare data in different categories or display data changes over time.
In a bar chart, categories are displayed on the x-axis and the corresponding values are represented by the height of the bars on the y-axis.
It’s a straightforward and effective way to showcase raw data, making it a staple in business reports, academic presentations and beyond.
Make sure your bar charts are concise with easy-to-read labels. Whether your bars go up or sideways, keep it simple by not overloading with too many categories.
Great for displaying trends and variations in data points over time or continuous variables.
Line charts or line graphs are your go-to when you want to visualize trends and variations in data sets over time.
One of the best quantitative data presentation examples, they work exceptionally well for showing continuous data, such as sales projections over the last couple of years or supply and demand fluctuations.
The x-axis represents time or a continuous variable and the y-axis represents the data values. By connecting the data points with lines, you can easily spot trends and fluctuations.
A tip when presenting data with line charts is to minimize the lines and not make it too crowded. Highlight the big changes, put on some labels and give it a catchy title.
Useful for illustrating parts of a whole, such as percentages or proportions.
Pie charts are perfect for showing how a whole is divided into parts. They’re commonly used to represent percentages or proportions and are great for presenting survey results that involve demographic data.
Each “slice” of the pie represents a portion of the whole and the size of each slice corresponds to its share of the total.
While pie charts are handy for illustrating simple distributions, they can become confusing when dealing with too many categories or when the differences in proportions are subtle.
Don’t get too carried away with slices — label those slices with percentages or values so people know what’s what and consider using a legend for more categories.
Effective for showing the relationship between two variables and identifying correlations.
Scatter plots are all about exploring relationships between two variables. They’re great for uncovering correlations, trends or patterns in data.
In a scatter plot, every data point appears as a dot on the chart, with one variable marked on the horizontal x-axis and the other on the vertical y-axis.
By examining the scatter of points, you can discern the nature of the relationship between the variables, whether it’s positive, negative or no correlation at all.
If you’re using scatter plots to reveal relationships between two variables, be sure to add trendlines or regression analysis when appropriate to clarify patterns. Label data points selectively or provide tooltips for detailed information.
Best for visualizing the distribution and frequency of a single variable.
Histograms are your choice when you want to understand the distribution and frequency of a single variable.
They divide the data into “bins” or intervals and the height of each bar represents the frequency or count of data points falling into that interval.
Histograms are excellent for helping to identify trends in data distributions, such as peaks, gaps or skewness.
Here’s something to take note of — ensure that your histogram bins are appropriately sized to capture meaningful data patterns. Using clear axis labels and titles can also help explain the distribution of the data effectively.
Useful for showing how different components contribute to a whole over multiple categories.
Stacked bar charts are a handy choice when you want to illustrate how different components contribute to a whole across multiple categories.
Each bar represents a category and the bars are divided into segments to show the contribution of various components within each category.
This method is ideal for highlighting both the individual and collective significance of each component, making it a valuable tool for comparative analysis.
Stacked bar charts are like data sandwiches—label each layer so people know what’s what. Keep the order logical and don’t forget the paintbrush for snazzy colors. Here’s a data analysis presentation example on writers’ productivity using stacked bar charts:
Similar to line charts but with the area below the lines filled, making them suitable for showing cumulative data.
Area charts are close cousins of line charts but come with a twist.
Imagine plotting the sales of a product over several months. In an area chart, the space between the line and the x-axis is filled, providing a visual representation of the cumulative total.
This makes it easy to see how values stack up over time, making area charts a valuable tool for tracking trends in data.
For area charts, use them to visualize cumulative data and trends, but avoid overcrowding the chart. Add labels, especially at significant points and make sure the area under the lines is filled with a visually appealing color gradient.
Presenting data in rows and columns, often used for precise data values and comparisons.
Tabular data presentation is all about clarity and precision. Think of it as presenting numerical data in a structured grid, with rows and columns clearly displaying individual data points.
A table is invaluable for showcasing detailed data, facilitating comparisons and presenting numerical information that needs to be exact. They’re commonly used in reports, spreadsheets and academic papers.
When presenting tabular data, organize it neatly with clear headers and appropriate column widths. Highlight important data points or patterns using shading or font formatting for better readability.
Utilizing written or descriptive content to explain or complement data, such as annotations or explanatory text.
Textual data presentation may not involve charts or graphs, but it’s one of the most used qualitative data presentation examples.
