Congratulations on finishing the course 'Visualise your data' and its four modules:

By going through the modules, case studies and reflective exercises in this course, you will have developed a deeper understanding of how to graphically represent data and information in a way that makes it easier for your audience to access and interpret your research.

To summarise what you have read, here are some key take-aways:

In the context of this course, to visualise means making visible the conditions or circumstances surrounding, for instance, a set of data. Although visualisation may also be used to analyse research data, this course has primarily focused on the communicative aspects of visualising data.
By acquainting yourself with your audience, as well as with the principles and practices of visualisation, you can get a head start in the visualisation process. If you additionally take human perception and cognitive processes into account, you may also find it easier to know when you might want to diverge from established principles and practices.
In biology and medicine in particular, a visual image might be anything from an exploratory diagnostic image to an explanatory image used to educate the public about a disease. Exploratory visualisations help researchers or others with discipline-specific knowledge understand the data better, or in a new way. Explanatory visualisations explain research data to an audience that lacks discipline-specific knowledge.
Visualisation, whether it has an exploratory or explanatory purpose, is a creative and pedagogical process. Aesthetic or technical decisions have to be made, but the basic principles can be summarised in Mackinlay's two design criteria, expressiveness and effectiveness. Your visualisation should communicate your data, no more and no less. In essence, you must tell the truth and nothing but the truth (don’t lie, and don’t lie by omission). It should furthermore be formulated in such a way that it is understandable to your intended audience, whether that audience is you, your research group, clinicians or the general public. When representing your data graphically, choose and use visual encodings that people decode better (where better means faster and/or more accurate).
A key tool in data visualisation is the mobilisation and leveraging of human perception; therefore, understanding graphic principles and how human perception works is key to effective visualisation. In graphic communication, as in all communication, there is a recipient - in this case, a viewer. The better we understand how the viewer perceives visual material, the better we can adapt the material we create and thereby, facilitate the communication process more effectively.
Gestalt principles describe how human perception helps us understand relatively complex and detailed visual material. There are numerous Gestalt principles, but those presented in this course are perhaps the most relevant to the field of research data visualisation.

Image by Karolinska Institutet University Library. Creative Commons BY SA
By applying Gestalt principles in a thoughtful way you might help your audience 'navigate' your visualisation more easily. For instance, our sense of perception is highly sensitive to deviations and irregularities in graphic material. An effective way of highlighting and directing the viewer's attention towards an object is therefore to let that object stand out and deviate in some way from its surroundings (which might comprise of, for example, a set of elements that are arranged symmetrically or in the same direction). Furthermore, keep in mind that grouping and commonality can be communicated in many different ways. For example, we tend to consider objects that are similar in shape or size as belonging to the same group, and often performing the same function. We also tend to consider objects placed near each other, or, objects that are connected to one another (for instance by a line), as belonging to the same group. Enclosure is yet another a way of asserting commonality. Lastly, it is important to understand the relationship between figure and background in an image, and how these elements interact.
Colour can play a major role in the visualisation of data, as can an absence of colour. Colour is not always necessary for an effective visualisation; always ask yourself why you want to use colour, preferably early in the visualisation process. Working initially in monochrome (one colour is used, but the lightness of the colour varies) may in fact even sharpen the other parameters of the visualisation and lead to more effective visualisation, especially if one or two accent colours are added at a later stage to draw attention to central elements.
If and when you use colour as part of your visualisation process, you will have a few key decisions to make. While there is no right or wrong and no definitive rules to follow, keeping a few principles in mind might be helpful. Consider already established practice when choosing colour, particularly as these are applied in your own field. Be aware that colour is sometimes used very differently within different fields in the life sciences. The cultural significance of colour is a bias but can also be useful in thinking about which colour to choose. Physical conditions, aesthetics and pre-existing palettes are other factors that might play a role in your choice of colour as well. Even if you use a pre-existing palette, make sure the colours you've chosen is appropriate for your particular data set, contain enough contrast, and that you have enough colours to choose from so that you can best present your work. Furthermore, colours also need to be accessible. There are many practical tools that can help you evaluate and make more inclusive colour choices (contrast, colour vision variation, etc.).
Creating accessible visualisations should be understood as a combination of several different communication principles. The primary one is basic accessibility, but to this we might add additional principles, such as clarity and transparency, the hobbyhorses of scientific communication. Another concept of significance is narrative, or storytelling.
One might, in fact, argue that data visualisation is accessibility. Trying to understand the patterns in someone's data set by scanning data tables is not very straightforward; it requires both time and mental effort. However, if the contents of the same data table is instead graphically encoded and visualised, for example through a graph, the patterns hidden in the data may instantly become much easier for the viewer to grasp.