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Understanding Data Presentations (Guide + Examples)

In this age of overwhelming information, the skill to effectively convey data has become extremely valuable. Initiating a discussion on data presentation types involves thoughtful consideration of the nature of your data and the message you aim to convey. Different types of visualizations serve distinct purposes. Whether you’re dealing with how to develop a report or simply trying to communicate complex information, how you present data influences how well your audience understands and engages with it. This extensive guide leads you through the different ways of data presentation.

Table of Contents

What is a Data Presentation?

What should a data presentation include, line graphs, treemap chart, scatter plot, how to choose a data presentation type, recommended data presentation templates, common mistakes done in data presentation.

We can label a presentation under the title of data presentation when the aim is to disclose quantitative information to an audience through the usage of visual formats and narrative techniques. The overall purpose of this kind of presentation is to simplify complex concepts, allowing the presenter to highlight trends, patterns, and insights with the core purpose of acting upon the shared information. This process requires a series of tools, such as charts, graphs, tables, infographics, dashboards, and so on, supported by concise textual explanations for better understanding and boosting retention rate.

Data presentations go beyond the mere usage of graphical elements. Seasoned presenters encompass visuals with the art of storytelling with data, so the speech skillfully connects the points through a narrative that resonates with the audience. Depending on the purpose – inspire, persuade, inform, support decision-making processes, etc. – is the data presentation format that is better suited to help us in this journey.

To nail your upcoming data presentation, ensure to count with the following elements:

• Clear Objectives: Understand the intent of your presentation before selecting the graphical layout and metaphors to make content easier to grasp.
• Engaging introduction: Use a powerful hook from the get-go. For instance, you can ask a big question or present a problem that your data will answer. Take a look at our guide on how to start a presentation for tips & insights.
• Structured Narrative: Your data presentation must tell a coherent story. This means a beginning where you present the context, a middle section in which you present the data, and an ending that uses a call-to-action. Check our guide on presentation structure for further information.
• Visual Elements: These are the charts, graphs, and other elements of visual communication we ought to use to present data. This article will cover one by one the different types of data representation methods we can use, and provide further guidance on choosing between them.
• Insights and Analysis: This is not just showcasing a graph and letting people get an idea about it. A proper data presentation includes the interpretation of that data, the reason why it’s included, and why it matters to your research.
• Conclusion & CTA: Ending your presentation with a call to action is necessary. Whether you intend to wow your audience into acquiring your services, inspire them to change the world, or whatever the purpose of your presentation, there must be a stage in which you convey all that you shared and show the path to staying in touch. Plan ahead whether you want to use a thank-you slide, a video presentation, or which method is apt and tailored to the kind of presentation you deliver.
• Q&A Session: After your speech is concluded, allocate 3-5 minutes for the audience to raise any questions about the information you disclosed. This is an extra chance to establish your authority on the topic. Check our guide on questions and answer sessions in presentations here.

Bar charts are a graphical representation of data using rectangular bars to show quantities or frequencies in an established category. They make it easy for readers to spot patterns or trends. Bar charts can be horizontal or vertical, although the vertical format is commonly known as a column chart. They display categorical, discrete, or continuous variables grouped in class intervals [1] . They include an axis and a set of labeled bars horizontally or vertically. These bars represent the frequencies of variable values or the values themselves. Numbers on the y-axis of a vertical bar chart or the x-axis of a horizontal bar chart are called the scale.

Real-Life Application of Bar Charts

Let’s say a sales manager is presenting sales to their audience. Using a bar chart, he follows these steps.

Step 1: Selecting Data

The first step is to identify the specific data you will present to your audience.

The sales manager has highlighted these products for the presentation.

• Product A: Men’s Shoes
• Product B: Women’s Apparel
• Product C: Electronics
• Product D: Home Decor

Step 2: Choosing Orientation

Opt for a vertical layout for simplicity. Vertical bar charts help compare different categories in case there are not too many categories [1] . They can also help show different trends. A vertical bar chart is used where each bar represents one of the four chosen products. After plotting the data, it is seen that the height of each bar directly represents the sales performance of the respective product.

It is visible that the tallest bar (Electronics – Product C) is showing the highest sales. However, the shorter bars (Women’s Apparel – Product B and Home Decor – Product D) need attention. It indicates areas that require further analysis or strategies for improvement.

Step 3: Colorful Insights

Different colors are used to differentiate each product. It is essential to show a color-coded chart where the audience can distinguish between products.

• Men’s Shoes (Product A): Yellow
• Women’s Apparel (Product B): Orange
• Electronics (Product C): Violet
• Home Decor (Product D): Blue

Bar charts are straightforward and easily understandable for presenting data. They are versatile when comparing products or any categorical data [2] . Bar charts adapt seamlessly to retail scenarios. Despite that, bar charts have a few shortcomings. They cannot illustrate data trends over time. Besides, overloading the chart with numerous products can lead to visual clutter, diminishing its effectiveness.

For more information, check our collection of bar chart templates for PowerPoint .

Line graphs help illustrate data trends, progressions, or fluctuations by connecting a series of data points called ‘markers’ with straight line segments. This provides a straightforward representation of how values change [5] . Their versatility makes them invaluable for scenarios requiring a visual understanding of continuous data. In addition, line graphs are also useful for comparing multiple datasets over the same timeline. Using multiple line graphs allows us to compare more than one data set. They simplify complex information so the audience can quickly grasp the ups and downs of values. From tracking stock prices to analyzing experimental results, you can use line graphs to show how data changes over a continuous timeline. They show trends with simplicity and clarity.

Real-life Application of Line Graphs

To understand line graphs thoroughly, we will use a real case. Imagine you’re a financial analyst presenting a tech company’s monthly sales for a licensed product over the past year. Investors want insights into sales behavior by month, how market trends may have influenced sales performance and reception to the new pricing strategy. To present data via a line graph, you will complete these steps.

First, you need to gather the data. In this case, your data will be the sales numbers. For example:

• January: $45,000
• February: $55,000
• March: $45,000
• April: $60,000
• May: $ 70,000
• June: $65,000
• July: $62,000
• August: $68,000
• September: $81,000
• October: $76,000
• November: $87,000
• December: $91,000

After choosing the data, the next step is to select the orientation. Like bar charts, you can use vertical or horizontal line graphs. However, we want to keep this simple, so we will keep the timeline (x-axis) horizontal while the sales numbers (y-axis) vertical.

Step 3: Connecting Trends

After adding the data to your preferred software, you will plot a line graph. In the graph, each month’s sales are represented by data points connected by a line.

Step 4: Adding Clarity with Color

If there are multiple lines, you can also add colors to highlight each one, making it easier to follow.

Line graphs excel at visually presenting trends over time. These presentation aids identify patterns, like upward or downward trends. However, too many data points can clutter the graph, making it harder to interpret. Line graphs work best with continuous data but are not suitable for categories.

For more information, check our collection of line chart templates for PowerPoint .

A data dashboard is a visual tool for analyzing information. Different graphs, charts, and tables are consolidated in a layout to showcase the information required to achieve one or more objectives. Dashboards help quickly see Key Performance Indicators (KPIs). You don’t make new visuals in the dashboard; instead, you use it to display visuals you’ve already made in worksheets [3] .

Keeping the number of visuals on a dashboard to three or four is recommended. Adding too many can make it hard to see the main points [4]. Dashboards can be used for business analytics to analyze sales, revenue, and marketing metrics at a time. They are also used in the manufacturing industry, as they allow users to grasp the entire production scenario at the moment while tracking the core KPIs for each line.

Real-Life Application of a Dashboard

Consider a project manager presenting a software development project’s progress to a tech company’s leadership team. He follows the following steps.

Step 1: Defining Key Metrics

To effectively communicate the project’s status, identify key metrics such as completion status, budget, and bug resolution rates. Then, choose measurable metrics aligned with project objectives.

Step 2: Choosing Visualization Widgets

After finalizing the data, presentation aids that align with each metric are selected. For this project, the project manager chooses a progress bar for the completion status and uses bar charts for budget allocation. Likewise, he implements line charts for bug resolution rates.

Step 3: Dashboard Layout

Key metrics are prominently placed in the dashboard for easy visibility, and the manager ensures that it appears clean and organized.

Dashboards provide a comprehensive view of key project metrics. Users can interact with data, customize views, and drill down for detailed analysis. However, creating an effective dashboard requires careful planning to avoid clutter. Besides, dashboards rely on the availability and accuracy of underlying data sources.

For more information, check our article on how to design a dashboard presentation , and discover our collection of dashboard PowerPoint templates .

Treemap charts represent hierarchical data structured in a series of nested rectangles [6] . As each branch of the ‘tree’ is given a rectangle, smaller tiles can be seen representing sub-branches, meaning elements on a lower hierarchical level than the parent rectangle. Each one of those rectangular nodes is built by representing an area proportional to the specified data dimension.

Treemaps are useful for visualizing large datasets in compact space. It is easy to identify patterns, such as which categories are dominant. Common applications of the treemap chart are seen in the IT industry, such as resource allocation, disk space management, website analytics, etc. Also, they can be used in multiple industries like healthcare data analysis, market share across different product categories, or even in finance to visualize portfolios.

Real-Life Application of a Treemap Chart

Let’s consider a financial scenario where a financial team wants to represent the budget allocation of a company. There is a hierarchy in the process, so it is helpful to use a treemap chart. In the chart, the top-level rectangle could represent the total budget, and it would be subdivided into smaller rectangles, each denoting a specific department. Further subdivisions within these smaller rectangles might represent individual projects or cost categories.

