presentation and interpretation of data ppt

Home Blog Design Understanding Data Presentations (Guide + Examples)

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.

A data presentation is a slide deck that aims to disclose quantitative information to an audience through the use of visual formats and narrative techniques derived from data analysis, making complex data understandable and actionable. This process requires a series of tools, such as charts, graphs, tables, infographics, dashboards, and so on, supported by concise textual explanations to improve understanding and boost retention rate.

Data presentations require us to cull data in a format that allows the presenter to highlight trends, patterns, and insights so that the audience can act upon the shared information. In a few words, the goal of data presentations is to enable viewers to grasp complicated concepts or trends quickly, facilitating informed decision-making or deeper analysis.

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 and our article about how to make a presentation graph .

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

Like this article? Please share

Data Analysis, Data Science, Data Visualization Filed under Design

Related Articles

Filed under Design • March 27th, 2024

How to Make a Presentation Graph

Detailed step-by-step instructions to master the art of how to make a presentation graph in PowerPoint and Google Slides. Check it out!

Filed under Presentation Ideas • January 6th, 2024

All About Using Harvey Balls

Among the many tools in the arsenal of the modern presenter, Harvey Balls have a special place. In this article we will tell you all about using Harvey Balls.

Filed under Business • December 8th, 2023

How to Design a Dashboard Presentation: A Step-by-Step Guide

Take a step further in your professional presentation skills by learning what a dashboard presentation is and how to properly design one in PowerPoint. A detailed step-by-step guide is here!

Leave a Reply

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

• We're Hiring!
• Help Center

Presentation, Analysis and Interpretation of data

Related Papers

Shanel Bautista

Adeel Ahmad Khan

Data are collected often in raw form. These are then not useable unless summarized. The techniques of presentation in tabular and graphical forms are introduced. Some illustrations provided are real-world examples. Graphical presentations cover bar chart, pie chart, histogram, frequency polygon, pareto chart, frequency curve and line diagram. Data are often collected in raw form. These are then not useable unless summarized. There are certain guidelines for data summarization such as summarization-should be as useful as possible,-should represent data fairly, and-should be easy to interpret. After collection of data (primary or secondary), it is necessary to summarize them suitably and present in such forms as can facilitate subsequent analysis and interpretation. There are two major tools/techniques for presentation of data as follows:-Presentation in tabular form-Presentation in graphical form. 2.1 Tabular Presentation Data may be presented in the form of statistical tables. In one table only simple frequencies can be shown. Also, in the same table cumulative frequencies, relative frequencies, and cumulative relative frequencies can be shown. Relative frequencies and cumulative frequencies are defined as follows: Relative frequency: It means the ratio of the frequency in the category of concern to the total frequency in the reference set.

Instrumental Analyses of Pollutants, Elsevier Science …

Adedeji Badiru

Chris Isokpenhi

lorenzo ruiz costo

Heewon Chang

Journal of the Royal Statistical Society: Series A (Statistics in Society)

Richard Heiberger

joshua pantaleon

John Paul Bentic

International Journal of Higher Education

Ida Kukliansky

Loading Preview

Sorry, preview is currently unavailable. You can download the paper by clicking the button above.

RELATED PAPERS

Forum of Mathematics, Sigma

Ilya Dumanski

https://www.elivapress.com/pl/authors/author-3932421560/

Zakaria H E R E S H Qaderi

Journal of Technology Management & Innovation

FERNANDO ANTONIO FERNANDO ANTONIO RIBEIRO SERRA

Scientific Investigations Report

John Masterson

Jorma Paavonen

Soumya Zachariah

Proceedings of International Symposium on Earth Resources Management & Environment

