WELCOME!
This is the first learning path prepared from the Dataviz.Shef team that helps you to get started. This learning path is specifically designed for those with no experience in data visualisation that wish to learn from the basics and explore a broad range of topics such as stages of visualising data, choosing the right charts, make use of powerful tools and libraries available out there, data exploratory techniques, and many more.
You will soon find out that we are often referring to external resources this is because there are already enormous amazing resources available on the internet, we have organised them in relevant sections for you to check out. In addition, the university has a partnership with Linkedin Learning providing thousands of online training courses to staff and students through MUSE, we have also included some useful courses to help you get started.
Data visualisation basics
When we visualise data, the basic process should be to first determine the goal of our visual analysis, then obtain the relevant data, organise the data, select the appropriate tools, display the data through appropriate charts, and finally display the visualised results. We have divided this section further into five sections, feel free to skip some sections if you are comfortable with the contents.
1. Ideas and questions
A piece of data visualisation often starts with good ideas/questions in mind, then we map data directly into perceptible graphics, symbols, colors, textures, etc., to communicate the idea or answer the question. Here are some interesting related articles:
If you are looking for inspiration, explore the following resources:
2. Data Preparation
Once you have a general understanding of what you want to produce, data collection can be carried out (or you might already have some data). We can collect the data we need to analyse according to the different methods of data acquisition, such as issuing questionnaires, obtaining data through experiments, or open source data. After we collect the data, it is likely that we need to pre-process the data, filter and delete invalid and unnecessary data. This process is extremely common in day-to-day work of professions such as Data Scientist, Data Engineer, Data analyst or Machine Learning Engineer. When the data is ready, we can import the data into the tool of our choice for data exploratory analysis.
3. Exploratory Analysis
Data exploratory analysis (DEA) is considered as an approach that explores the structure and laws of the data and verifies a set of hypotheses by means of drawing, tabulation, equation fitting, and calculating feature quantities. There are a broad range of techniques and tools but it is not necessary to use all of these. Watch the following course to get started:
In short, DEA helps you to get a deep understanding of chosen data and extract meaningful insight from data so that you can make choices of which information you want to present to your audiences.
4. Data visualisation
After you have made a decision on what kind of information you want to present, it is the time to produce data visualisations with appropriate selection of charts and graphs, these charts might have come from DEA you have conducted or you have new ideas to present the insight. The following videos/articles is helpful in understanding the importance of visual display in data visualisation:
For more information on selection of charts, go to What Charts? section.
5. Interactivity
Adding interactivity to your data visualisation could take your impact to the next level. By offering interactive widgets/dashboard for your data visualisations, audiences get some entertainment while having the freedom to explore different areas of the dataset you have opened access to. Learn more about interactivity from the following resources:
Other resources
You might want to check out the following courses.
What charts?
Choosing the right chart for your data does not come easy. Fortunately, there are some great websites that can help you to make decisions.
See these resources also:
Tools!
There are many tools available out there to build visualisations and each of them has their own focus, strength and weaknesses. These tools are either programming languages, open source software/libraries, or commercial softwares and platforms. Here we listed materials to get started on some popular programming languages for data visualisations, each programming language has their own community and no doubt there will be some amazing data visualisation libraries for you to use. If you want to learn more about other alternatives, check out this article.
Python
R
Matlab
When working with Python and Matlab, it is likely you will be working with Jupyter Notebook, learn more about it on this course.