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.

• Top-level rectangle: Total Budget
• Second-level rectangles: Departments (Engineering, Marketing, Sales)
• Third-level rectangles: Projects within each department
• Fourth-level rectangles: Cost categories for each project (Personnel, Marketing Expenses, Equipment)

Step 2: Choose a Suitable Tool

It’s time to select a data visualization tool supporting Treemaps. Popular choices include Tableau, Microsoft Power BI, PowerPoint, or even coding with libraries like D3.js. It is vital to ensure that the chosen tool provides customization options for colors, labels, and hierarchical structures.

Here, the team uses PowerPoint for this guide because of its user-friendly interface and robust Treemap capabilities.

Step 3: Make a Treemap Chart with PowerPoint

After opening the PowerPoint presentation, they chose “SmartArt” to form the chart. The SmartArt Graphic window has a “Hierarchy” category on the left.  Here, you will see multiple options. You can choose any layout that resembles a Treemap. The “Table Hierarchy” or “Organization Chart” options can be adapted. The team selects the Table Hierarchy as it looks close to a Treemap.

Step 5: Input Your Data

After that, a new window will open with a basic structure. They add the data one by one by clicking on the text boxes. They start with the top-level rectangle, representing the total budget.  

Step 6: Customize the Treemap

By clicking on each shape, they customize its color, size, and label. At the same time, they can adjust the font size, style, and color of labels by using the options in the “Format” tab in PowerPoint. Using different colors for each level enhances the visual difference.

Treemaps excel at illustrating hierarchical structures. These charts make it easy to understand relationships and dependencies. They efficiently use space, compactly displaying a large amount of data, reducing the need for excessive scrolling or navigation. Additionally, using colors enhances the understanding of data by representing different variables or categories.

In some cases, treemaps might become complex, especially with deep hierarchies.  It becomes challenging for some users to interpret the chart. At the same time, displaying detailed information within each rectangle might be constrained by space. It potentially limits the amount of data that can be shown clearly. Without proper labeling and color coding, there’s a risk of misinterpretation.

A heatmap is a data visualization tool that uses color coding to represent values across a two-dimensional surface. In these, colors replace numbers to indicate the magnitude of each cell. This color-shaded matrix display is valuable for summarizing and understanding data sets with a glance [7] . The intensity of the color corresponds to the value it represents, making it easy to identify patterns, trends, and variations in the data.

As a tool, heatmaps help businesses analyze website interactions, revealing user behavior patterns and preferences to enhance overall user experience. In addition, companies use heatmaps to assess content engagement, identifying popular sections and areas of improvement for more effective communication. They excel at highlighting patterns and trends in large datasets, making it easy to identify areas of interest.

We can implement heatmaps to express multiple data types, such as numerical values, percentages, or even categorical data. Heatmaps help us easily spot areas with lots of activity, making them helpful in figuring out clusters [8] . When making these maps, it is important to pick colors carefully. The colors need to show the differences between groups or levels of something. And it is good to use colors that people with colorblindness can easily see.

Check our detailed guide on how to create a heatmap here. Also discover our collection of heatmap PowerPoint templates .

Pie charts are circular statistical graphics divided into slices to illustrate numerical proportions. Each slice represents a proportionate part of the whole, making it easy to visualize the contribution of each component to the total.

The size of the pie charts is influenced by the value of data points within each pie. The total of all data points in a pie determines its size. The pie with the highest data points appears as the largest, whereas the others are proportionally smaller. However, you can present all pies of the same size if proportional representation is not required [9] . Sometimes, pie charts are difficult to read, or additional information is required. A variation of this tool can be used instead, known as the donut chart , which has the same structure but a blank center, creating a ring shape. Presenters can add extra information, and the ring shape helps to declutter the graph.

Pie charts are used in business to show percentage distribution, compare relative sizes of categories, or present straightforward data sets where visualizing ratios is essential.

Real-Life Application of Pie Charts

Consider a scenario where you want to represent the distribution of the data. Each slice of the pie chart would represent a different category, and the size of each slice would indicate the percentage of the total portion allocated to that category.

Step 1: Define Your Data Structure

Imagine you are presenting the distribution of a project budget among different expense categories.

• Column A: Expense Categories (Personnel, Equipment, Marketing, Miscellaneous)
• Column B: Budget Amounts ($40,000, $30,000, $20,000, $10,000) Column B represents the values of your categories in Column A.

Step 2: Insert a Pie Chart

Using any of the accessible tools, you can create a pie chart. The most convenient tools for forming a pie chart in a presentation are presentation tools such as PowerPoint or Google Slides.  You will notice that the pie chart assigns each expense category a percentage of the total budget by dividing it by the total budget.

For instance:

• Personnel: $40,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 40%
• Equipment: $30,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 30%
• Marketing: $20,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 20%
• Miscellaneous: $10,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 10%

You can make a chart out of this or just pull out the pie chart from the data.

3D pie charts and 3D donut charts are quite popular among the audience. They stand out as visual elements in any presentation slide, so let’s take a look at how our pie chart example would look in 3D pie chart format.

Step 03: Results Interpretation

The pie chart visually illustrates the distribution of the project budget among different expense categories. Personnel constitutes the largest portion at 40%, followed by equipment at 30%, marketing at 20%, and miscellaneous at 10%. This breakdown provides a clear overview of where the project funds are allocated, which helps in informed decision-making and resource management. It is evident that personnel are a significant investment, emphasizing their importance in the overall project budget.

Pie charts provide a straightforward way to represent proportions and percentages. They are easy to understand, even for individuals with limited data analysis experience. These charts work well for small datasets with a limited number of categories.

However, a pie chart can become cluttered and less effective in situations with many categories. Accurate interpretation may be challenging, especially when dealing with slight differences in slice sizes. In addition, these charts are static and do not effectively convey trends over time.

For more information, check our collection of pie chart templates for PowerPoint .

Histograms present the distribution of numerical variables. Unlike a bar chart that records each unique response separately, histograms organize numeric responses into bins and show the frequency of reactions within each bin [10] . The x-axis of a histogram shows the range of values for a numeric variable. At the same time, the y-axis indicates the relative frequencies (percentage of the total counts) for that range of values.

Whenever you want to understand the distribution of your data, check which values are more common, or identify outliers, histograms are your go-to. Think of them as a spotlight on the story your data is telling. A histogram can provide a quick and insightful overview if you’re curious about exam scores, sales figures, or any numerical data distribution.

Real-Life Application of a Histogram

In the histogram data analysis presentation example, imagine an instructor analyzing a class’s grades to identify the most common score range. A histogram could effectively display the distribution. It will show whether most students scored in the average range or if there are significant outliers.

Step 1: Gather Data

He begins by gathering the data. The scores of each student in class are gathered to analyze exam scores.

After arranging the scores in ascending order, bin ranges are set.

Step 2: Define Bins

Bins are like categories that group similar values. Think of them as buckets that organize your data. The presenter decides how wide each bin should be based on the range of the values. For instance, the instructor sets the bin ranges based on score intervals: 60-69, 70-79, 80-89, and 90-100.

Step 3: Count Frequency

Now, he counts how many data points fall into each bin. This step is crucial because it tells you how often specific ranges of values occur. The result is the frequency distribution, showing the occurrences of each group.

Here, the instructor counts the number of students in each category.

• 60-69: 1 student (Kate)
• 70-79: 4 students (David, Emma, Grace, Jack)
• 80-89: 7 students (Alice, Bob, Frank, Isabel, Liam, Mia, Noah)
• 90-100: 3 students (Clara, Henry, Olivia)

Step 4: Create the Histogram

It’s time to turn the data into a visual representation. Draw a bar for each bin on a graph. The width of the bar should correspond to the range of the bin, and the height should correspond to the frequency.  To make your histogram understandable, label the X and Y axes.

In this case, the X-axis should represent the bins (e.g., test score ranges), and the Y-axis represents the frequency.

The histogram of the class grades reveals insightful patterns in the distribution. Most students, with seven students, fall within the 80-89 score range. The histogram provides a clear visualization of the class’s performance. It showcases a concentration of grades in the upper-middle range with few outliers at both ends. This analysis helps in understanding the overall academic standing of the class. It also identifies the areas for potential improvement or recognition.

Thus, histograms provide a clear visual representation of data distribution. They are easy to interpret, even for those without a statistical background. They apply to various types of data, including continuous and discrete variables. One weak point is that histograms do not capture detailed patterns in students’ data, with seven compared to other visualization methods.

A scatter plot is a graphical representation of the relationship between two variables. It consists of individual data points on a two-dimensional plane. This plane plots one variable on the x-axis and the other on the y-axis. Each point represents a unique observation. It visualizes patterns, trends, or correlations between the two variables.

Scatter plots are also effective in revealing the strength and direction of relationships. They identify outliers and assess the overall distribution of data points. The points’ dispersion and clustering reflect the relationship’s nature, whether it is positive, negative, or lacks a discernible pattern. In business, scatter plots assess relationships between variables such as marketing cost and sales revenue. They help present data correlations and decision-making.

Real-Life Application of Scatter Plot

A group of scientists is conducting a study on the relationship between daily hours of screen time and sleep quality. After reviewing the data, they managed to create this table to help them build a scatter plot graph:

In the provided example, the x-axis represents Daily Hours of Screen Time, and the y-axis represents the Sleep Quality Rating.

The scientists observe a negative correlation between the amount of screen time and the quality of sleep. This is consistent with their hypothesis that blue light, especially before bedtime, has a significant impact on sleep quality and metabolic processes.