Chulantha Jayawardena

Joseph Kell

IRJET Journal

Folklor Edebiyat

AYNUR KOÇAK

Langston Hughes in Context

Juan Rodriguez Barrera

Pierre Kestener

Enfermagem em Foco

Abigail Andrade

Vladimir Tesar

Odontología Activa Revista Científica

LIZET J MATUTE ORTIZ

Revista de filosofia dianoia

Federico Ast

Religion Compass

Glenn Bowman

Agen Tas Delivery Ikan SegarWA/TELP : 0822-3006-6162, Harga Tas Delivery Bunga, Harga Tas Delivery Florist, Harga Tas Delivery Cafe Dan Resto

fitri aisyah rahmania

offer录取通知书办美国大学， 学，毕业证办加拿大大学，

Vjesnik Arheološkog muzeja u Zagrebu

Ante Rendić-Miočević

Aftie K Hakimi

Environmental Engineering Research

MD.milon Hossain

RELATED TOPICS

•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024

Researched by Consultants from Top-Tier Management Companies

Powerpoint Templates

Icon Bundle

Kpi Dashboard

Professional

Business Plans

Swot Analysis

Gantt Chart

Business Proposal

Marketing Plan

Project Management

Business Case

Business Model

Cyber Security

Business PPT

Digital Marketing

Digital Transformation

Human Resources

Product Management

Artificial Intelligence

Company Profile

Acknowledgement PPT

PPT Presentation

Reports Brochures

One Page Pitch

Interview PPT

All Categories

Top 10 Data Analysis Templates with Samples and Examples

Mohammed Sameer

If people could eat data instead of food, we could end world hunger with enough spare data left over to tackle 3 famines.

This startling but obvious statement underscores the abundance of data available to the human race today and the humungous rate at which it has grown in our digital age. Just as sustenance nourishes our bodies, data fuels our intellect, satiating the hunger for insights and understanding. 

Data is the foundation upon which the structure of information stands tall. Imagine gazing at a puzzle's scattered pieces – each is important, might be beautiful and vital, but the true picture emerges only when the pieces interlock. Similarly, data is the root of knowledge for today’s businesses. Our new Data Analysis Templates are the masterful hands that bring all that scattered knowledge and wisdom together.

These PPT Presentations emerge as essential companions in a landscape where accurate decision-making means the difference between thriving and surviving. Understanding data is pivotal in the symphony of business strategies, marketing endeavors, and research pursuits. 

The 100% customizable nature of the templates provides you with the desired flexibility to edit your presentations. The content-ready slides give you the much-needed structure.

Let’s explore!

Template 1: Data Analysis Process PPT Set

Use this PPT Set to help stakeholders understand difficulties that mar the data analysis process and gain valuable insights. Explore the crucial stages of data analysis, from establishing data requirements and efficient data collection to thorough data processing and cleaning. This PPT Design highlights the often underestimated yet pivotal phase of data cleaning. With this template, you'll understand how data lays the foundation for seamless analysis, leading to more accurate results and impactful communication. Download now!

Download this template

Template 2: Data Analysis Business Evaluation Process for Visualization and Presentation

This holistic PPT Bundle guides you through the complex stages of visualization and presentation while offering a profound understanding of each crucial phase. Use this presentation template to understand the essence of successful data analysis, as it breaks down the process into digestible segments. From the initial steps of business issue comprehension and data understanding to data preparation, exploratory analysis, monitoring, validation, and finally, captivating visualization and presentation – every facet is covered. This PPT Preset goes beyond mere process explanation, offering a robust framework for the holistic development of data conceptualization, collection, analysis, and cleaning procedures. Get it today!

Get this template

Template 3: Data Requirement Analysis PPT Bundle

Navigating challenges of problem-solving, prioritization, and data insight, this PPT Presentation presents a strategic roadmap that transforms raw information into actionable intelligence. It starts with a deep dive into the heart of your business challenges. Focusing on defining the core problems, this presentation template guides you through the process of setting priorities, ensuring every move is a step closer to your objectives. Data collection, a crucial cornerstone, is explained through insightful visual aids and organized segments. Witness the transformation of disparate data points into a coherent narrative, empowering you to decipher trends, anomalies, and opportunities.

