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What is Data Visualisation & How does it Enhance Data Science?

Data scientists work with highly technical data sets and large amounts of numerical information, which can be greatly enhanced with the aid of visualisation.
Data visualisation plays into the core purpose of data analytics: transforming dry facts and figures into valuable insights and helpful information able to be understood by a broader audience. As digital acceleration continues to create data of all kinds, businesses and organisations are making decisions based on detailed data sets. This new way of working is increasing the necessity of data visualisation skills for data scientists.

Read on to learn about the different methods of data visualisation, followed by tips to make data visualisations more engaging.


Types of diagrams and graphs in data visualisation

When undertaking a data visualisation project, there are numerous types of charts and graphs to choose from, each with their own characteristics and advantages. Before we compare some key styles of charts and graphs, let’s clarify these two terms. 

Chart is an umbrella term covering several data visualisation formats including tables, graphs and others, whereas only diagrams demonstrating the relationship between variables fall into the graph definition. In summary, all graphs are charts, but not all charts are graphs.

1. Scatter plots
This type of diagram is best used for the purpose of directly comparing two or more data sets, or to explore variables and trends in the distribution of data elements. 

2. Pie charts
Pie charts are an exceptionally easy to understand visual format. By plotting elements as portions of a circle, this type of chart shows data elements as a percentage. Pie charts are best used to compare different data distributions.

3. Bar graphs
Bar graphs are a popular form of visual data representation, likely due to their clarity and simplicity. Often used to check balance in a data set, bar graphs can also be used to compare all data elements in a data set.

4. Histograms
Histograms are well suited for use creating a visual data representation demonstrating frequency patterns. This is often for the purposes of forecasting or tracking patterns over time.

5. Box plots
Box plots show data through quartiles, making them great for gaining complex insights from a data set. This data visualisation method can also help identify outliers. 


Data visualisation examples

Countless industries use data science, making for some interesting information visualisation examples. Here are some impressive data visualisation examples.

  • The Great American Word Mapper

The Great American Word Mapper provides a visual representation of regional vocabulary variations via an interactive heatmap. Viewing the prevalence of various terms over the map shows how vocabulary and slang terms vary depending on region as well as other factors. 

  •  An Ocean of Noise

An Ocean of Noise visualises Spotify data to gain insights about the lifecycle of popular music. For example, an animated line graph shows that 50% of hit songs drop off the Spotify global top 100 chart within six weeks. 


Data visualisation tips

  • Before you choose which type of chart to use, consider your strategy first. Your audience and their data literacy level will influence your choice.
  • Keep your visualisation style simple. Unnecessary additional lines, graphic elements or illegible elements will make your data visualisation difficult to interpret and therefore not fit for purpose.
  • Label clearly, but not excessively. Think about which elements need labels and which can be inferred by the reader. 


Learn how to visualise data with a data science qualification

Whether you’re a data scientist looking to enhance your skills or making your way through the industry, a University of Adelaide data science qualification will teach you how to effectively articulate the significance of data. You’ll learn analysis and presentation techniques, including data visualisation. 

Our online Graduate Certificate in Data Science will have you work ready in 8 months, while our online Master of Data Science equips students to become industry leaders. Book a call to discuss program options with one of our expert advisors. 

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