There are a few things to remember when using a scatter plot. Even when a scatter diagram indicates a relationship, it doesn’t mean one variable affects the other. A third factor can influence both variables. The more the plot resembles a straight line, the stronger the relationship is perceived [11] . If it suggests no ties, the observed pattern might be due to random fluctuations in data. When the scatter diagram depicts no correlation, whether the data might be stratified is worth considering.

Choosing the appropriate data presentation type is crucial when making a presentation . Understanding the nature of your data and the message you intend to convey will guide this selection process. For instance, when showcasing quantitative relationships, scatter plots become instrumental in revealing correlations between variables. If the focus is on emphasizing parts of a whole, pie charts offer a concise display of proportions. Histograms, on the other hand, prove valuable for illustrating distributions and frequency patterns. 

Bar charts provide a clear visual comparison of different categories. Likewise, line charts excel in showcasing trends over time, while tables are ideal for detailed data examination. Starting a presentation on data presentation types involves evaluating the specific information you want to communicate and selecting the format that aligns with your message. This ensures clarity and resonance with your audience from the beginning of your presentation.

1. Fact Sheet Dashboard for Data Presentation

Convey all the data you need to present in this one-pager format, an ideal solution tailored for users looking for presentation aids. Global maps, donut chats, column graphs, and text neatly arranged in a clean layout presented in light and dark themes.

Use This Template

2. 3D Column Chart Infographic PPT Template

Represent column charts in a highly visual 3D format with this PPT template. A creative way to present data, this template is entirely editable, and we can craft either a one-page infographic or a series of slides explaining what we intend to disclose point by point.

3. Data Circles Infographic PowerPoint Template

An alternative to the pie chart and donut chart diagrams, this template features a series of curved shapes with bubble callouts as ways of presenting data. Expand the information for each arch in the text placeholder areas.

4. Colorful Metrics Dashboard for Data Presentation

This versatile dashboard template helps us in the presentation of the data by offering several graphs and methods to convert numbers into graphics. Implement it for e-commerce projects, financial projections, project development, and more.

5. Animated Data Presentation Tools for PowerPoint & Google Slides

A slide deck filled with most of the tools mentioned in this article, from bar charts, column charts, treemap graphs, pie charts, histogram, etc. Animated effects make each slide look dynamic when sharing data with stakeholders.

6. Statistics Waffle Charts PPT Template for Data Presentations

This PPT template helps us how to present data beyond the typical pie chart representation. It is widely used for demographics, so it’s a great fit for marketing teams, data science professionals, HR personnel, and more.

7. Data Presentation Dashboard Template for Google Slides

A compendium of tools in dashboard format featuring line graphs, bar charts, column charts, and neatly arranged placeholder text areas. 

8. Weather Dashboard for Data Presentation

Share weather data for agricultural presentation topics, environmental studies, or any kind of presentation that requires a highly visual layout for weather forecasting on a single day. Two color themes are available.

9. Social Media Marketing Dashboard Data Presentation Template

Intended for marketing professionals, this dashboard template for data presentation is a tool for presenting data analytics from social media channels. Two slide layouts featuring line graphs and column charts.

10. Project Management Summary Dashboard Template

A tool crafted for project managers to deliver highly visual reports on a project’s completion, the profits it delivered for the company, and expenses/time required to execute it. 4 different color layouts are available.

11. Profit & Loss Dashboard for PowerPoint and Google Slides

A must-have for finance professionals. This typical profit & loss dashboard includes progress bars, donut charts, column charts, line graphs, and everything that’s required to deliver a comprehensive report about a company’s financial situation.

Overwhelming visuals

One of the mistakes related to using data-presenting methods is including too much data or using overly complex visualizations. They can confuse the audience and dilute the key message.

Inappropriate chart types

Choosing the wrong type of chart for the data at hand can lead to misinterpretation. For example, using a pie chart for data that doesn’t represent parts of a whole is not right.

Lack of context

Failing to provide context or sufficient labeling can make it challenging for the audience to understand the significance of the presented data.

Inconsistency in design

Using inconsistent design elements and color schemes across different visualizations can create confusion and visual disarray.

Failure to provide details

Simply presenting raw data without offering clear insights or takeaways can leave the audience without a meaningful conclusion.

Lack of focus

Not having a clear focus on the key message or main takeaway can result in a presentation that lacks a central theme.

Visual accessibility issues

Overlooking the visual accessibility of charts and graphs can exclude certain audience members who may have difficulty interpreting visual information.

In order to avoid these mistakes in data presentation, presenters can benefit from using presentation templates . These templates provide a structured framework. They ensure consistency, clarity, and an aesthetically pleasing design, enhancing data communication’s overall impact.

Understanding and choosing data presentation types are pivotal in effective communication. Each method serves a unique purpose, so selecting the appropriate one depends on the nature of the data and the message to be conveyed. The diverse array of presentation types offers versatility in visually representing information, from bar charts showing values to pie charts illustrating proportions. 

Using the proper method enhances clarity, engages the audience, and ensures that data sets are not just presented but comprehensively understood. By appreciating the strengths and limitations of different presentation types, communicators can tailor their approach to convey information accurately, developing a deeper connection between data and audience understanding.

[1] Government of Canada, S.C. (2021) 5 Data Visualization 5.2 Bar Chart , 5.2 Bar chart .  https://www150.statcan.gc.ca/n1/edu/power-pouvoir/ch9/bargraph-diagrammeabarres/5214818-eng.htm

[2] Kosslyn, S.M., 1989. Understanding charts and graphs. Applied cognitive psychology, 3(3), pp.185-225. https://apps.dtic.mil/sti/pdfs/ADA183409.pdf

[3] Creating a Dashboard . https://it.tufts.edu/book/export/html/1870

[4] https://www.goldenwestcollege.edu/research/data-and-more/data-dashboards/index.html

[5] https://www.mit.edu/course/21/21.guide/grf-line.htm

[6] Jadeja, M. and Shah, K., 2015, January. Tree-Map: A Visualization Tool for Large Data. In GSB@ SIGIR (pp. 9-13). https://ceur-ws.org/Vol-1393/gsb15proceedings.pdf#page=15

[7] Heat Maps and Quilt Plots. https://www.publichealth.columbia.edu/research/population-health-methods/heat-maps-and-quilt-plots

[8] EIU QGIS WORKSHOP. https://www.eiu.edu/qgisworkshop/heatmaps.php

[9] About Pie Charts.  https://www.mit.edu/~mbarker/formula1/f1help/11-ch-c8.htm

[10] Histograms. https://sites.utexas.edu/sos/guided/descriptive/numericaldd/descriptiven2/histogram/ [11] https://asq.org/quality-resources/scatter-diagram

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Present Your Data Like a Pro

• Joel Schwartzberg

Demystify the numbers. Your audience will thank you.

While a good presentation has data, data alone doesn’t guarantee a good presentation. It’s all about how that data is presented. The quickest way to confuse your audience is by sharing too many details at once. The only data points you should share are those that significantly support your point — and ideally, one point per chart. To avoid the debacle of sheepishly translating hard-to-see numbers and labels, rehearse your presentation with colleagues sitting as far away as the actual audience would. While you’ve been working with the same chart for weeks or months, your audience will be exposed to it for mere seconds. Give them the best chance of comprehending your data by using simple, clear, and complete language to identify X and Y axes, pie pieces, bars, and other diagrammatic elements. Try to avoid abbreviations that aren’t obvious, and don’t assume labeled components on one slide will be remembered on subsequent slides. Every valuable chart or pie graph has an “Aha!” zone — a number or range of data that reveals something crucial to your point. Make sure you visually highlight the “Aha!” zone, reinforcing the moment by explaining it to your audience.

With so many ways to spin and distort information these days, a presentation needs to do more than simply share great ideas — it needs to support those ideas with credible data. That’s true whether you’re an executive pitching new business clients, a vendor selling her services, or a CEO making a case for change.

• JS Joel Schwartzberg oversees executive communications for a major national nonprofit, is a professional presentation coach, and is the author of Get to the Point! Sharpen Your Message and Make Your Words Matter and The Language of Leadership: How to Engage and Inspire Your Team . You can find him on LinkedIn and X. TheJoelTruth

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Data Analysis 101: How to Make Your Presentations Practical and Effective

• December 27, 2022
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Understanding Importance of Data Analysis

The results of data analysis can give business the vital insights they need to turn in to successful and profitable ventures. It could be the difference between a successful business operation and a business operation that is in trouble.

Data analysis, though one of the most in-demand job roles globally, doesn’t require a degree in statistics or mathematics to do well, and employers from a wide variety of industries are very keen to recruit data analysts.

Businesses hire data analysts in the field of finance, marketing, administration, HR, IT and procurement, to name just a few.  Understand the big picture and provide answers. By engaging in data analysis, you can actually delve deep and discover hidden truths that most business people would never be able to do.

What skills you should master to be a data analyst?

While Data Analyst roles are on the rise, there are certain skills that are vital for anyone who wants to become a data analyst . Before the job, a candidate needs to have either a degree in statistics, business or computer science or a related subject, or work experience in these areas. 

If you’re interested in becoming a data analyst, you’ll need to know: 

• Programming and algorithms
• Data Visualization 
• Open-source and cloud technologies 
• No coding experience is required. 

How much is a data analyst worth?  Data analysts earn an average salary of £32,403 per annum, according to jobs site Glassdoor. This pays for a salary, with benefits such as medical insurance and paid leave included in the starting salary.  If you think you have the right skills, there are plenty of roles on offer.