It involves using written content to provide context, explanations or annotations alongside data visuals. Think of it as the narrative that guides your audience through the data.
Well-crafted textual data can make complex information more accessible and help your audience understand the significance of the numbers and visuals.
Textual data is your chance to tell a story. Break down complex information into bullet points or short paragraphs and use headings to guide the reader’s attention.
Using simple icons or images to represent data is especially useful for conveying information in a visually intuitive manner.
Pictograms are all about harnessing the power of images to convey data in an easy-to-understand way.
Instead of using numbers or complex graphs, you use simple icons or images to represent data points.
For instance, you could use a thumbs up emoji to illustrate customer satisfaction levels, where each face represents a different level of satisfaction.
Pictograms are great for conveying data visually, so choose symbols that are easy to interpret and relevant to the data. Use consistent scaling and a legend to explain the symbols’ meanings, ensuring clarity in your presentation.
Looking for more data presentation ideas? Use the Venngage graph maker or browse through our gallery of chart templates to pick a template and get started!
A comprehensive data presentation should include several key elements to effectively convey information and insights to your audience. Here’s a list of what should be included in a data presentation:
1. Title and objective
2. Key data points
3. Context and significance
4. Key takeaways
5. Visuals and charts
6. Implications or actions
7. Q&A and discussion
Presenting data is a crucial skill in various professional fields, from business to academia and beyond. To ensure your data presentations hit the mark, here are some common mistakes that you should steer clear of:
Presenting too much data at once can overwhelm your audience. Focus on the key points and relevant information to keep the presentation concise and focused. Here are some free data visualization tools you can use to convey data in an engaging and impactful way.
It’s easy to assume that your audience understands as much about the topic as you do. But this can lead to either dumbing things down too much or diving into a bunch of jargon that leaves folks scratching their heads. Take a beat to figure out where your audience is coming from and tailor your presentation accordingly.
Using misleading visuals, such as distorted scales or inappropriate chart types can distort the data’s meaning. Pick the right data infographics and understandable charts to ensure that your visual representations accurately reflect the data.
Data without context is like a puzzle piece with no picture on it. Without proper context, data may be meaningless or misinterpreted. Explain the background, methodology and significance of the data.
Neglecting to cite sources and provide citations for your data can erode its credibility. Always attribute data to its source and utilize reliable sources for your presentation.
Avoid simply presenting numbers. If your presentation lacks a clear, engaging story that takes your audience on a journey from the beginning (setting the scene) through the middle (data analysis) to the end (the big insights and recommendations), you’re likely to lose their interest.
Infographics are great for storytelling because they mix cool visuals with short and sweet text to explain complicated stuff in a fun and easy way. Create one with Venngage’s free infographic maker to create a memorable story that your audience will remember.
Presenting data without first checking its quality and accuracy can lead to misinformation. Validate and clean your data before presenting it.
Fancy charts might look cool, but if they confuse people, what’s the point? Go for the simplest visual that gets your message across. Having a dilemma between presenting data with infographics v.s data design? This article on the difference between data design and infographics might help you out.
Data isn’t just about numbers; it’s about people and real-life situations. Don’t forget to sprinkle in some human touch, whether it’s through relatable stories, examples or showing how the data impacts real lives.
At the end of the day, your audience wants to know what they should do with all the data. If you don’t wrap up with clear, actionable insights or recommendations, you’re leaving them hanging. Always finish up with practical takeaways and the next steps.
Business reports often benefit from data presentation through bar charts showing sales trends over time, pie charts displaying market share,or tables presenting financial performance metrics like revenue and profit margins.
Creative data presentation ideas for academic presentations include using statistical infographics to illustrate research findings and statistical data, incorporating storytelling techniques to engage the audience or utilizing heat maps to visualize data patterns.
When choosing a chart format , consider factors like data complexity, audience expertise and the message you want to convey. Options include charts (e.g., bar, line, pie), tables, heat maps, data visualization infographics and interactive dashboards.
Knowing the type of data visualization that best serves your data is just half the battle. Here are some best practices for data visualization to make sure that the final output is optimized.
To select the right data presentation method, start by defining your presentation’s purpose and audience. Then, match your data type (e.g., quantitative, qualitative) with suitable visualization techniques (e.g., histograms, word clouds) and choose an appropriate presentation format (e.g., slide deck, report, live demo).