This PPT Template equips you with the tools to not only gather data but also comprehend its implications, turning information into true knowledge. Navigating the challenges of data requirement analysis is no longer a daunting task. From security gaps that demand attention to complex data systems that require expertise, our template ensures you're prepared to overcome these hurdles with confidence. The high costs that often come with data analysis are confronted head-on, unraveling budget-friendly strategies that don't compromise on quality. Get this template today!

Grab this template

Template 4: Big Data Analysis PPT Set

This comprehensive PPT Deck presents a pre-made Big Data Analysis funnel that guides you through the rather complex process of turning data into gold. Gain a competitive edge by understanding effective data analysis techniques of association rule learning, classification tree analysis, genetic algorithm, regression analysis, and sentiment analysis. It's more than a run-of-the-mill PPT Presentation; it's a transformative tool. Invest in a big data analysis PPT like resource that's not just about graphs and numbers; get it now. Download now!

Template 5: Data Management Analysis PPT Framework

For achieving business excellence, the quest for efficient and time-saving solutions is a universal endeavor. Recognizing your aspirations, we present the Data Management Analysis PowerPoint Presentation — an invaluable asset for seamless change management and effective data analysis. It incorporates PPT Slides designed to provide an effortless avenue for embracing change management and conducting incisive data analysis. It offers a cohesive platform for centralizing your objectives, ready to be shared with your team. The judicious use of text boxes empowers you to articulate your perspectives with precision on each pertinent subject. Download today!

Template 6: Predictive Data Analysis PPT Layout

Get this PPT Preset to consolidate your stakeholder's grasp on predictive analytics, a discipline that uses statistical methodologies, cutting-edge machine learning algorithms, and a suite of tools to dissect historical data. This PPT Layout guides you through a well-structured journey, unfolding the essentials of predictive analytics, its foundational framework, and a suite of models that constitute its core. The significance of predictive analytics takes center stage, underscored by its multifaceted applications. Additionally, this resource has an Estimation Model PPT Slide, which explains the key tenets of diverse predictive analytics tools and their closely-knit workflows. The demarcation between the four pivotal categories of advanced analytics in this PPT deck receives careful attention. It sheds light on predictive analytics models – from classification to clustering models and beyond. Download now!

Template 7: Dashboard For IT Operations Data Analysis

This PPT Template Dashboard is a dynamic representation of your operational landscape. This PPT Set helps track the total number of cases from inception to resolution. Visualize trends with a graph showcasing the weekly ebb and flow of opened and closed cases. Prioritize effectively, allocating resources where they matter most, as the presentation template depicts it across departments. Efficiency meets clarity as you explore the time distribution of tickets on a day-by-day basis. Gain a better understanding of workflow patterns and resource utilization. Analyze open case statuses, fostering an environment of proactive response and swift action. Download now!

Template 8: Quarterly Sales Data Analysis Report

Visualize your progress with ease using this PPT Template's intuitive presentation of monthly sales data. Get a clear view of team-wise statistics that showcase individual contributions, fostering a culture of recognition and growth. Uncover finer details through the nuanced comparison of total versus actual sales values, empowering you to identify trends and opportunities. Engage stakeholders in strategy evaluation as you assess team goals versus actual achievements. Pinpoint areas of excellence and those warranting attention, refining your approach. Download now!

Template 9: Real-Time  Marketing Data Analysis

Here's a dynamic marketing analysis tool blending insights and aesthetics. It presents a pie chart comparing planned vs. actual budgets while diving deep into sections showcasing real-time marketing benefits: Elevated customer experiences, surging conversions, enhanced retention, and refined brand perception. Navigate budget allocation through intuitive bar graphs. Improve your strategy with data symphony, moving a step closer to success through informed choices. Download now!

Template 10: Data Analysis Process for Visualization and Presentation

Embark on a data-driven journey with this PPT Set. Learn the process of Data Analysis, Visualization, and Presentation to address complex business challenges. This PPT Design walks you through these stages, from issue identification and data preparation to exploratory analysis modeling. Witness raw data transform into insights through rigorous validation. Culminate in captivating visualizations and masterful presentations, setting new standards for impactful communication. Download now!