What data analysis entails

Data analysis is an analytical process which involves recording and tabulating (recording and entering, entering and tabulating) the quantities of a product, such as numbers of units produced, costs of materials and expenses.

While data analyst can take different forms, for example in databases, in other structures such as spreadsheets, numbers are the main means of data entry. This involves entering and entering the required data in a data analysis system such as Excel.

For example, although a database doesn’t require a data analyst, it can still benefit from data analysis techniques such as binomial testing, ANOVA and Fisher’s exact tests.  Where is the data analysis courses in IT?  Given the ever-increasing reliance on technology in business, data analysis courses are vital skills.

What are the types of data analysis methods?

• Cluster analysis 

The act of grouping a specific set of data in a manner that those elements are more similar to one another than to those in other groups – hence the term ‘cluster.’ Since there is no special target variable while doing clustering, the method is often used to find hidden patterns in the data. The approach is purposely used to offer additional context to a particular trend or dataset.  

• Cohort analysis 

This type of data analysis method uses historical data to examine and compare a determined segment of users’ behavior, which can then be grouped with others with similar characteristics. By using this data analysis methodology, it’s possible to gain a wealth of insight into consumer needs or a firm understanding of a broader target group.

A dependent variable is an element of a complex system that is assumed to have a single cause, but it’s affected by multiple factors, thus giving researchers an indication as to how a complex system function.  

• Regression analysis

The regression analysis is used to predict how the value of a dependent variable changes when one or more independent variables change, stay the same or the dependent variable is not moved. Regression is a sophisticated statistical method that includes mathematical functions that are typically called “segmentation,” “distribution,” and “intercept” functions.

Regression is a type of regression analysis that only contains linear and quadratic functions. You can change the types of factors (or the independent variables) that are selected in regression analysis (it’s typically called “nonlinear regression analysis”) by changing the order in which the models are constructed.To begin, let’s explain how regression analysis works.  

Examples in business world

The Oracle Corporation is one of the first multinational companies to adopt this type of analysis method, based on which the company was able to develop predictive modelling systems for marketing purposes.

In a more specific sense, a Regression analysis is a popular type of data analysis used for analyzing the likelihood that a random variable will move up or down a range of parameters in response to a change in a specific control variable.

Companies who use this type of analysis are looking for trends and patterned performance over time. For example, how a company may respond to a rising cost of labor and its effect on its business bottom line, a weather-related issue like an earthquake, a new advertising campaign, or even a surge in customer demand in some areas.

What are basic pointers to consider while presenting data

Recognize that presentation matters.

Too often, analysts make the mistake of presenting information in order to show an abstracted version of it.  For instance, say a B2B company has 4 ways to improve their sales funnel:

• More Visually Engaging 
• More Easily Transacted 
• More Cost Effective 

Then, “informative” would mean that a B2B company needs to optimize their sales funnel to each of these to be more “convenient, faster, easier, more visually engaging, or most cost effective.” Sure, it would be nice if they all improved – they would all provide a competitive advantage in some way. But that’s not what the data tells us.

Don’t scare people with numbers

When you’re presenting data, show as many as possible, in as many charts as possible. Then, try to talk through the implications of the data, rather than overwhelming people with an overwhelming amount of data.

Why? Research suggests that when a number is presented in a visual, people become more likely to process it and learn from it.  I recommend using video, text, graphs, and pictures to represent your numbers. This creates a more visually appealing data set. The number of followers on Twitter is visually appealing. The number of followers on Facebook is visually appealing. But nobody looks at their Twitter followers. If you don’t know what your numbers mean, how will your audience?  That doesn’t mean numbers aren’t important.

Maximize the data pixel ratio

The more data you show to a critical stakeholder, the more likely they are to get lost and distracted from what you’re actually trying to communicate. This is especially important in the case of people in the sales and marketing function.

Do you have a sales person out in the field who is trying to close a deal? It would be a shame if that person got lost in your Excel analytics and lost out on the sale.  This problem also occurs on the web.

Consider how web visitors respond to large, colorful charts and graphs. If we’re talking about visualizations that depict web performance, a visual might be helpful. But how often do we see this done?  Research shows that people respond better to web-based data in a simplified, less complex format.

Save 3-D for the movies

There are great stories in the universe. This is an oversimplification, but if you look at history, humans only understand stories. We are great storytellers. We develop, through trial and error, our own intuition about the “right” way to tell stories.

 One of the most powerful and effective ways to present data is to go beyond the visual to the audible, that is, to tell stories in a way that people can relate to. Everything you hear about computers being a series of numbers is wrong. We visualize numbers in a precise, quantitative way. But the numbers are not a collection of isolated events. To understand them, we need to understand the broader context.

Friends don’t let friends use pie charts

Businesses and analysts have done this since pie charts first appeared on Microsoft Excel sheets. When presenting data, break down your pie chart into its component segments.

 As opposed to an equal-sized circle for the average earnings for all the employees, share a pie chart where the percentages for each individual segment are different, with a link to the corresponding chart.

 Pair with explanatory text, show their correlation, and make your choice based on your audience, not on whether you want to scare or “educate” them. The majority of audiences will see the same image, regardless of whether it’s presented in a bar chart, bar chart, line chart, or something else.

Choose the appropriate chart

Does the data make logical sense? Check your assumptions against the data.  Are the graphs charting only part of the story? Include other variables in the graphs.  Avoid using axis labels to mislead. Never rely on axes to infer, “logical” conclusions.  Trust your eyes: you know what information your brain can process.

Think of numbers like music — they are pleasing, but not overwhelming.  Save 3D for the movies. When everyone is enjoying 4K, 8K, and beyond, it’s hard to envision your audience without the new stuff. I remember the first time I got to see HDTV. At home, I sat behind a chair and kept turning around to watch the TV. But at the theatre, I didn’t need a chair. All I had to do was look up, and see the giant screen, the contrast, and the detail.

Don’t mix chart types for no reason

Excel chart s with colored areas help people focus. Arrows give us scale. Assume your audience doesn’t understand what you’re saying, even if they do. Nobody wants to open a recipe book to learn how to cook soup. Instead, we start with a recipe.

Use a formula to communicate your analysis with as few words as possible. Keep it simple.  Resist the urge to over-complicate your presentation. A word cloud is not a word cloud. A bar chart is not a bar chart. If you use a word cloud to illustrate a chart, consider replacing a few words with a gif. A bar chart doesn’t need clouds. And a bar chart doesn’t need clouds.  If there’s one thing that’s sure to confuse your audience, it’s bar charts.

Use color with intention

Use color with intention. It’s not about pretty. When it comes to presenting data clearly, “informative” is more important than “beautiful.” 

However, visualizations like maps, axes, or snapshots can help visual communication to avoid this pitfall. If you are going to show a few locations on a map, make sure each location has a voice and uses a distinct color. Avoid repeating colors from the map or bottom bar in all the visuals. Be consistent with how you present the data .  A pie chart is not very interesting if all it shows is a bunch of varying sizes of the pie.

Data analysis in the workplace, and how it will impact the future of business

Business leaders are taking note of the importance of data analysis skills in their organisation, as it can make an enormous impact on business.

 Larger organisations such as Google, Amazon and Facebook employ huge teams of analysts to create their data and statistics. We are already seeing the rise of the next generation of big data analysts – those who can write code that analyses and visualizes the data and report back information to a company to help it improve efficiency and increase revenue. 

The increasing need for high-level understanding of data analysis has already led to the role of data analyst becoming available at university level. It is no longer a mandatory business qualification but one that can enhance your CV.

By understanding the importance of each variable, you can improve your business by managing your time and creating more effective systems and processes for running your business. The focus shifts from just providing services to providing value to your customers, creating a better, more intuitive experience for them so they can work with your company for the long-term. 

Adopting these small steps will allow you to be more effective in your business and go from being an employee to an entrepreneur.

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5 Presentation Tips for Data Analysts – Data Storytelling

When you’re working in analytics, telling a story can be as important as the actual data behind it. Your analytical insights are only as good as the actions that you can take from them, and persuasive presentations are at the core of convincing business people to act. I realize that sounds cliche, but it’s true. Without effective presentation skills, an analyst’s job is incomplete.

At MITRE and at Sestra, I’ve worked with some great engineers, but they have a tendency to put WAY too much information on their slides and to get lost behind the language there. It’s always surprising to me to discover how little education many people receive around effective presentations. I feel lucky that my grad school program focused on these skills, and I draw from some of it’s lessons below.

The most prominent example of this lack of presentation education I experienced was at MITRE when one of the smartest folks on my team presented results of work that he had done. He’s a terrific engineer and was doing brilliant work. However, the presentation was one of the dullest that I’ve ever been in attendance for. Every slide had paragraphs of text, and he mostly read them to us. For 45 minutes. I was even on the more technical side of the audience, and I couldn’t understand most of the terminology. It was a disaster.

To avoid a situation like that, here are 5 quick things to remember when you’re compiling your next presentation.

1) WIIFTA – What’s in it for the audience? – Remember that you’re not giving the presentation for you. It’s for the people who are listening. A typical example of a WIIFTA consideration could be a detailed methodology slide. This slide is important to you – it’s a clever new twist on an analytics technique and without you, your company would never have thought of it. But it’s not about you.

Consider your audience: In a context where you are presenting your findings to a room full of data scientists looking to learn the latest language or technique, this slide would be absolutely appropriate. It’s a clever new way to look at something and this group wants to learn about it. However, in a context where you’re presenting the conclusions of your research to your company’s executives, it’s likely that this slide should minimized or removed completely, because this group doesn’t care. They likely trust that you’ve found a good solution, however you got there, and they just want to know why they should make a certain decision.