For more presentation ideas , check out this guide on how to make a good presentation or use a presentation software to simplify the process.
To enhance data presentations, use compelling narratives, relatable examples and fun data infographics that simplify complex data. Encourage audience interaction, offer actionable insights and incorporate storytelling elements to engage and inform effectively.
The opening of your presentation holds immense power in setting the stage for your audience. To design a presentation and convey your data in an engaging and informative, try out Venngage’s free presentation maker to pick the right presentation design for your audience and topic.
Data presentation typically involves conveying data reports and insights to an audience, often using visuals like charts and graphs. Data visualization , on the other hand, focuses on creating those visual representations of data to facilitate understanding and analysis.
Now that you’ve learned a thing or two about how to use these methods of data presentation to tell a compelling data story , it’s time to take these strategies and make them your own.
But here’s the deal: these aren’t just one-size-fits-all solutions. Remember that each example we’ve uncovered here is not a rigid template but a source of inspiration. It’s all about making your audience go, “Wow, I get it now!”
Think of your data presentations as your canvas – it’s where you paint your story, convey meaningful insights and make real change happen.
So, go forth, present your data with confidence and purpose and watch as your strategic influence grows, one compelling presentation at a time.
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The call and text message records of nearly all of AT&T's cellular customers were exposed in a data breach , the company said Friday.
The company said in a filing with the U.S. Securities and Exchange Commission it learned in April that customer data was illegally downloaded from an AT&T workspace on a third-party cloud platform.
The company said it is working with law enforcement to arrest those involved in the incident, and that at least one person has been apprehended.
"We have an ongoing investigation into the AT&T breach and we're coordinating with our law enforcement partners," the Federal Communications Commission said on social media Friday morning.
Here's what else to know about the massive data breach, if you were affected and what to do if you are concerned about possible exposure of sensitive information.
AT&T data breach: Do users need to do anything?
In the SEC filing, AT&T said the threat actors exfiltrated files containing records of customer call and text interactions that occurred approximately between May 1 and October 31, 2022. The company said the compromised data also includes records from January 2, 2023 for a "very small number of customers."
In addition to cellular customers, customers of mobile virtual network operators (MVNOs) using AT&T's wireless network and AT&T landline customers who interacted with these cellular numbers were also affected.
AT&T said it will contact affected customers by text, email or U.S. mail. Customers can also check their accounts online to see if they were affected.
AT&T said in a news release Friday that "the data does not contain the content of calls or texts, personal information such as Social Security numbers, dates of birth, or other personally identifiable information."
"It also does not include some typical information you see in your usage details, such as the time stamp of calls or texts," the company added.
AT&T also said that while the compromised data also does not include customer names, there are often ways, using publicly available online tools, to find the name associated with a specific telephone number.
"At this time, we do not believe that the data is publicly available," the company said in the news release.
AT&T data breach: AT&T says nearly all of its cell customers' call and text records were exposed in massive breach
AT&T said that as a general rule, customers should remain cautious of any phone call or text request asking you for personal, account or credit card details. If you suspect:
AT&T also says customers should only open text messages from people you know and trust, and shouldn't reply to a text from an unknown sender with personal details.
While AT&T says Social Security numbers were not exposed in the data breach, customers who are concerned about being exposed can follow this guidance from the Federal Trade Commission :
While AT&T said that "personally identifiable information" wasn't involved with the data breach, if you suspect your banking information has been leaked, the FTC suggests:
Contributing: Chad Murphy, USA TODAY Network
Gabe Hauari is a national trending news reporter at USA TODAY. You can follow him on X @GabeHauari or email him at [email protected].
Remember: Less is more.
A strong presentation is so much more than information pasted onto a series of slides with fancy backgrounds. Whether you’re pitching an idea, reporting market research, or sharing something else, a great presentation can give you a competitive advantage, and be a powerful tool when aiming to persuade, educate, or inspire others. Here are some unique elements that make a presentation stand out.
As an intern or early career professional, chances are that you’ll be tasked with making or giving a presentation in the near future. Whether you’re pitching an idea, reporting market research, or sharing something else, a great presentation can give you a competitive advantage, and be a powerful tool when aiming to persuade, educate, or inspire others.
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CrowdStrike’s cybersecurity software is used by scores of industries, including airlines, hospitals and retailers, to prevent hacks and data breaches.