Bridging Numbers and Narratives: Your Journey Through Data Analysis

In a world where data weaves the fabric of progress, our journey through this blog comes to an inspiring end. As you venture into data analysis armed with our templates, remember that each graph, each layout, and each piece of information is a brushstroke on the canvas of understanding. With every mouse click, you’re not just navigating slides; you're charting the course for informed decisions, breakthrough discoveries, and transformative strategies.

FAQs on Data Analysis

What is data analysis.

Data analysis involves inspecting, cleansing, transforming, and modeling data to derive meaningful insights, draw conclusions, and support decision-making. It encompasses various techniques, including statistical methods, machine learning, and visualization, to uncover patterns, trends, and relationships within datasets.

What are the four types of data analysis?

There are four main types of data analysis:

• Descriptive Analysis: This type of analysis focuses on summarizing and describing the main features of a dataset. It involves statistical measures such as mean, median, mode, range, and standard deviation. Descriptive analysis aims to clearly understand the data's characteristics but doesn't involve drawing conclusions or making predictions.
• Diagnostic Analysis: Diagnostic analysis involves digging deeper into data to understand why certain patterns or outcomes occurred. It aims to identify the root causes of specific events or trends. Techniques used in diagnostic analysis often include data visualization, exploratory data analysis, and statistical tests to uncover relationships and correlations.
• Predictive Analysis: Predictive analysis involves using historical data to predict future events or outcomes. This type of analysis uses statistical models, machine learning algorithms, and data mining techniques to identify patterns and trends that can be used to forecast future trends. It's widely used in finance, marketing, and healthcare for making informed decisions.
• Prescriptive Analysis: Prescriptive analysis goes beyond predicting future outcomes. It provides recommendations or solutions for specific situations based on historical and current data analysis. This type of analysis considers different possible actions and their potential outcomes to guide decision-making. Prescriptive analysis is often used in complex scenarios involving multiple variables and options.

Where is data analysis used?

Data analysis is used in a wide range of fields and industries, including but not limited to:

• Business: Analyzing customer behavior, market trends, and financial performance.
• Healthcare: Analyzing patient records, medical research data, and disease trends.
• Science: Analyzing experimental results, simulations, and observations.
• Finance: Analyzing investment trends, risk assessment, and portfolio management.
• Marketing: Analyzing campaign effectiveness, consumer preferences, and market segmentation.
• Social Sciences: Analyzing survey data, demographic trends, and human behavior.
• Sports: Analyzing player performance, game statistics, and strategy optimization.

What is the main tool for data analysis?

There isn't a single "main" tool for data analysis, as the choice of tools depends on the specific tasks and the preferences of the analyst. However, some widely used tools for data analysis include:

• Spreadsheet Software: Like Microsoft Excel or Google Sheets, used for basic data manipulation and visualization.
• Statistical Software: Such as R and Python's libraries (e.g., pandas, numpy, scipy), used for in-depth statistical analysis and modeling.
• Data Visualization Tools: Like Tableau, Power BI, or matplotlib/seaborn in Python, used to create visual representations of data.
• Database Management Systems (DBMS): Such as SQL-based systems for querying and managing large datasets.
• Machine Learning Libraries: Such as scikit-learn, TensorFlow, and PyTorch for building predictive models.

Why is data analysis important?

Data analysis is crucial for several reasons:

• Informed Decision-Making: It provides insights that help individuals and organizations make informed decisions based on evidence rather than intuition.
• Identifying Patterns and Trends: It helps to uncover hidden patterns, trends, and correlations in large datasets that might not be apparent on the surface.
• Problem Solving: Data analysis aids in solving complex problems by providing a structured approach to understanding and addressing issues.
• Improving Efficiency and Performance: It allows businesses to optimize processes, improve efficiency, and enhance performance based on data-driven insights.
• Innovation and Research: Data analysis is essential in scientific research and innovation, helping to validate hypotheses and drive discoveries.
• Competitive Advantage: Organizations that effectively use data analysis gain a competitive edge by better understanding their customers, markets, and internal operations.
• Risk Management: Data analysis enables better risk assessment and management by identifying potential issues or anomalies early on.
• Resource Allocation: It helps allocate resources effectively by understanding where investments are most likely to yield positive outcomes.