Oy, the following slide has some other issues (which we’ll revisit later), but if your audience demands a methodology slide, fine, include one. But if you include this slide when presenting to your boss, you’ve probably already lost her (and this is only slide 3!).

2) BLUF – Bottom Line Up Front. – For most young analysts, this is the single biggest change to your presenting skill set that you can make. Oftentimes, an analyst wants to lay out a bunch of evidence and allow it to walk the audience to a conclusion. This is how your research worked, and it feels like the right way to structure your findings and to tell the story, too. It is not. In a business context, the audience wants to know exactly what they are listening for immediately. This goes for a presentation as a whole, along with individual sections or slides.

Check out this slide from kissmetrics. Even at 10% size, you know exactly what the slide is about. Well done. Most analysts writing this slide would have made the header “Equipment Choices” or some equivalent, but it is so much nicer to know exactly what conclusion you are supposed to draw right away.

3) Seriously. Less Text. If someone is reading your slides they’re not paying attention to you, the presenter. Minimize the amount of reading that they have to do while not listening to you. I recommend trying to keep slides to 1 statement or phrase or conclusion per slide.

Let’s look at that methodology slide again. If you have to include it, because your audience is interested, could you make it less of a giant block of text? Try to reduce this slide to a few key ideas:

4) Make a separate version. If your slides need to stand on their own, that’s understandable – sometimes not everyone can make the live presentation. However, the deck that you use that can stand on its own shouldn’t be the same version that you use when you’re presenting with it. By definition if it can stand on its own, you don’t need to be there and you’ve just made yourself obsolete. Yes, I know. Maintaining two separate versions of the same presentation can be a hassle, but it’s worth the trouble.

The following slides are from a presenstation that I gave recently. Forgive the bad BLUF in the second slide, but it shows how I maintained a different variation for people who missed the in-person presentation.

5) Make use of highlights and callouts to draw the eye. Sometimes you need to show a larger about of information on a slide in order to give context. That’s acceptable, at times, but make sure you’ve simplified it as much as possible. Once you’ve done that, use highlights, or bold, or boxes to draw attention to the important takeaway from the slide.

Look again at the slide from my presentation above. While analytics can be important across the company, this particular presentation was focused on Product Analytics. I wanted to call out the fact that going forward in the presentation, we were focused on that area.

If you keep these 5 things in mind, it’ll help keep your audience engaged with you as you take them through your data story. If you forget, you risk losing them before they get the whole story, and you can’t get your point across.

For some more detailed reading, Avinash Kaushik’s Storytelling with Data article is also a terrific read!

Published by Stephen

I love helping people at all levels of a company use data to inform their decision making. View all posts by Stephen

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10 Data Presentation Tips

There’s a popular joke in data circles that you might have already heard: Data practitioners spend 80% of their time preparing data and 20% complaining about preparing data. The truth is, there’s much more to being a data professional than this. Sure, you’ll prepare data — and complain about it sometimes — but you’ll also need to make data presentations to key stakeholders in your company. Remember, data doesn’t mean much until you provide context and present it clearly.

Thankfully, we’re here to help. Here are 10 data presentation tips to effectively communicate with executives, senior managers, marketing managers, and other stakeholders.

1. Choose a Communication Style

Every data professional has a different way of presenting data to their audience. Some people like to tell stories with data, illustrating solutions to existing and potential business problems. Others enjoy using personas to demonstrate how their data findings impact real people. And then some like to present data more conventionally and simply explain what different figures and statistics mean in a business context.

Whatever style you choose, think about the words you will use and how you will present your information. You’ll want to engage your audience as much as possible, even if your findings aren’t particularly interesting.

2. Break Down Complicated Information

Not everyone will comprehend data as well as you do. As a data practitioner, you’ll understand the nuances of data, such as how different data sets correlate with each other and how outliers can impact analysis. However, most people lack knowledge of these concepts.

That’s why you should simplify your data presentations and focus on key takeaways from your findings that stakeholders will understand. For example, instead of showing your audience a spreadsheet with lots of numbers, explain what those numbers  prove and what they mean for the company you will work for.

3. Choose the Right Data Visualizations

Sharing cold, hard data sets with people won’t be very effective. Instead, use different data visualizations so your audience can understand the relationships between data sets and the context behind them. There are lots of  different visualizations  that help you communicate important information:

• Line graphs
• Scatter plots

The type of  visualizations you choose depends on what information you’re trying to convey. Graphs, for example, help you showcase potential business outcomes to stakeholders clearly and consistently. Heat maps, on the other hand, let you highlight the most critical data values your audience should know about.

4. Choose the Right Visualization Tools

Numerous  data visualization tools  on the market will help you communicate data to people in your company. These tools include:

• Microsoft Power BI
• Google Charts

All of these tools are inherently better than presenting data in Excel. You’ll be able to communicate patterns and trends in data more effectively and encourage your audience to interact with your findings.

5. Get Your Audience Involved

Communication is a two-way process, so encourage those at your future data presentations to interact with your content. Before you begin your data presentation, you might want to tell your audience to interrupt you if they want more clarification about a particular data point or insight.

Alternatively, people can ask you questions at the end of your presentation if they don’t understand something or require additional context.

6. Be Authoritative

You’ll almost always present your data findings to key stakeholders in your business. Project confidence when sharing insights and make it clear you know what you’re talking about. Otherwise, your audience might lack confidence in your abilities. Ultimately, explain how you came to a particular conclusion and why you think it’s important to share.

Of course, there will be times when the data you present won’t be what your audience wants to hear. For example, a line graph might reveal that a business will lose revenue over time . In these scenarios, always communicate the facts, even if doing so puts you in an uncomfortable position.

7. Label Your Data Clearly

This point goes back to the fact that your audience won’t know as much about your data as you do. So, avoid using unfamiliar acronyms to label charts or complicated jargon that only other data practitioners would understand. Your role is to present information in a clear and visually compelling way to help stakeholders make better data-driven decisions .

8. Practice Your Data Presentation With Other Team Members

You can always have a dress rehearsal for a presentation before walking into the boardroom. Delivering your findings to other data practitioners on your team, data scientists, data engineers, or other data professionals in your department will help you identify any weak spots in your presentation and ensure you use the right communication style for your audience.

9. Allow Your Audience to Access Your Findings After Your Presentation

A 30- or 60-minute meeting normally won’t be long enough to communicate all your findings or receive stakeholder feedback. Audience members might also forget key points after it’s finished. So, share your insights after your presentation, perhaps in a document. You’ll be able to email colleagues your report so they can review important information. Alternatively, you can upload your presentation slides to Dropbox or your company’s intranet.

Data practitioners often worry about presenting their data to an audience, which is understandable. But you’ll develop a unique communication style and become more confident as the months and years go by. Just remember you’re not a doctor breaking bad news about an incurable health condition. You’re helping businesses understand data, which can be an exciting thing, so try to relax and enjoy yourself!

The Pragmatic Editorial Team comprises a diverse team of writers, researchers, and subject matter experts. We are trained to share Pragmatic Institute’s insights and useful information to guide product, data, and design professionals on their career development journeys. Pragmatic Institute is the global leader in Product, Data, and Design training and certification programs for working professionals. Since 1993, we’ve issued over 250,000 product management and product marketing certifications to professionals at companies around the globe. For questions or inquiries, please contact [email protected] .

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10 Data Presentation Examples For Strategic Communication

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:

10 Essential data presentation examples + methods you should know

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:

1. Bar graph

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.

2. Line graph

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.

3. Pie chart

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.

4. Scatter plot

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.

5. Histogram

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.

6. Stacked bar chart

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:

7. Area chart

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.

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. 

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.

9. Textual data

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.

10. Pictogram

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

• Begin with a clear and informative title that sets the context for your presentation.
• State the primary objective or purpose of the presentation to provide a clear focus.

2. Key data points

• Present the most essential data points or findings that align with your objective.
• Use charts, graphical presentations or visuals to illustrate these key points for better comprehension.

3. Context and significance

• Provide a brief overview of the context in which the data was collected and why it’s significant.
• Explain how the data relates to the larger picture or the problem you’re addressing.

4. Key takeaways

• Summarize the main insights or conclusions that can be drawn from the data.
• Highlight the key takeaways that the audience should remember.

5. Visuals and charts

• Use clear and appropriate visual aids to complement the data.
• Ensure that visuals are easy to understand and support your narrative.

6. Implications or actions

• Discuss the practical implications of the data or any recommended actions.
• If applicable, outline next steps or decisions that should be taken based on the data.

7. Q&A and discussion

• Allocate time for questions and open discussion to engage the audience.
• Address queries and provide additional insights or context as needed.

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:

Overloading with data

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. 

Assuming everyone’s on the same page

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.

Misleading visuals

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.

Not providing context

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.

Not citing sources properly

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.

Not telling a story

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.

Ignoring data quality

Presenting data without first checking its quality and accuracy can lead to misinformation. Validate and clean your data before presenting it.

Simplify your visuals

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. 

Missing the emotional connection

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.

Skipping the actionable insights

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.

Can you provide some data presentation examples for business reports?

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.

What are some creative data presentation examples for academic presentations?

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.

What are the key considerations when choosing the right data presentation format?

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. 

How can I choose the right data presentation method for my data?

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.  

How can I make my data presentations more engaging and informative?

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. 

What is the difference between data visualization and data presentation?

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.

10 Superb Data Presentation Examples To Learn From

The best way to learn how to present data effectively is to see data presentation examples from the professionals in the field.

