Visualising Data
What will this topic cover?
This topic forms part of a wider learning pathway and is designed to help you explore fundamental digital skills and review how you can use them to enhance your daily working practices and approaches. This learning topic, within the Intro to Digital Literacy pathway, introduces you to the concept of data literacy and using key systems within the University.
This topic will focus on working with data in a visual format which can be useful for reporting and feedback on the data collected. We will go through some common ways of visualising data and give you some ideas about how this can be achieved using some of the software we have available.
By the end of this topic, you will be able to:
How to use this topic page
This topic page is split up into different sections. Each section has a step and an activity to complete. These include scenarios and links off to instructions to try elements for yourself. Each learning unit also has a reflective section to think about how this will be used within your own practice.
Step 1: Why do we need to visualise data?
Visualising data through techniques such as scatter plots, bar charts, and line graphs helps make your data easier to understand and interpret. These tools help highlight trends, patterns, and outliers. By telling a story with data, you can communicate insights more effectively and support data-driven decision-making. Additionally, enhancing accessibility by using clear, well-labelled visuals and providing alternative text descriptions ensures that everyone, regardless of their abilities, can benefit from the insights presented.
Why should I use visualisations of data?
- Simplify complex data into understandable and easier to read formats.
- Supports quicker decision making by highlighting key points of conclusions.
- Makes it easier to identify trends and outliers.
- Helps support a narrative to explain what you have discovered.
Considerations for visualisation of data
Whilst visualisations can help with data explanation, there are several things to consider when creating them.
- Tailor your visualisation to meet the needs of your audience, not all visualisations give the same information, so choosing the correct one can be vital in highlighting your key points.
- Keep visualisations simple and provide a summary of the main points. This will help tie your narrative and visuals together.
- Use clear labels and legends and highlight key data points where possible.
- Use Alt-text and summaries to help with accessibility (Digital Education | Accessible tables and graphs | Web).
Activity 1: Reflect & Apply
Thinking within your role or department, do you have regular data that is presented? Do any of these groups use visualisations to help support key points?
Think of some reports that have been generated and reflect on the following:
- Has the data been visualised to help with understanding the meaning?
- If so, what visualisations have been used and does it give you the full picture of what you need?
- Do any visualisations have summaries and alt-text available to help staff and students understand the content?
- If no visuals have been used, looking at the data do you think visuals would help you understand this in context?
Make a note of these approaches, as we will come back to some of them in the next step.
Step 2: An overview of different visualisation types
This step looks at some common visualisation types that are often used in reports, with an overview of which situations they are useful for.
Scatter plots display the relationship between two variables by plotting data points on a two-dimensional graph.
© Image originally created by Copilot
Best Uses:
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- Correlation Analysis: Identify the strength and direction of the relationship between two variables.
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- Outlier Detection: Spot outliers or unusual data points that deviate from the overall pattern.
Use Case:
Analysing the relationship between study hours and student grades. A scatter plot can show whether more study hours are associated with higher grades (data provided for illustrative purposes only).
Line graphs display data points connected by straight lines, showing trends over time.
© Image originally created by Copilot
Best Uses:
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- Trend Analysis: Track changes in a variable over time.
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- Forecasting: Predict future values based on historical trends.
Use Case:
Visualising enrolment trends over the past decade. A line graph can show whether enrolment is increasing, decreasing, or remaining stable over time.
Area charts display quantitative data graphically by plotting data points and filling the area below the line. They are similar to line charts but emphasise the magnitude of change over time by filling the space between the line and the x-axis.
© Image originally created by Copilot
Best Uses:
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- Trend Analysis: Show changes in a variable over time, highlighting the magnitude of change.
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- Cumulative Data: Visualise cumulative data to see how individual parts contribute to the whole.
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- Comparing Trends: Compare multiple data series to see how they change relative to each other over time.
Use Case:
Analysing Website Traffic: An area chart can be used to compare the number of visitors to a website over different months. By filling the area below the line, it highlights the total number of visitors and makes it easy to see trends and patterns in website traffic over time.
Bar charts compare different categories by displaying rectangular bars with lengths proportional to the values they represent.