Related posts:

• How Financial Management Templates Can Make a Money Master Out of You
• How to Design the Perfect Service Launch Presentation [Custom Launch Deck Included]
• Quarterly Business Review Presentation: All the Essential Slides You Need in Your Deck
• [Updated 2023] How to Design The Perfect Product Launch Presentation [Best Templates Included]

Liked this blog? Please recommend us

Top 20 Big Data and Analytics Templates for Machine Learning, Cloud Computing and Artificial Intelligence PPT Presentations

Top 10 Data Security Management Templates to Safeguard Your Business (Free PDF Attached)

This form is protected by reCAPTCHA - the Google Privacy Policy and Terms of Service apply.

Digital revolution powerpoint presentation slides

Sales funnel results presentation layouts

3d men joinning circular jigsaw puzzles ppt graphics icons

Business Strategic Planning Template For Organizations Powerpoint Presentation Slides

Future plan powerpoint template slide

Project Management Team Powerpoint Presentation Slides

Brand marketing powerpoint presentation slides

Launching a new service powerpoint presentation with slides go to market

Agenda powerpoint slide show

Four key metrics donut chart with percentage

Engineering and technology ppt inspiration example introduction continuous process improvement

Meet our team representing in circular format

• SUGGESTED TOPICS
• The Magazine
• Newsletters
• Managing Yourself
• Managing Teams
• Work-life Balance
• The Big Idea
• Data & Visuals
• Reading Lists
• Case Selections
• HBR Learning
• Topic Feeds
• Account Settings
• Email Preferences

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

Partner Center

Chapter 8 Data Evaluation and Interpretation

Dec 20, 2019

150 likes | 174 Views

Chapter 8 Data Evaluation and Interpretation. Learning Objectives. Appreciate the role of critical thinking in evaluating research findings Assess the dependability of information sources (print and digital) Assess the quality of your evidence

Share Presentation

Presentation Transcript

Chapter 8Data Evaluation and Interpretation

Learning Objectives • Appreciate the role of critical thinking in evaluating research findings • Assess the dependability of information sources (print and digital) • Assess the quality of your evidence • Interpret your findings accurately and without bias • Understand that “certainty” in research is an elusive goal

Learning Objectives (continued) • Recognize common errors in reasoning and statistical analysis • Understand that research carries the potential for error

Evaluating and Interpreting Not all information is equal. Not all interpretations are equal either:

Evaluate the Sources Start the evaluating and interpreting process by evaluating your sources: • Determine the currency of the source. • Assess the reputation of a printed source. • Assess the perspective of a digital source. • Consider the possible motives of those who have funded astudy. • Cross-check the source against other, similar sources.

Evaluate the Evidence Then evaluate the evidence. Evidenceis any finding used to support or refute a particular claim: • Determine the sufficiency of the evidence. • Differentiate hard from soft evidence. Hard evidence is facts, expert opinion, and statistics; soft evidence is uninformed opinion and speculation. • Decide whether the presentation of evidence is balanced and reasonable. • Consider how the facts are being framed.

Interpret Your Findings Then try to uncover the truth of your findings: • Identify your level of certainty. Is it a conclusive answer (irrefutable), the probable answer (likely), or an inconclusive answer (uncertain)? • Examine the underlying assumptions. Assumptions are ideas we often accept without proof. Does the information you located make assumptions? • Be alert to personal bias. Does the information you’ve found seem biased? • Consider other possible interpretations. Is there another way you can look at what you’ve found?

Avoid Distorted or Unethical Reasoning Next apply reason to the information you’ve found by seeing if it contains errors in reasoning, such as the following logical errors: • Faulty generalization. Generalizing on the basis of limited evidence. Ask yourself: “How much can we generalize from these findings?” • Faulty causal reasoning. Confusing or distorting why one thing caused or will cause another thing to happen. Ask yourself: “Did X possibly, probably, or definitely cause Y?”

