Do you want to effectively communicate your findings from data analysis and machine learning to a broad audience with great visualisations, well structured notebooks, or an interactive shiny dashboard? Join our Tech.Mondays Project Group to learn about dos and don’ts in visualisations and how to set up notebooks and build your own interactive dashboard prototype.
The amount of data, which can be analysed through data science tasks, is constantly increasing. Usually, around 95% of the time is spent on data analysis and coding and only around 5% on visualising the results. However, what other people see at the end of the day is 0% data analysis and code and 100% visuals. Thus, effective communication of findings from data mining, statistical analysis, and machine learning are a key skill in data science. In order to win over customers within and outside of the organisation, share insights with team members, or present scientifical findings it is essential to be able to do this in an accessible, appealing and structured manner.
We learn how to create beautiful visualisations and embed them in our own notebooks and dashboards. The goal is to make your conducted data analysis accessible and appealing. You can also bring your own data analysis ideas and projects and discuss them with us and other like-minded people in the project group. Finally, we also briefly touch on the important topic of storytelling with data.
In the context of interactive and collaborative sessions, we use Tableau and the R packages ggplot2, plotly, Rmarkdown, and shiny, learn their syntax and look at some interesting examples and applications. We further share tips and tricks, previous knowledge and gained experience among each other in the project group. If time permits and there is a strong interest in Python, we might include some sessions on matplotlib, seaborn, flask and dash.
We welcome Bachelor’s up to Ph.D. students who are interested in data science and want to learn how to effectively communicate their data analyses to a broad audience. No preliminary knowledge is required (in particular for the first few sessions on the theory of data visualisation and Tableau). Some prior experiences with R (or Python or Matlab) may be beneficial for the sessions on notebooks and dashboards, however, we will adjust the content to the participants level of knowledge.
Makerspace (university main building)
Every Monday at 18:15 (except break period)