© Image originally created by Copilot
Best Uses:
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- Category Comparison: Visualise the differences across your data set.
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- Trend Analysis: Show changes in a variable over time when using a grouped or stacked bar chart.
Use Case:
Comparing average grades across different extracurricular activities. A bar chart can highlight which activities are associated with higher or lower average grades.
Combo charts combine two or more chart types (e.g., bar, line) into a single chart. This allows for the visualisation of different data series with varying scales and types, providing a comprehensive view of the data.
© Image originally created by Copilot
Best Uses:
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- Multiple Data Series: Display multiple data series with different chart types to highlight various aspects of the data.
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- Dual Axes: Use dual axes to compare data series with different scales.
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- Enhanced Insights: Combine different chart types to provide a more detailed and insightful analysis.
Use Case:
A combo chart can be used to compare historical student grades over time. For example, a column chart can represent the average grades for each academic year, while a line chart can show the overall grade trend. This combination helps to visualise the relationship between semester performance and overall academic progress, providing a clearer understanding of student achievement (figures above are purely illustrative).
Stacked bar charts display the total value of different categories by stacking rectangular bars on top of each other. Each segment of the bar represents a sub-category, with the length proportional to its value.
© Image originally created by Copilot
Best Uses:
- Category Comparison: Compare the total values of different categories while also showing the contribution of sub-categories.
- Proportional Analysis: Visualise the proportion of sub-categories within the total for each main category.
- Trend Analysis: Show changes in the composition of categories over time when using a stacked bar chart with a time axis.
Use Case:
Analysing Project Budget: A stacked bar chart can be used to compare the total budget allocation across different projects while also showing the breakdown of expenses (e.g., licence costs, materials, staff costs) within each project.
Pie charts display the proportions of a whole by dividing a circle into slices.
© Image originally created by Copilot
Best Uses:
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- Proportion Analysis: Show the relative sizes of parts to a whole.
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- Distribution: Visualise the distribution across your dataset.
Use Case:
Displaying the distribution of students across different schools or programmes. A pie chart can show which services have the highest and lowest student numbers
Doughnut Charts compare different categories by displaying segments of a circle, with each segment’s arc length proportional to the value it represents. The centre of the circle is cut out, giving it a doughnut-like appearance.
© Image originally created by Copilot
Best Uses:
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- Category Comparison: Compare the values of different categories in a visually appealing way.
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- Proportion Representation: Show the proportion of each category relative to the whole.
Use Case:
Comparing student enrolment across different UK regions. A doughnut chart can highlight which activities are associated with higher or lower recruitment (figures shown are purely illustrative and do not represent any real recruitment figures).
Histograms display the distribution of a single variable by grouping data into bins and showing the frequency of each bin.
© Image originally created by Copilot
Best Uses:
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- Frequency Distribution: Understand the distribution of data points within a dataset.
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- Data Spread: Identify the spread and central tendency of data.
Use Case:
Analysing the distribution of student grades in a course. A histogram shows how grades are distributed, indicating whether most students scored high, low, or somewhere in between.
Box plots (also called Box and Whisker plots) display the distribution of a dataset based on five summary statistics: minimum, first quartile, median, third quartile, and maximum.
© Image originally created by Copilot
Best Uses:
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- Data Distribution: Understand the spread and skewness of data.
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- Outlier Detection: Identify outliers and extreme values.
Use Case:
Comparing the distribution of grades across different classes. A box plot can show the range, median, and variability of grades, highlighting any outliers.
Funnel charts display the progressive reduction of data as it passes through sequential stages in a process. The chart is shaped like a funnel, with the widest part at the top representing the initial stage and the narrowest part at the bottom representing the final stage.
© Image originally created by Copilot
Best Uses:
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- Process Visualisation: Visualise the stages of a process and the drop-off at each stage.
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- Conversion Analysis: Analyse conversion rates at each stage of a sales or marketing funnel.
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- Identifying Bottlenecks: Identify stages where there is a significant drop-off, indicating potential bottlenecks or areas for improvement.