Avoid Distorted or Unethical Reasoning (continued) • Faulty statistical analysis. Interpreting the numbers in a misleading way. Ask yourself: “Are these numbers really accurate?” Avoid: * sanitized statistics (numbers that have manipulated to obscure the facts) * meaningless statistics (quantifying things that really can’t be quantified) * undefined averages (averages that are mathematically skewed) * distorted percentage figures (percentages that aren’t mathematically valid or ignore the margin or error)

Avoid Distorted or Unethical Reasoning (continued) * bogus rankings (items compared on the basis of ill-defined criteria) * confusion of correlation and causation (misinterpreting coincidence as truth) * biased meta-analysis (using only findings from other studies that support your own bias) * fallible computer model (trusting a computer with biased assumptions programmed in) * misleading terminology (using statistical terms that hide meaning)

Acknowledge the Limits of Research Lastly, understand that there are limits to even the most careful research you use to support your own conclusions. • Not all research is valid (correct) and reliable (repeatable). Even a valid survey can lead to invalid results when respondents misunderstand survey questions, answer questions dishonestly, or respond in a way that the think they ought to. Even a reliable survey can become unreliable when the same respondents answer the same survey questions differently the second time.

Acknowledge the Limits of Research (continued) • Research studies can be flawed. Epidemiological studies (the study of populations), laboratory studies (studies conducted under laboratory conditions), and human exposure studies (clinical trials) can go wrong in a variety of ways. • Research can sometimes be deceptively reported. Sometimes researchers avoid reporting results that are awkward, embarrassing, or unpopular.

Review Questions 1. What is the difference between evaluation and interpretation of information? 2. What are the five ways to evaluate a source? 3. What are four ways to evaluate evidence? 4. What are hard evidence and soft evidence? 5. What are the four ways of interpreting your findings? 6. What are the three levels of certainty? 7. What is a faulty generalization?

Review Questions (continued) 8. What is faulty causal reasoning? 9. What are five types of faulty statistical reasoning? 10. In what three ways can research findings be limited?

• More by User

Data analysis and Interpretation

“Developing an understanding of the Independent Foster Care Sector in Northern Ireland, through the perspectives of Independent Foster Carers, Independent Foster Care Agencies and other key professionals”.

256 views • 7 slides

Interpretation and Evaluation

Interpretation and Evaluation . By Anton Mararenko. Interpretation and Evaluation. Interpretation and Evaluation are two great tools that help us understand the message behind a work of art. Together, they piece all the elements of the artwork together and allow us to judge them.

546 views • 23 slides

Interpretation/Evaluation of Laboratory Data

Interpretation/Evaluation of Laboratory Data. CLS 552 Human Microbiology & Immunology Laboratory. Note: View this PowerPoint as a ‘Full Screen Slide Show’. In the right hand corner of the screen, click on the ‘Full Screen Slide Show’ icon. To advance, perform left-mouse click.

383 views • 14 slides

Data Interpretation

Data Interpretation. Charts. Includes……. Graphs. Tables. Diagrams & Figures. Step 1 – Scan the Passage. Bar Graphs :. Text: very little text SKIP the introduction Come back if problem with the graph. Step 2 – Scan the Graph. A “thing” that has some type of value.

955 views • 21 slides

Chapter 8: Biological Knowledge Assembly and Interpretation

Chapter 8: Biological Knowledge Assembly and Interpretation . Ju Han Kim Division of Biomedical Informatics, Seoul National University College of Medicine, Seoul, Korea, Presenter: Zhen Gao . Outline. Review of major computational approaches to facilitate biological interpretation of

701 views • 56 slides

Data Collection and Interpretation

Data Collection and Interpretation. A Chieng. Today’s workshop. Data collection Instrumentation Research questions Data analysis Interpretation of findings. What is your study?. Another case study? Developmental research?. Research questions Enough Types of instruments.

