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Learning Path - Lab
Tutorials and guides on create data visualisations using different tools and languages.
Dataviz.Shef Team


This is the second learning path prepared from the Dataviz.Shef team that is specifically designed for those who have completed Learning path - Concept or those with some experience in data visualisations and programming languages such as Python, R, or Matlab. If not, it is recommended that you go through the first learning path first before you read on.

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.

Unlike the previous learning path where most of the resources concentrated on concepts of data visualisations and guides for coding, this learning path will mainly focus on exploring what we can do with each programming language to produce suitable data visualisations. In each of the languages there are three sections for you to explore, Data processing, Data visualisation, and Share. Choose a programming language to get started.


There are two articles[1][2] listed many useful R packages that we recommended you to look at. When you start using R, it is common that you’ll be recommended to install R studio (an Integrated development environment (IDE) for R) as it is a great tool for source code editing, build automation and debugging. Learn more about R studio at rstudio.


Perhaps many of you have already used Jupyter Notebook or JupyterLab for writing Python codes, otherwise we suggest you take a look at this website to learn more about them and why they are great for developing data visualisations with Python.


Matlab is different to the previous languages described here, in that it is an interactive programming environment, with the Matlab language at it core. Mathlab was designed for engineers and scientists, so it focuses on making data manipulation, analysis and visualisation tools as intuative and natural as possible.


You have completed the learning path - Lab, now you know how to create data visualisations with your chosen programming languages and packages. While continuing to explore more about data visualisations, perhaps we should think about how to create a reproducible process...

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