By Kate Conger and Claire Moses
On Friday morning, flights were canceled, broadcasters went off air, trains didn’t run and medical procedures were delayed around the world. Frustrated workers were confronted with blue computer screens and no obvious way to get back online.
The root of the problem? CrowdStrike, a cybersecurity company dedicated to preventing exactly this sort of global outage.
CrowdStrike, which was founded in 2011, is a cloud-based cybersecurity platform whose software is used by scores of industries around the world to protect against hackers and outside breaches.
Its software secures computers at many Fortune 500 companies, and the company has built a reputation over the years as one that can solve even the toughest security problems. CrowdStrike was tapped to investigate the hack of Sony Pictures in 2014, which resulted in the release of confidential data from the movie studio, and the hack of the Democratic National Committee in 2016, which exposed Hillary Clinton’s emails. CrowdStrike serves about 29,000 customers and has about $4 billion in annual sales.
Cybersecurity software like CrowdStrike’s has broad privileges to run across a computer system, including into sensitive areas. That means when errors occur, the ripple effect can be significant.
CrowdStrike updates its security software automatically and silently. A flawed update on Friday morning, of its Falcon Sensor software, resulted in crashes of machines running Microsoft Windows operating system and caused the worldwide chaos.
We’re deeply sorry for the impact that we’ve caused to customers,” George Kurtz, the chief executive of CrowdStrike, said in an interview on the “Today” show. “We know what the issue is. We’re resolving and have resolved the issue now.”
Mr. Kurtz said the outages were not the result of a security incident or cyberattack.
An updated fix of the software has been sent to computers, but experts said outages would most likely persist as CrowdStrike customers worked to reboot computers that had already been affected. Some systems were able to automatically install the fix, Mr. Kurtz said, while others would require manual intervention from I.T. specialists.
CrowdStrike, which went public in 2019, is worth about $76 billion. Its stock fell dramatically on Friday after news of the outage, and remained down about eight percent that morning.
“This is an unprecedented event and not something we or anyone had in their cards,” analysts for Evercore, an investment banking firm, wrote in a note to investors. “This event clearly proves that cyber needs to be diversified and the idea of relying on one or two vendors will come under scrutiny.”
Kate Conger is a technology reporter based in San Francisco. She can be reached at [email protected]. More about Kate Conger
Claire Moses is a Times reporter in London, focused on coverage of breaking and trending news. More about Claire Moses
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Textual Presentation of Data has the following drawbacks: 1. One of the major drawbacks of the textual presentation of data is that it provides extensive data in the form of text and paragraphs which makes it difficult for the user of data to draw a proper conclusion at a glance. This facility is provided in tabular or diagrammatic presentation ...
This method of displaying data uses diagrams and images. It is the most visual type for presenting data and provides a quick glance at statistical data. There are four basic types of diagrams, including: Pictograms: This diagram uses images to represent data. For example, to show the number of books sold in the first release week, you may draw ...
Definition: Data presentation is the art of visualizing complex data for better understanding. Importance: Data presentations enhance clarity, engage the audience, aid decision-making, and leave a lasting impact. Types: Textual, Tabular, and Graphical presentations offer various ways to present data.
Step 1: Define Your Data Hierarchy. While presenting data on the budget allocation, start by outlining the hierarchical structure. The sequence will be like the overall budget at the top, followed by departments, projects within each department, and finally, individual cost categories for each project. Example:
Textual presentation; Data tables; Diagrammatic presentation; Here we will be studying only the textual and tabular presentation, i.e. data tables in some detail. Textual Presentation. The discussion about the presentation of data starts off with it's most raw and vague form which is the textual presentation. In such form of presentation ...
The tabulation method is usually preferred to textual- presentation as. (i) It facilitates comparison between rows and columns. (ii) Complicated data can also be represented using tabulation. (iii) It is a must for diagrammatic representation. (iv) Without tabulation, statistical analysis of data is not possible.
Data can be presented in three ways: 1. Textual Mode of presentation is layman's method of presentation of data. Anyone can prepare, anyone can understand. No specific skill (s) is/are required. 2. Tabular Mode of presentation is the most accurate mode of presentation of data. It requires a lot of skill to prepare, and some skill (s) to ...
Data sets can be presented either by listing all the elements or by giving a table of values and frequencies. This page titled 1.3: Presentation of Data is shared under a CC BY-NC-SA 3.0 license and was authored, remixed, and/or curated by Anonymous via source content that was edited to the style and standards of the LibreTexts platform. In ...
