What I’ve done
The main goal of this workshop is to learn how to understand, analyse, and communicate data through visualisation. The task includes reflection, analysis, and hands-on practice.
Review and reflect on last week's data generation tasks
- What challenges did we face when generating your dataset last week?
- What did we hope to learn from your data?
- If we had more time or resources, what would we do differently in our data collection process?
- What does this experience reveal about the relationship between data and power?
Key Concepts in Data Visualisation
- Dataset: A table-like set of data arranged in rows and columns.
- Variables: Characteristics of each record (quantitative, categorical, text-based, etc.).
- Chart types: Visual forms used to represent data—bar charts, pie charts, line charts, tree maps, Sankey diagrams, and more.
Considering the Audience
- Who is the audience for our visualisation?
- What do we want them to think, feel, or do?
- Working backwards from this goal, what key messages must our visualisation communicate?
Practical Data Visualisation
- Task A: Create Descriptive Charts and Basic Analysis in Excel
- Task B: Design More Advanced Visualisations in Tableau Public
More thoughts
In this workshop, we first reviewed the data collection task from last week and found that the data generation process was full of challenges, such as how to design questions, ensure the authenticity of responses, and obtain effective information from a limited sample. This made us realize that data is not neutral but is "produced" in specific contexts. We originally hoped to discover through the data the preferences of graduate students for the school to offer AIGC tool usage guidance courses. However, due to the limited sample size and imperfect variable design, the results obtained were difficult to draw strong conclusions from. Nevertheless, visualization helped us initially identify some trends, such as the general need for related courses among students, but they preferred online courses over offline ones. If more resources were available, we would expand the sample, optimize the variable design, adopt systematic collection methods, and plan in advance for data cleaning and coding methods. This experience also made us recognize the relationship between data and power: who collects the data, what is collected, and how it is interpreted are all deeply influenced by power, and visualization has a narrative quality when conveying information.
Through this workshop, we learned to use Excel to create basic descriptive charts and conduct simple statistical analyses with the Data Analysis toolkit. At the same time, we attempted to create more complex visualizations in Tableau, such as interactive charts and multi-variable relationship displays. We deeply realized that visualization is not only a tool for presenting data but also a way to understand data, discover patterns, and tell stories. When designing charts, we must be clear about the audience and the goal, making the charts not only aesthetically pleasing but also capable of conveying key information. Overall, this workshop helped us understand the process of data production and interpretation, the analytical and narrative functions of visualization, and the basic skills of tool usage, laying a solid foundation for subsequent research and data analysis.
Reading references (Feigenbaum & Alamalhodaei, 2020)
- Objectives describe intended outcomes, but without understanding the audience, they don't translate into effective storytelling.
- Audience segmentation allows you to tailor messages to specific groups.
- Segmentation often uses behaviours, demographics, or attitudes.
- Interviews, surveys, focus groups, and social media listening help uncover gaps between current and desired audience mindsets.