Working with data

Aims and objectives

This module will:

  • explain what data is
  • examine how data is used
  • explore ways to analyse data.

After completing this module, you will be able to:

  • find, clean and use data
  • evaluate and select tools to analyse and display data.

5. Analyse and display data

Data analysis is the process of examining data to find answers to questions. There are many ways that you can analyse and display data. This module will give an overview of some common analysis methods and tools. The data can be read through and manually organised by you, or processed using software.

Get an overview of your data

  1. Look through, or listen to, all the data you have gathered to get an overall idea of what you have gathered
  2. Transcribe any audio content so it is ready to be organised. You can record directly in Word for web or upload an audio file to get a transcription
  3. Think about how you can reveal patterns or meaning in the data
  4. You may be able to sort the qualitative data in a way that it can be measured.

Read the blog - How we helped our reporters learn to love spreadsheets.

You can access the Times data training program in Google Drive, including training information, datasets and tip sheets to learn skills for Google Sheets and using data to tell a story.

Descriptive data analysis

Descriptive analysis provides a summary of the data that has been gathered. For quantitative data this involves presenting the data visually in tables and charts, and measuring values such as the mean. For qualitative data the data is interpreted through grouping into categories or coding. Coding is the process of organising and sorting your data.

You can use one method or a mix of methods to do your analysis. Methods include:

  • Classification — sorting the data into different kinds of things
  • Statistical analysis — for qualitative data this could be counting different elements, such as keywords and phrases, to find out how often something is mentioned or has occured.
  • Thematic analysis — looking for recurring themes in the data.

Thematic analysis of qualitative data

Looking for patterns or themes in your data can help to answer your original question or may lead to new questions. Descriptive coding is a useful technique for thematic analysis. Coding can be done by hand or by using software, such as NVivo (Find information on accessing and using NVivo in the table on Data and text analysis software on this page).

Descriptive coding by hand

  What does coding look like? (YouTube, 4m42s)

Statistical analysis of quantitative data

Statistical analysis methods can range from simple to complex. Simple methods include:

  • Range — the difference between the highest and lowest value
  • Minimum — the smallest value
  • Maximum — the largest value
  • Frequency — the number of times a certain value appears
  • Mean — the total of the values divided by the number of values
  • Median — the middle value of any data after they are put in order
  • Mode — the most frequently occurring value.

Learn about:

You can use data analysis software to perform the statistical analysis.

See how to use features in Microsoft Excel to analyse data, including sort, filter, charts and pivot tables. It includes step-by-step tutorials on the different functions. 

Inferential data analysis

Inferential analysis involves using the information from the data to make judgements about a topic or issue. For example, the results from a small group could be used to infer something about a larger group, or the results could be used to predict what might happen. Inferential analysis of qualitative data usually requires more advanced statistical methods

  Choosing which statistical test to use (YouTube, 9m32s)

Which Stats Test is a question tool to help you narrow down the type of statistical test to use. (UQ login required).

Data visualisation

Tables, graphs, maps and charts are used to summarise and display data. Once you have done your analysis you need to think about the best way to present the data. Get tips on good and bad visualisations.

   Here is an example of data visualisation of the average years of schooling per country since 1950.

Choosing a chart

Displaying data

Misrepresenting data

Graphs can make data easier to understand but they can also be used to misrepresent data. Check graphs carefully. The graph creator can manipulate the design to innacurately reinforce their own agenda.

Read 5 ways writers use graphs to mislead you, including:

  • omitting baselines to make one group look better than another
  • manipulating the y-axis to blow out the scale
  • only including certain parts of the data
  • choosing a type of chart that does not fit the data
  • using colours, that alter long-held conventions or associations

Tools for analysing and visualising data

There are many tools available for analysing and visualising data. You may want to use a tool that you have some experience with already, like Excel or Google Sheets, or you may want to try using software that is specifically for data analysis.

Spreadsheet tools

These tools can be used to analyse and visualise data.

Tool Free account available Licensed version available via UQ Guides Tutorials UQ Library Training
Microsoft Excel No Yes Excel tables and charts Excel tutorials from beginner to advanced (LinkedIn Learning pathway) Requires UQ login Yes
Google Sheets Yes No Create and edit charts Google Sheets Essential Training (LinkedIn Learning course) Require UQ login No

Data and text analysis software

These tools can be used to analyse and visualise data.

Tool Free account available Licensed version available via UQ Available on library computers Guides Tutorials UQ Library training
R Yes No Yes Essential R resources Data Science with R and RStudio (LinkedIn Learning pathway) Requires UQ login Yes
NVivo No (A reduced price student license is available) For UQ computers only Yes Getting started Learning NVivo (LinkedIn Learning course) Requires a UQ login Yes
Leximancer No Yes No Leximancer guides A First Leximancer 4.5 analysis (YouTube, 19m,17s) No
Python Yes No Yes Python for beginners Data Science with Python (LinkedIn Learning pathway) Requires a UQ login Yes


Yes Analysing and visualising data with MATLAB MATLAB 2018 Essential Training (LinkedIn Learning course) Requires a UQ login No
SPSS No (A student license can be purchased)  For UQ computers only Yes SPSS Learning Modules Introduction to SPSS for data analysis (YouTube, 16m17s) No

Geographic information systems (GIS) tools

GIS tools are used to capture, analyse and present spatial or geographical data.

Tool Free accounts available Licensed version available via UQ Guides and tutorials
ESRI ArcGIS No Yes Learning ArcGIS (LinkedIn Learning course) Requires a UQ login
Carto Yes No Carto tips and tricks
QGIS Yes No Learning QGIS (LinkedIn Learning, 2h5m) Requires a UQ login

Our Geographic information systems (GIS) guide lists other tools and software to use with spatial or geographical data.

Visualisation tools

These tools are specifically for visualising data. You will need to do your analysis first before using these tools. 

Tool Free account available Licensed version available via UQ Guides Tutorials UQ Library Training
Venngage Yes (but no download as PNG image or PDF available) A limited number of upgraded licenses (for education) are available for UQ students attending the Library's Infographic sessions. Venngage Help Center Venngage for beginners (YouTube, 24m34s) Yes
PowerPoint No Yes PowerPoint training Create and format charts in PowerPoint (LinkedIn Learning, 4m24s) Requires a UQ login Yes

Yes - a one year license for

No but the free account requires you to be a student or teacher at an accredited academic institution Learning resources for students Tableau essential training (LinkedIn Learning course) Requires a UQ login No
Canva Yes (Download as JPG, PNG or PDF) No Get to know Canva Canva design YouTube videos No
Piktochart Yes (Download as JPG or PNG image) No Make an infographic in 5 steps Piktochart tutorials No

Our Data visualisation guide lists more tools for visualising data.

Duration:   Approximately 30 minutes

Graduate attributes

Knowledge and skills you can gain to contribute to your Graduate attributes:

 Independence and creativity

 Critical judgement

 Ethical and social understanding

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