We collected superb examples of graphical presentation and visualization of data in statistics, research, sales, marketing, business management, and other areas.

On this page:

How to present data effectively? Clever tips.

• 10 Real-life examples of data presentation with interpretation.

Download the above infographic in PDF

Your audience should be able to walk through the graphs and visualizations easily while enjoy and respond to the story.

[bctt tweet=”Your reports and graphical presentations should not just deliver statistics, numbers, and data. Instead, they must tell a story, illustrate a situation, provide proofs, win arguments, and even change minds.” username=””]

Before going to data presentation examples let’s see some essential tips to help you build powerful data presentations.

1. Keep it simple and clear

The presentation should be focused on your key message and you need to illustrate it very briefly.

Graphs and charts should communicate your core message, not distract from it. A complicated and overloaded chart can distract and confuse. Eliminate anything repetitive or decorative.

2. Pick up the right visuals for the job

A vast number of types of graphs and charts are available at your disposal – pie charts, line and bar graphs, scatter plot , Venn diagram , etc.

Choosing the right type of chart can be a tricky business. Practically, the choice depends on 2 major things: on the kind of analysis you want to present and on the data types you have.

Commonly, when we aim to facilitate a comparison, we use a bar chart or radar chart. When we want to show trends over time, we use a line chart or an area chart and etc.

3. Break the complex concepts into multiple graphics

It’s can be very hard for a public to understand a complicated graphical visualization. Don’t present it as a huge amount of visual data.

Instead, break the graphics into pieces and illustrate how each piece corresponds to the previous one.

4. Carefully choose the colors

Colors provoke different emotions and associations that affect the way your brand or story is perceived. Sometimes color choices can make or break your visuals.

It is no need to be a designer to make the right color selections. Some golden rules are to stick to 3 or 4 colors avoiding full-on rainbow look and to borrow ideas from relevant chart designs.

Another tip is to consider the brand attributes and your audience profile. You will see appropriate color use in the below data presentation examples.

5. Don’t leave a lot of room for words

The key point in graphical data presentation is to tell the story using visuals and images, not words. Give your audience visual facts, not text.

However, that doesn’t mean words have no importance.

A great advice here is to think that every letter is critical, and there’s no room for wasted and empty words. Also, don’t create generic titles and headlines, build them around the core message.

6. Use good templates and software tools

Building data presentation nowadays means using some kind of software programs and templates. There are many available options – from free graphing software solutions to advanced data visualization tools.

Choosing a good software gives you the power to create good and high-quality visualizations. Make sure you are using templates that provides characteristics like colors, fonts, and chart styles.

A small investment of time to research the software options prevents a large loss of productivity and efficiency at the end.

10 Superb data presentation examples 

Here we collected some of the best examples of data presentation made by one of the biggest names in the graphical data visualization software and information research.

These brands put a lot of money and efforts to investigate how professional graphs and charts should look.

1. Sales Stage History  Funnel Chart 

Data is beautiful and this sales stage funnel chart by Zoho Reports prove this. The above funnel chart represents the different stages in a sales process (Qualification, Need Analysis, Initial Offer, etc.) and shows the potential revenue for each stage for the last and this quarter.

The potential revenue for each sales stage is displayed by a different color and sized according to the amount. The chart is very colorful, eye-catching, and intriguing.

2. Facebook Ads Data Presentation Examples

These are other data presentation examples from Zoho Reports. The first one is a stacked bar chart that displays the impressions breakdown by months and types of Facebook campaigns.

Impressions are one of the vital KPI examples in digital marketing intelligence and business. The first graph is designed to help you compare and notice sharp differences at the Facebook campaigns that have the most influence on impression movements.

The second one is an area chart that shows the changes in the costs for the same Facebook campaigns over the months.

The 2 examples illustrate how multiple and complicated data can be presented clearly and simply in a visually appealing way.

3. Sales Opportunity Data Presentation

These two bar charts (stacked and horizontal bar charts) by Microsoft Power Bi are created to track sales opportunities and revenue by region and sales stage.

The stacked bar graph shows the revenue probability in percentage determined by the current sales stage (Lead, Quality, Solution…) over the months. The horizontal bar chart represents the size of the sales opportunity (Small, Medium, Large) according to regions (East, Central, West).

Both graphs are impressive ways for a sales manager to introduce the upcoming opportunity to C-level managers and stakeholders. The color combination is rich but easy to digest.

4. Power 100 Data Visualization 

Want to show hierarchical data? Treemaps can be perfect for the job. This is a stunning treemap example by Infogram.com that shows you who are the most influential industries. As you see the Government is on the top.

This treemap is a very compact and space-efficient visualization option for presenting hierarchies, that gives you a quick overview of the structure of the most powerful industries.

So beautiful way to compare the proportions between things via their area size.

When it comes to best research data presentation examples in statistics, Nielsen information company is an undoubted leader. The above professional looking line graph by Nielsen represent the slowing alcoholic grow of 4 alcohol categories (Beer, Wine, Spirits, CPG) for the period of 12 months.

The chart is an ideal example of a data visualization that incorporates all the necessary elements of an effective and engaging graph. It uses color to let you easily differentiate trends and allows you to get a global sense of the data. Additionally, it is incredibly simple to understand.

6. Digital Health Research Data Visualization Example

Digital health is a very hot topic nowadays and this stunning donut chart by IQVIA shows the proportion of different mobile health apps by therapy area (Mental Health, Diabetes, Kidney Disease, and etc.). 100% = 1749 unique apps.

This is a wonderful example of research data presentation that provides evidence of Digital Health’s accelerating innovation and app expansion.

Besides good-looking, this donut chart is very space-efficient because the blank space inside it is used to display information too.

7. Disease Research Data Visualization Examples

Presenting relationships among different variables is hard to understand and confusing -especially when there is a huge number of them. But using the appropriate visuals and colors, the IQVIA did a great job simplifying this data into a clear and digestible format.

The above stacked bar charts by IQVIA represents the distribution of oncology medicine spendings by years and product segments (Protected Brand Price, Protected Brand Volume, New Brands, etc.).

The chart allows you to clearly see the changes in spendings and where they occurred – a great example of telling a deeper story in a simple way.

8. Textual and Qualitative Data Presentation Example

When it comes to easy to understand and good looking textual and qualitative data visualization, pyramid graph has a top place. To know what is qualitative data see our post quantitative vs qualitative data .

9. Product Metrics Graph Example

If you are searching for excel data presentation examples, this stylish template from Smartsheet can give you good ideas for professional looking design.

The above stacked bar chart represents product revenue breakdown by months and product items. It reveals patterns and trends over the first half of the year that can be a good basis for data-driven decision-making .

10. Supply Chain Data Visualization Example 

This bar chart created by ClicData  is an excellent example of how trends over time can be effectively and professionally communicated through the use of well-presented visualization.

It shows the dynamics of pricing through the months based on units sold, units shipped, and current inventory. This type of graph pack a whole lot of information into a simple visual. In addition, the chart is connected to real data and is fully interactive.

The above data presentation examples aim to help you learn how to present data effectively and professionally.

About The Author

Silvia Valcheva

Silvia Valcheva is a digital marketer with over a decade of experience creating content for the tech industry. She has a strong passion for writing about emerging software and technologies such as big data, AI (Artificial Intelligence), IoT (Internet of Things), process automation, etc.

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Data Analytics Powerpoint Presentation Slides

Analyze raw data in order to make a conclusion by utilizing this Data Analytics PowerPoint Presentation Slides. Take the assistance of this data mining PPT visuals to mention the importance of social media and interactive platforms like Google, Facebook, Twitter, Youtube, Instagram. Showcase how cloud computing provides real-time information and on-demand insights with the help of data source PPT graphics. Take the aid of this big data management PPT templates to showcase the web services which provide free and quick information insights to everyone. You can also, discuss how big data is generated from the internet of things with the help of data transformation PPT graphics. You can also highlight the popular databases such as MS Access, DB2, Oracle, SQL, which can provide for the interaction of insights that are used to drive business profits. Display various data warehouse applications that help in the analysis of transactional data. Discuss the sources of big data such as legacy documents, media, cloud, social influencers, etc. Help your business operate more effectively by downloading this data integration PowerPoint Presentation

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This complete deck is oriented to make sure you do not lag in your presentations. Our creatively crafted slides come with apt research and planning. This exclusive deck with twenty slides is here to help you to strategize, plan, analyze, or segment the topic with clear understanding and apprehension. Utilize ready to use presentation slides on Data Analytics Powerpoint Presentation Slides with all sorts of editable templates, charts and graphs, overviews, analysis templates. The presentation is readily available in both 4:3 and 16:9 aspect ratio. Alter the colors, fonts, font size, and font types of the template as per the requirements. It can be changed into formats like PDF, JPG, and PNG. It is usable for marking important decisions and covering critical issues. This presentation deck can be used by all professionals, managers, individuals, internal-external teams involved in any company organization.

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Content of this Powerpoint Presentation

Data may come across as a technical term to us but the truth is we analyze and process data in our everyday lives. From calculating the right amount of ingredients for a cup of coffee to giving ETAs of your assigned tasks, data analytics is part and parcel of our lives. Organizations employ data analytics tools to anticipate and achieve success.Identifying the right sources of data is a primary requirement for delivering accurate results and should be conveyed to teams handling these channels. For this, you need Data Analytics PowerPoint Presentation Slides to highlight the key sources of data procurement so that the relevant team will know whom to approach. 

