Over the rest of the semester, you will work towards creating an interactive, data-driven story. To extract value from large and complex datasets, data visuals have evolved over the past decade from static charts and graphs to interactive and immersive visuals that tell a story. This allows the audience to modify elements of the data being presented and manipulate the graphical representation. Static charts and graphs do not have the capability to adjust the visual, such as hovering, sorting and scaling. Interactive visualizations allow users to generate transformative insights, identify relationships, view trends and create meaningful stories through data.
Data storytelling is the execution of describing data through visualizations by building a compelling narrative around a dataset. This adds meaningful context to the data and helps the audience easily understand the information.
In your work as data scientists, in addition to doing modeling and machine learning work, you will be responsible (either individually or as part of a team) for providing the following as part of a project:
- Findings: what does the data say?
- Conclusions: what is your interpretation of the data?
- Recommendations: what can be done to address the question/problem at hand
Your narrative will focus on the first two above. It should allow the audience to be able to understand the topic you are analyzing, presenting and discussing. The visual narrative should not attempt to tackle and solve a large scale problem, that is not the objective. The objective is simply to find a topic of interest, work with the data, and present it to an audience that may not know very much about the subject using a data-driven narrative.