546 views • 34 slides

DATA INTERPRETATION

DATA INTERPRETATION. By Akash Vasant Thorat B.TECH (UICT), MBA (JBIMS), JAIIB Probationary Officer 2013 Commercial Branch – Fort, Mumbai. DI-SICS. Tables Byheart 1 -25 ( 13,17,19,23 compulsory) Squares 1-30 Cubes 1-12 Fractions 1-20. FRACTIONS. DI (contd..).

388 views • 24 slides

Data and Interpretation

Data and Interpretation. What have you learnt?. The delver into nature’s aims Seeks freedom and perfection; Let calculation sift his claims With faith and circumspection -Goethe.

474 views • 34 slides

Chapter 6: Data Analysis and Interpretation

Chapter 6: Data Analysis and Interpretation. Data Analysis and Interpretation. After today’s activity you should be able to: Define data analysis and data interpretation Identify appropriate data analysis techniques for your action research project

3.65k views • 32 slides

Chapter 13 Quantitative Data Analysis and Interpretation

Chapter 13 Quantitative Data Analysis and Interpretation. Chapter Objectives. edit questionnaire and interview responses handle blank responses set up the coding key for the data set and code the data categorise data and create a data file

442 views • 19 slides

Data Interpretation. Dr Amna R Siddiqui & Professor Ahmed Mandil CMED 305. Objectives. To describe interpretation of epidemiological data To classify the sub-group analysis based on hypothesized -exposure/risk/determinant with the -outcome/factor per study objectives

455 views • 26 slides

For more details follow: www.catking.in

171 views • 9 slides

Creative Biostructure now can provide a collection of tools and analysis package for biomolecular structure determination, refinement and analysis from crystallographic, EM or NMR data. In structural biology field, in order to reveal a dedicated and reliable structure of biomolecule, research data is acquired, interpreted, published, reused, and sometimes eventually discarded. Creative Biostructure helps the development of appropriate infrastructural services, ones which make it easier and time-saving for researchers to collect, read and process the data and obtain the expected and target structure results. https://www.creative-biostructure.com/Data-Processing-and-Interpretation-584.htm

36 views • 1 slides

Chapter 9 Data Analysis, Interpretation, and Presentation

Chapter 9 Data Analysis, Interpretation, and Presentation. Goals. Discuss the difference between qualitative and quantitative data and analysis Enable you to analyze data gathered from: Questionnaires Interviews Observation studies

594 views • 20 slides

274 views • 26 slides

Data Interpretation. Dr Amna R Siddiqui CMED 305 February 16, 2015. Objectives. To describe interpretation of epidemiological data To classify the sub-group analysis based on hypothesized -exposure/risk/determinant with the -outcome/factor per study objectives

257 views • 23 slides

Chapter 8 Data Analysis, Interpretation And Presentation

Chapter 8 Data Analysis, Interpretation And Presentation. Damodar Justus Yasmeen Kevin Kazuya Wanda. Qualitative And Quantitative. Data. Qualitative. Quantitative. Data that is difficult to measure, count or express in numerical terms in a sensible fashion.

154 views • 9 slides

214 views • 19 slides

DATA ANALYSIS AND INTERPRETATION

DATA ANALYSIS AND INTERPRETATION. Getting Data Ready for Analysis. Editing Data Open-ended questions Questionnaire data have to be checked for incompleteness and inconsistencies. Getting Data Ready for Analysis (Cont’d). Handling blank responses

220 views • 5 slides

Data Analysis PPT: Definition, Types and Process

Data Analysis PPT: Definition, Types and Process Free Download: Although many groups, organizations, and specialists have extraordinary approaches to approach data analysis, maximum of them may be distilled right into a one-size-fits-all definition.

Data analysis is the system of cleaning, changing, and processing raw facts, and extracting actionable, applicable records that enables corporations make knowledgeable decisions. The process enables lessen the dangers inherent in choice-making through imparting beneficial insights and statistics, regularly supplied in charts, images, tables, and graphs. A easy instance of facts evaluation may be visible on every occasion we take a choice in our every day lives through comparing what has occurred withinside the beyond or what is going to occur if we make that choice.