Our complete deck on Data Analytics PowerPoint Presentation Slides offers a visually appealing way to guide your organization in identifying the correct sources of data. This data will then be sent to processing and analysis to generate valuable key insights. The data analysis thus obtained will be a fair, all-encompassing, and a reliable source of information for the organization to refer to and draw conclusions from. On this note, let’s explore the best presentation slides of this PPT Template to give you an idea of the investment you will make upon downloading it.

Template 1: Media

This slide of our data analytics PowerPoint Presentation will highlight the importance of media as a hub of data to draw insights on customer preferences and changing trends. It will emphasize on the importance of social media channels and interactive platforms in being a rich source of qualitative and quantitative data. By highlighting media as a reliable source of data, this PPT Template will guide teams in employing this important channel for data analytics.

Template 2: Cloud

With cloud-based products and services gaining significance,, it would be a missed opportunity not to leverage them for sourcing data. Highlight the significance of cloud computing, emphasizing its ability to accommodate large data files and its accessibility, making it a vast reservoir of data, on this PowerPoint slide. Highlight the fact that using cloud files to fetch data will widen the scope of information collected from sources thus validating your analysis more.

Template 3: Web

Utilize the world wide web as a data resource to guide your business strategies and assessments and point this important reservoir of data with this PPT Template. Your team can explore the plethora of researches, statistics, and news shared by verified portals to back up your data analytics report. The visuals and icons will add to the effect of conveying its importance. 

Template 4: Internet of Things

The contribution of IoT in data analytics will always be top-tier and you can convey the same with content-ready visuals of this PPT Design. Sensors, software, and other devices that gather first-hand data add credibility to subsequent analysis. performed on it thereof can be pointed out during the discussion and elaboration. During discussion, highlight the IoT devices utilized in your organization, showcasing their role in data analytics and organizational benefits. This PPT Layout facilitates easy awareness building. 

Template 5: Databases

Data is an asset and your organization can rely on previously collected, stored, and processed data that will guide future analysis. Emphasize the importance of your organizational database in guiding future analytics work. Use this slide to encourage data governance of the database and direct teams to rely on it for future data analytics. 

Template 6: Social Network Profiles

In this PPT Slide, you can focus on social media profiles being contributors to the data sent for analysis and drawing important conclusions. By examining profiles on platforms such as Twitter, Facebook, LinkedIn etc, garther a list of like-minded prospective clients to study their interests and to devise your business strategies. Using API integration, you can analyze relevant B2B marketers and tailor pitches accordingly. 

Template 7: Social influencers

Social influencers can serve as another source of data collection allowing you to tap into the potential of influencer marketing and use their profiles to collect important data, customer preferences, and inclinations. Blog posts, user forums, review sites, are some of the ways you can get the most out of influencer marketing contributing to your companies data analytics.

Template 8: Activity-Generated Data

Businesses can acquire additional data for processing and analysis by tracking usage, generating feedback forms, and enquiring about customer preferences. IoT embedded in applications, products, or as a part of service contract will help companies study the interest and usage of their services and products by clients. This will also be the basis of a reliable data analytics report for your company. 

Template 9: Big Data Sources

In this slide, you can summarize all the previously discussed big data sources and add to this list. Icons will support the easy visualization of the sources being discussed and you can edit the list as all of our slides are 100% editable and customizable.

Template 10: Network and In-Stream Monitoring Technologies

This PPT Slide will help you highlight the importance of network and in-stream monitoring technologies in data analytics. In this presentation design you can talk about how monitoring the incoming and outgoing traffic on a computer network will help users fetch data that will be helpful in data analytics. You can point to the need for specialized hardware and/or software in collecting this important data. So, download it now!

Know Your Tools

As you help your audience know the tools for data analysis, you can assign respective teams to be vigilant about collecting the big data. Discuss the process of collecting data and how to preserve it for long without depleting its value or tampering it. Use this carefully collected data to power your analytic reports and this journey will begin effectively upon downloading this comprehensive training material titled Data Analytics PowerPoint Presentation.

Data Analytics Powerpoint Presentation Slides with all 20 slides:

Use our Data Analytics Powerpoint Presentation Slides to effectively help you save your valuable time. They are readymade to fit into any presentation structure.

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How Do I Become a Cybersecurity Data Analyst?

Learn how to become a cybersecurity data analyst and the benefits of working in cybersecurity.

The cybersecurity industry is currently worth $172.32 billion and continues to grow at a fast rate [ 1 ]. The US Bureau of Labor Statistics (BLS) predicts an industry growth rate of 32 percent between now and 2032, creating more than 168,00 jobs for cybersecurity analysts [ 2 ]. Additionally, data from the 2023 Cybersecurity Workforce Study conducted by ISC2 revealed a significant gap between the number of skilled cybersecurity professionals and the demand in the US and abroad [ 3 ]. In an industry so in demand, salary and career progression are excellent if you have the skills, experience, and education you need to work as a cybersecurity data analyst. 

In this article, learn more about the role of a cybersecurity data analyst, the salary, and the steps you can take to start your career in cybersecurity. 

Read more: 5 Cybersecurity Career Paths (and How to Get Started)

What is a cybersecurity data analyst?

A cybersecurity data analyst, also known as an information security analyst , protects organizations from cyberattacks and security breaches by analyzing data to assess vulnerabilities in infrastructure and computer networks. 

Hackers and prevented attacks leave information behind that a cybersecurity data analyst can use to determine future threats, types of attacks, and any specialist software to be aware of. Knowing what to look for and developing familiarity with bad actors' tactics allows cybersecurity data analysts to implement more robust security measures.

Read more: Information Security Analyst: Salary and Job Outlook

Duties and responsibilities 

The typical tasks you will perform as a cybersecurity data analyst are similar to those of a general data analyst. However, in this role, you will analyze and interpret data specifically relevant to cybersecurity and the safety of a company. Duties and responsibilities include:

Analyzing and assessing security systems and infrastructure

Monitoring website traffic and potential threats as they happen

Using encryption and firewalls for protection and restricting access

Undertaking risk analysis

Conducting penetration tests

Writing reports and incident logs

Strengthening security vulnerabilities 

Read more: What Does a Cybersecurity Analyst Do? Job Guide

Salary and job outlook

As mentioned, a cybersecurity data analyst's job outlook and career progression are excellent, with an expected growth rate of 32 percent [ 2 ]. The average annual US salary for information security analysts, another name for the cybersecurity data analyst role, is 119,693.51, according to data from Lightcast™ [4]. In comparison, Glassdoor suggests that a data analyst outside of the cybersecurity industry earns significantly less, with an average base salary of $76,983 [ 5 ].

How do I become a data analyst in cybersecurity?

Working as a cybersecurity data analyst involves demonstrating your skills in data analysis and putting that into the context of cybersecurity, along with relevant experience, qualifications, and certifications. 

Let’s examine each step in more detail to help you envision the path forward.

Step 1: Get a degree.

Earning a degree is an excellent way to learn the skills and knowledge you need to work as a cybersecurity data analyst. Consider earning your degree in computer science or a similar field. Sometimes, employers may look for professionals with a master’s degree . In some colleges, you may find specialist cybersecurity degrees to help you focus your studies further. Examples include a bachelor’s degree in cybersecurity and information assurance and a master’s in information security. 

However, a degree is only one of your options. According to Zippia, 61 percent of cybersecurity data analysts have a bachelor’s degree [ 6 ]. In other cases, employers value skills over education and look favorably on boot camps where you learn up-to-date skills in a hands-on setting and in a shorter time than a degree. 

Step 2: Gain experience.

Experience is crucial in getting hired as a cybersecurity analyst; in some cases, experience and certifications can be enough without a degree. Working in an IT role, in an entry-level information security job, taking an internship, or coming from a data analysis role outside cybersecurity—you have many options to gain valuable experience you can use. 

Step 3: Obtain certification.

In a fast-moving technical industry like cybersecurity, certifications are of high importance. They are a way of learning and consolidating relevant, up-to-date skills essential to certain positions. Some employers ask for specific certifications, but even if it’s not required, having these credentials can be a helpful addition to your resume, particularly when applying for a competitive role. For a cybersecurity data analyst role, you might consider credentials such as:

Certified Ethical Hacker (CEH)

Certified Information Systems Security Professional (CISSP)

CompTIA Security+

Certified Information Security Manager (CISM)

Step 4: Put yourself out there.

Leverage your professional network to help find opportunities. Attend industry events, enroll in boot camps, network, and work on personal projects with others. You might also consider connecting with a mentor to guide you based on their experience. 

Skills of a cybersecurity data analyst

Cybersecurity data analysts have a specific skill set that blends both technical skills and workplace skills. To secure a role, you must build and demonstrate these skills:

Technical skills

Network security

Cloud security

Data modeling and management

Data analysis

Risk analysis

Data modeling 

Python and other programming languages

Intrusion detection

Incident response

Workplace skills

Analytical skills

Communication skills

Problem-solving

Critical thinking

Adaptability

Collaboration

Time management

Detail oriented 

How to transition from data analyst to cybersecurity data analyst

Although you already have many foundational skills necessary for working as a cybersecurity data analyst, you will need to engage in additional training or earn cybersecurity-specific certifications for an effective transition from a more generalized data analyst role. For example, you’ll need to develop a robust understanding of privacy issues, regulatory standards and compliance strategies, and how to work with threat models.  

Perks of working in cybersecurity

Working in cybersecurity has many perks, many of which you’ll find outlined above. Let's recap.

High salary : The cybersecurity industry offers high salaries, averaging over $100,000 [3]. Top-level roles in cybersecurity come with even higher wages. For example, a chief information security officer earns an average of $202,717, with bonuses and additional pay upwards of $100,000, according to February 2024 data from Glassdoor [ 7 ].