Use Case:
Analysing Student Enrolment: A funnel chart can be used to visualise the stages of the student enrolment process, from initial inquiries to final enrolment. This helps in identifying stages with high drop-off rates and areas where improvements can be made to increase enrolment rates.
Radar charts compare multiple variables by displaying them on axes that radiate from a central point. Each variable is represented by a spoke, and data points are plotted along these spokes, creating a polygon.
© Image originally created by Copilot
Best Uses:
- Performance Analysis: Compare performance across different categories or metrics.
- Multivariate Data: Visualise and compare multiple variables simultaneously.
- Identifying Strengths and Weaknesses: Highlight areas of strength and weakness in a dataset.
Use Case:
Comparing Student Performance Across Subjects: A radar chart can be used to compare student performance across different subjects. Each spoke represents a subject, and the data points show the performance in each subject, helping to identify areas where students excel or need improvement.
Summary
Each visualisation technique in Microsoft 365 has its own strengths and best uses:
- Scatter Plots: Great for analysing relationships and spotting outliers.
- Line Graphs: Best for tracking changes and forecasting trends.
- Area Charts: Great for showing cumulative totals over time and highlighting the magnitude of change.
- Bar Charts: Ideal for comparing categories and showing trends over time.
- Combo Charts: Useful for combining different chart types to compare multiple data sets and highlight different aspects of the data.
- Stacked Bar Charts: Useful for comparing total values and the contribution of sub-categories.
- Pie Charts: Useful for showing proportions and category distribution.
- Doughnut Charts: Similar to pie charts but with a hole in the centre, useful for showing proportions and category distribution in a visually appealing way.
- Histograms: Excellent for understanding frequency distribution and data spread.
- Box Plots: Perfect for visualising data distribution and identifying outliers.
- Funnel Charts: Ideal for visualising process stages and identifying drop-off points.
- Radar Charts: Excellent for comparing multiple variables and visualising performance across different categories.
Activity 2: Try it yourself
Thinking about common data in your area, pick one or two examples and looking through the most common visualisations from Step 2.
- Which type do you think would suit your current data set and why?
- What will help you explain or show?
Step 3: Generating visualisations
This step focusses on the practicality of adding visualisations within common software. We will be focusing on Forms and Excel.
Using Microsoft Forms to show basic visualisations.
The visualisation feature in Microsoft Forms is a powerful tool that enhances data analysis and reporting. By transforming raw data into visual summaries like charts and graphs, it provides quick insights and helps identify trends and patterns effortlessly. This feature not only improves understanding and interpretation of the data but also streamlines the reporting process, making it easier to create compelling presentations and documents. Ultimately, it supports better decision-making by highlighting key information clearly and efficiently.
One element which is useful within Forms is to be able to create a summary report which holds key information and some visualisations to help highlight trends. This can be useful as a quick overview to share from the live data within the form.
See Activity 3 for guides to try it for yourself.
Using Excel for Generating Charts
Excel offers powerful data visualisation features that enhance data comprehension and decision-making. With a variety of charts and graphs, PivotTables and PivotCharts, conditional formatting, sparklines, and in-cell visual tools like data bars and colour scales, Excel makes it easy to identify trends, patterns, and anomalies. These tools not only make complex data more accessible but also improve communication of insights to stakeholders, leading to more informed and efficient decision-making. The visualisation approach for excel is varied and with the variety of visuals available it’s important to think about the type you want to display and the information that will be shown.
Try Activity 3 to have a go yourself.
Activity 3: Try it for Yourself
For this activity we would recommend you use some fake data (see provided Excel file which can be downloaded for your own use).
The guides below will give you step by step instructions on how to generate visualisations within these key areas.
Step 4: Reflection
What have I learnt from this learning topic?
This step is designed to help you think about what you have learned and how this applies to your own practice and context. Learning Activity 4 will ask you some questions to help you with this reflection.
Activity 4: Reflect
Use the following questions to help you think about your own practice.
- Can you think of any instances in your work where you would benefit from using data visualtion?
- Which types of visualisation best suits your needs?
- Do you have any current practices around data visualisation? Can these practices be improved?
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