Data analysis is any task undertaken to try and discover meaningful information from a dataset. This covers a huge range of activities, from looking for correlations and performing statistical analyses, to describing and analyisng images or text to draw out thematic relationships.
If you are unsure of what tool would be most helpful in your analysis, check out Analysis Tools to get some ideas.
Data visualisation refers to any way of presenting information so that it can be interpreted visually. This is a pretty broad bucket that can include things like charts or graphs, diagrams, images, animations, and infographics. Data visualisation makes use of our ability to recognise trends, patterns and relationships to draw out meaning from our data. This can be useful in analysing your data (yes, data visualisation can be an important activity in data analysis!), or in communicating the results of your work to others.
There are many, many different kinds of visualisations to choose from when deciding how to display your data, and the tools that you can use to create visualisations are just as numerous. In order to make the task of visualising your data a little less overwhelming, we have gathered together some resources for you. Check out the Creating a Visualisation section for information on things to think about when you want to make a visualisation, and how to pick the most appropriate visualisation for your data. Visualisation Tools lists some of the tools available to you to try and create visualisations of your own.
If you want advice on the best way to visualise your data or the best tool to use, please get in touch by emailing Research data support.
The University offers researchers a range of training in data analysis and visualisation. Check out research data training sessions run by the Library, including an introduction to data visualisation, how to create infographics, and how to manage your data to set you up well for performing quantitative and qualitative data analysis.
The Sydney Informatics Hub at the University schedules lots of different training sessions to teach you to wrangle and analyse your data, including Excel, programming in Python and R, and High Performance Computing.