Growing industry : The BLS anticipates a growth rate of 32 percent in the decade leading up to 2032 [ 1 ].

Career growth : Due to the industry’s rapid rate of expansion and significant skills gap, available jobs outweigh the number of suitable candidates. If you have the right skills, this translates to professional opportunities. 

Getting started with Coursera

To start a career in the cybersecurity industry, begin building your skills and knowledge. Getting a degree is only one of your available options. You might also earn a resume-building credential by taking courses such as the Google Cybersecurity Professional Certificate or the IBM Cybersecurity Analyst Professional Certificate—both available on Coursera. 

Article sources

Fortune Business Insights. “ Cybersecurity Market , https://www.fortunebusinessinsights.com/industry-reports/cyber-security-market-101165” Accessed February 26, 2024. 

US Bureau of Labor Statistics. “ Occupational Outlook Handbook, Information Security Analysts , https://www.bls.gov/ooh/computer-and-information-technology/information-security-analysts.htm.” Accessed February 26, 2024. 

ISC2. “ ISC2 Cybersecurity Workforce Study: Looking Deeper Into the Workforce Gap , https://www.isc2.org/Insights/2023/11/ISC2-Cybersecurity-Workforce-Study-Looking-Deeper-into-the-Workforce-Gap.” Accessed February 26, 2024. 

Lightcast™ Analyst. “Occupation Summary for Information Security Analysts.” Accessed February 26, 2024.

Glassdoor. “ How Much Does A Data Analyst Make? , https://www.glassdoor.com/Salaries/data-analyst-salary-SRCH_KO0,12.htm.” Accessed February 26, 2024. 

Zippia. “ Best Colleges and Degrees For Cyber Security Analyst s, https://www.zippia.com/cyber-security-analyst-jobs/education/?src=chatbot_popout_displayed.” Accessed February 26, 2024.

Glassdoor. “ How Much Does A Chief Information Security Officer Make? , https://www.glassdoor.com/Salaries/us-chief-information-security-officer-salary-SRCH_IL.0,2_IN1_KO3,37.htm.” Accessed February 26, 2024.

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Coursera staff.

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This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

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News & Insights

These 2 ‘Strong Buy’ Penny Stocks Could Go Boom, Say Analysts

March 28, 2024 — 10:30 am EDT

Written by Michael Marcus for TipRanks  ->

Nearly three months into 2024, it’s clear that the bullish trends are here to stay. Markets hit a bottom in late October, but since then the S&P 500 has gained 27% and now stands at more than 5,200. The NASDAQ index is up 30% from its own October 27 low point. These are bull-market numbers, and show little sign of stopping.

Watching the market from Oppenheimer, chief investment strategist John Stoltzfus comments on the market outlook: “S&P 500 earnings results over the most recent two quarterly reporting seasons, economic data that persists in showing resilience, the Fed’s mandate-sensitive monetary policy, and prospects for innovation coupled with cross generational demographic needs that suggest a shift in mindset driven not so much by fear and greed but a need to invest for intermediate to longer-term goals suggest to us an opportunity to tweak our target higher… We are increasing our year-end target price for the S&P 500 to $5,500 (from $5,200).”

The growing appetite for risk among investors bodes well for the overall stock market, but it is especially good for the higher-risk stocks. For those willing to embrace this risk, the potential rewards can be substantial. The penny stocks, equities priced below $5 per share, exemplify this combination of risk and reward, with the potential to double or even triple the initial investment.

Given the inherent volatility of these investments, Wall Street analysts recommend doing some due diligence before pulling the trigger, noting that not all penny stocks are bound for greatness.

With this in mind, we set out on our own search for compelling investments that are set to boom. Using TipRanks’ database , we pulled two penny stocks that have amassed enough analyst support to earn a “Strong Buy” consensus rating. Not to mention, each offers massive upside potential.

Lineage Cell Therapeutics ( LCTX )

First up is a micro-cap biotherapeutic firm working to create cell therapies that target severe conditions with high unmet medical needs. The company uses a proprietary cell-based platform to develop its therapies and can create lines of terminally-differentiated human cells capable of supporting or replacing cells that are dysfunctional or absent due to injury or degenerative disease. The company’s drug candidates, designed on this platform, assist the natural immune system in putting up an effective defense against a wide range of conditions.

Lineage currently has five candidates in its research pipeline. Three of these are in preclinical stages – but the other two are undergoing human clinical trials. Of these, the more advanced is OpRegen, an ophthalmological drug being tested in the treatment of dry AMD with geographic atrophy.

Lineage is developing this drug in a partnership agreement with Roche affiliate Genentech, which promises lucrative future royalties. For now, the key developments revolve around upcoming data releases. Study results from the Phase 1/2a clinical trial, covering 24 months of testing, are set for release at the 2024 Retinal Cell & Gene Therapy Innovation Summit, scheduled for May 3. The company’s presentation will include long-term follow-up data from 10 out of 12 patients, covering anatomical and functional results.

OpRegen is the subject of an ongoing Phase 2a study on the optimization of subretinal surgical delivery. The study will evaluate safety and activity in up to 60 patients and began in March of last year. The trial is being run by Roche and interim data from the study is likely to be Lineage’s next major catalyst.

Covering Lineage for Baird, analyst Jack Allen is enthusiastic about OpRegen as the company’s main draw for investors. He writes of the program’s overall prospects, “We continue to believe OpRegen’s profile which features both positive anatomical and functional changes is differentiated in the GA space. For context, neither of the approved GA therapies have been shown to improve vision function (not even slow decline), rather they were primarily approved on studies focused on slowing lesion growth… With an estimated ~2.5M GA patient in the developed markets (US and EU) and no approved treatments for this disease we believe that Roche’s annual sales of this asset could easily top $4B, which could translate to over $500M in annual royalties to Lineage, a dynamic that should drive significant upside to Lineage’s valuation in the long term.”

“Moving forward, we remain positive on the potential for Lineage’s cell therapy pipeline and anticipate investor appreciation for the OpRegen opportunity will increase in the coming months as additional data are presented,” the analyst added.

To this end, Allen rates LCTX shares an Outperform (i.e. Buy), while his $5 price target shows his confidence in a robust 275% upside potential for the next 12 months. (To watch Allen’s track record, click here )

Overall, the Strong Buy consensus rating on Lineage’s shares is unanimous, based on 5 recent positive analyst recommendations. The shares are trading for $1.35 each, and the $5.40 average price target, even more bullish than the Baird view, suggests that a one-year gain of ~303% lies ahead. (See LCTX stock forecast )

MDxHealth ( MDXH )

From biopharmas, we’ll switch over to medical diagnostics. MDxHealth is another micro-cap firm in the healthcare sector, but rather than focus on treatment options, its work is aimed at early detection. The company is working to commercialize precision diagnostics that provide medical professionals and patients with actionable molecular information needed to create personalized treatment plans.

MDxHealth does this by using proprietary genomic, epigenetic, and other molecular technologies to develop accurate tests to make diagnoses and prognoses for urologic cancers and other urologic diseases. The company has several diagnostic tests on the market, including three focused on prostate cancer and one on urinary tract infections.

The company’s two main products are the Select mdx test for prostate cancer, used pre-biopsy, and the Confirm mdx test for prostate cancer, used post-biopsy. The first is used as a screener for aggressive prostate cancer. It is a urine test, proven to be highly predictive, given to men at risk for the disease before initial biopsies are conducted. The second test is given after a negative biopsy and is used to identify patient populations with clinically significant, but undetected, prostate cancer.

This company recently released its financial results for 4Q23 and showed sound revenues and a deeper-than-anticipated earnings loss. At the top line, the company’s quarterly revenue of $19.39 million was up 50% year-over-year – and it skated over the forecast by just under half a million dollars. At the bottom line, the company’s GAAP EPS of 39 cents missed the forecast by 8 cents per share. We should note here that MDxHealth’s revenues have been on an upward trend since the beginning of last year.

For BTIG analyst Mark Massaro, this stock shows clear potential in a growth field. Massaro writes of the company, “MDx Health executed nearly flawlessly operationally in 2023 and announced it is evaluating a number of growth opportunities in the form of new partnerships or possible acquisitions… MDXH delivered another strong quarter as it drove a Q4 revenue beat and reiterated that its 2024 revenue guide is above where we/the Street were modeling as they guided for +12-15% Y/Y growth. We think there is upside to this guide and for context, MDXH drove +89% Y/Y revenue growth in 2023 and +42% Y/Y organically. We view MDx Health as an attractive small-cap growth and value stock and a one-stop-shop in prostate diagnostics testing, and trades at just ~1.1x our 2025 rev estimate of $90M, below peer historical averages of ~3-7x.”

Looking ahead, Massaro quantifies his stance with a Buy rating and an $8 price target that suggests a 221% upside potential on the one-year horizon. (To watch Massaro’s track record, click here )

All in all, there are 3 recent analyst reviews of this micro-cap stock, and they are all positive – giving the shares a unanimous Strong Buy consensus rating. The stock’s $7 average target price and $2.49 trading price together imply a one-year upside of 181%. (See MDxHealth stock forecast )

To find good ideas for stocks trading at attractive valuations, visit TipRanks’  Best Stocks to Buy , a tool that unites all of TipRanks’ equity insights.

Disclaimer: The opinions expressed in this article are solely those of the featured analysts. The content is intended to be used for informational purposes only. It is very important to do your own analysis before making any investment.

The views and opinions expressed herein are the views and opinions of the author and do not necessarily reflect those of Nasdaq, Inc.

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