Scatter plots display the relationship between two variables by plotting data points on a two-dimensional graph.

Best Uses:

• • Correlation Analysis: Identify the strength and direction of the relationship between two variables.

• • 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.

Best Uses:

• • Trend Analysis: Track changes in a variable over time.

• • 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.

Best Uses:

• • Trend Analysis: Show changes in a variable over time, highlighting the magnitude of change.

• • Cumulative Data: Visualise cumulative data to see how individual parts contribute to the whole.

• • 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.

Best Uses:

• • Category Comparison: Visualise the differences across your data set.

• • 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.

Best Uses:

• • Multiple Data Series: Display multiple data series with different chart types to highlight various aspects of the data.

• • Dual Axes: Use dual axes to compare data series with different scales.

• • 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.

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.

Best Uses:

• • Proportion Analysis: Show the relative sizes of parts to a whole.

• • 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.

Best Uses:

• • Category Comparison: Compare the values of different categories in a visually appealing way.

• • 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.

Best Uses:

• • Frequency Distribution: Understand the distribution of data points within a dataset.

• • 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.

Best Uses:

• • Data Distribution: Understand the spread and skewness of data.

• • 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.

Best Uses:

• • Process Visualisation: Visualise the stages of a process and the drop-off at each stage.

• • Conversion Analysis: Analyse conversion rates at each stage of a sales or marketing funnel.

• • 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.

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.