Textual Data refers to sytematically collected material consisting of written, printed, or electronically published words, typically either purposefully written or transcribed from speech.[2] Document. A text document is a single unit of textual data: one element of the systematically collected material acessible in a holistic way (either as a ...
Data Presentation. Data can be presented in one of the three wa ys: - as text; - in tabular form; or. - in graphical form. Methods of presenta tion must be determined according. to the data ...
DATA PRESENTATION, ANALYSIS AND INTERPRETATION. 4.0 Introduction. This chapter is concerned with data pres entation, of the findings obtained through the study. The. findings are presented in ...
Conclusion. With a clear understanding of the tabular format and textual meaning, you can present any data effectively. The textual presentation uses words to present the data. Tabular data is self-explanatory as there are segments that depict what the data wants to convey. The textual data need to be explained with words.
As soon as the data collection is over, the investigator needs to find a way of presenting the data in a meaningful, efficient and easily understood way to identify the main features of the data at a glance using a suitable presentation method. Generally, the data in the statistics can be presented in three different forms, such as textual ...
To make voluminous data readily usable and easil y. comprehensible three forms of presentation are possible. 1. Textual (descriptive) 2. Tabular. 3. Diagrammatic (graphical) When the quantity of ...
Textual analysis is a broad term for various research methods used to describe, interpret and understand texts. All kinds of information can be gleaned from a text - from its literal meaning to the subtext, symbolism, assumptions, and values it reveals. The methods used to conduct textual analysis depend on the field and the aims of the ...
The textual presentation uses words to present the data.Tabular data is self-explanatory as there are segments that depict what the data wants to convey. The textual data need to be explained with words.The key difference thus is that the textual representation of data is subjective. In a tabular format, the data is mentioned in the form of ...
Tabular Ways of Data Presentation and Analysis. To avoid the complexities involved in the textual way of data presentation, people use tables and charts to present data. In this method, data is presented in rows and columns - just like you see in a cricket match showing who made how many runs. Each row and column have an attribute (name, year ...
Textual data refers to any information or message conveyed in written or printed form. It can include everything from books, articles, and surveys to social media posts, chat conversations, and emails. It is processed using NLP techniques that analyze the text's words, syntax, and context to extract meaningful insights.
Data visualization is an essential part of data presentation. Visual elements like charts, graphs, and infographics are powerful tools for effectively presenting and sharing data. ... Word clouds let you show textual data with word size representing frequency, which makes it easy to identify common terms. ... Define any technical terms or ...
The format for the presentation of data will depend on the target audience and the information that needs to be relayed. In the end, data should be presented in a way that interpretation and analysis is made easy. ... Diagrammatic Presentation of Data; Textual and Tabular Presentation of Data; Share with friends. Customize your course in 30 ...
What are the advantages and disadvantages of presenting a data in textual form? The advantage of it is that, the data would be more interpreted, because there is a direct explanation of how the data happened to be like that. It may appear overwhelming and too long so its
This document discusses techniques for presenting and interpreting data in tabular and graphical forms. It focuses on textual presentation of data, which involves presenting numbers, measurements, and other data points using words and sentences. Some key considerations for effective textual presentation include knowing the target audience, using unbiased language, maintaining accuracy ...
8. Tabular presentation. Presenting data in rows and columns, often used for precise data values and comparisons. Tabular data presentation is all about clarity and precision. Think of it as presenting numerical data in a structured grid, with rows and columns clearly displaying individual data points.
The call and text message records of nearly all of AT&T's cellular customers were exposed in a data breach, the company said Friday.. The company said in a filing with the U.S. Securities and ...
A strong presentation is so much more than information pasted onto a series of slides with fancy backgrounds. Whether you're pitching an idea, reporting market research, or sharing something ...
Across the world, critical businesses and services including airlines, hospitals, train networks and TV stations, were disrupted on Friday by a global tech outage affecting Microsoft users.
CrowdStrike's cybersecurity software is used by scores of industries, including airlines, hospitals and retailers, to prevent hacks and data breaches. By Kate Conger and Claire Moses On Friday ...
Huge protests across Bangladesh escalated into deadly violence this week with clashes between students, pro-government supporters and armed police fueling widespread anger over civil service job ...