Table of Content

• Introduction
• Why is Data Analysis Important?
• Data Analysis Process
• Types of Data Analysis

Related Posts

Social media marketing ppt presentation seminar free, biomedical waste management ppt presentation free, monkey and the cap seller story ppt presentation free download, 1210 electrical engineering(eee) seminar topics 2024, 112 iot seminar topics-internet of thing presentation topics 2024.

330 Latest AI (Artificial Intelligence Seminar Topics) 2024

No comments yet, leave a reply cancel reply.

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed .

Got any suggestions?

We want to hear from you! Send us a message and help improve Slidesgo

Top searches

Trending searches

68 templates

33 templates

36 templates

34 templates

9 templates

35 templates

Data Analysis and Statistics - 9th Grade

Data analysis and statistics - 9th grade presentation, premium google slides theme and powerpoint template.

Download the "Data Analysis and Statistics - 9th Grade" presentation for PowerPoint or Google Slides. High school students are approaching adulthood, and therefore, this template’s design reflects the mature nature of their education. Customize the well-defined sections, integrate multimedia and interactive elements and allow space for research or group projects—the possibilities of this engaging and effective Google Slides theme and PowerPoint template are endless. Download this design to provide a logical and organized structure, allowing for the seamless flow of information.

Features of this template

• 100% editable and easy to modify
• Different slides to impress your audience
• Contains easy-to-edit graphics such as graphs, maps, tables, timelines and mockups
• Includes 500+ icons and Flaticon’s extension for customizing your slides
• Designed to be used in Google Slides and Microsoft PowerPoint
• Includes information about fonts, colors, and credits of the resources used

What are the benefits of having a Premium account?

What Premium plans do you have?

What can I do to have unlimited downloads?

Don’t want to attribute Slidesgo?

Gain access to over 22400 templates & presentations with premium from 1.67€/month.

Are you already Premium? Log in

Related posts on our blog

How to Add, Duplicate, Move, Delete or Hide Slides in Google Slides

How to Change Layouts in PowerPoint

How to Change the Slide Size in Google Slides

Related presentations.

Premium template

Unlock this template and gain unlimited access

Register for free and start editing online

Microsoft Power BI Blog

Supporting continuous slide shows in power bi storytelling add-in.

Reports that track performance data in Power BI are common for many organizations. These reports can cover different aspects of organizational performance, such as production, sales, engineering, and so on. Many organizations want to share these reports with everyone in the organization, and even make them publicly visible , so everyone can see what the leadership cares about and how the organization is doing.

PowerPoint lets you play a presentation continuously. This is handy when you want to show information on public screens without anyone having to control it. You can do it by setting your presentation to run in an infinite loop and set the slides transition.

But up until today, if the Power BI add-in was part of a presentation running in a continuous slide show, the data in the add-in might become outdated, since the add-in only gets the data from Power BI when the slide is loaded, or when the user manually refreshes the data in the add-in.

Therefore, we added a new configuration to the add-in that ensures that you get the latest data from Power BI when running in continuous slide show. When the slide-show configuration is turned on, the add-in automatically pulls data from Power BI, without anyone having to do anything, ensuring that the presentation always has the most recent data.

To enable automatic refresh in slide show, go to the add-in footer, select Add-in options , choose Slide show settings , check Automatic refresh in slide show and set the desired frequency. This will cause the add-in to pulls data from Power BI every given period, when the presentation is in slide show.

The auto refresh only happens in slide show mode and not while you’re editing the presentation, so don’t worry if data does not change we you are still editing the presentation. (If you want the latest data when you are editing, go to Data options and choose Refresh .)

For best performance, the device running the presentation in infinite loop must have Office build 17126.20208 and above .

Let us know your feedback about the new slide show support inPower BI Storytelling add-in at  ideas.fabric.microsoft.com . Please add “Storytelling” to the title and give us enough information to make improvements to meet your requirements.

• power bi data storytelling