Tableau Visualizations
Here are some of the Visual Analytics Chart that I made while learning how to use tableau for Data Analyst Profile.
Learning Resource
This project was part of the Google Advanced Data Analytics Course and focused on helping the Seoul Transportation Department optimize their bicycle maintenance scheduling. Using rental data from 2018, I built an interactive Tableau dashboard to uncover usage patterns across different times of the day. This was my first time using dashboarding tools like Tableau or PowerBI. The core goal was to minimize service disruptions by identifying low-activity windows during standard working hours (8 a.m. to 5 p.m.). By analyzing rental trends, we were able to suggest ideal maintenance windows when the demand was consistently low—helping the city maintain operational efficiency without affecting user experience.
Dashboards
What I learnt
I began to grasp some of the fundamental principles that sit at the intersection of data visualization and human-computer interaction. I realized that building a dashboard isn’t just about placing charts on a screen—it’s about designing an interface where the user’s cognitive load is minimized, and the insights are surfaced at just the right level of abstraction.
By experimenting with filters, interactive timelines, and comparative views, I learned how interactivity can shift the role of a dashboard from being a static report to becoming a decision-support tool. More importantly, I saw how visualization acts as a bridge between complex datasets and human reasoning: the way patterns emerge on a heatmap or a time-series chart directly shapes how a decision-maker interprets and trusts the information.
Making a dashboard isn't hard - there are more analysts than the world can make use of - and now we also have AI. But the right techniques make an effective dashboard - which requires thinking like both an analyst and an HCI designer—structuring not just the data, but also the experience of discovery from the user's standpoint.
Working on this project, even with its straightforward scope, has been surprisingly revealing. I'm beginning to appreciate that data visualization isn't just about representing numbers; it's fundamentally a human-computer interaction challenge. It's fascinating to see how even minor adjustments—like how a filter behaves or how data points are highlighted—can significantly reduce a user's cognitive load and make insights feel more intuitive. This small taste has made me deeply curious about the more subtle principles at play. Making a dashboard isn't hard - there are more analysts than the world can make use of - and now we also have AI - so It’s entirely one thing to make data visible, but another to make it understandable, and I'm intrigued by how much depth there is to explore in bridging that gap.
Learning Resource
This project was part of the Google Advanced Data Analytics Course and focused on helping the Seoul Transportation Department optimize their bicycle maintenance scheduling. Using rental data from 2018, I built an interactive Tableau dashboard to uncover usage patterns across different times of the day. This was my first time using dashboarding tools like Tableau or PowerBI. The core goal was to minimize service disruptions by identifying low-activity windows during standard working hours (8 a.m. to 5 p.m.). By analyzing rental trends, we were able to suggest ideal maintenance windows when the demand was consistently low—helping the city maintain operational efficiency without affecting user experience.
Dashboards
What I learnt
I began to grasp some of the fundamental principles that sit at the intersection of data visualization and human-computer interaction. I realized that building a dashboard isn’t just about placing charts on a screen—it’s about designing an interface where the user’s cognitive load is minimized, and the insights are surfaced at just the right level of abstraction.
By experimenting with filters, interactive timelines, and comparative views, I learned how interactivity can shift the role of a dashboard from being a static report to becoming a decision-support tool. More importantly, I saw how visualization acts as a bridge between complex datasets and human reasoning: the way patterns emerge on a heatmap or a time-series chart directly shapes how a decision-maker interprets and trusts the information.
Making a dashboard isn't hard - there are more analysts than the world can make use of - and now we also have AI. But the right techniques make an effective dashboard - which requires thinking like both an analyst and an HCI designer—structuring not just the data, but also the experience of discovery from the user's standpoint.
Working on this project, even with its straightforward scope, has been surprisingly revealing. I'm beginning to appreciate that data visualization isn't just about representing numbers; it's fundamentally a human-computer interaction challenge. It's fascinating to see how even minor adjustments—like how a filter behaves or how data points are highlighted—can significantly reduce a user's cognitive load and make insights feel more intuitive. This small taste has made me deeply curious about the more subtle principles at play. Making a dashboard isn't hard - there are more analysts than the world can make use of - and now we also have AI - so It’s entirely one thing to make data visible, but another to make it understandable, and I'm intrigued by how much depth there is to explore in bridging that gap.
Learning Resource
This project was part of the Google Advanced Data Analytics Course and focused on helping the Seoul Transportation Department optimize their bicycle maintenance scheduling. Using rental data from 2018, I built an interactive Tableau dashboard to uncover usage patterns across different times of the day. This was my first time using dashboarding tools like Tableau or PowerBI. The core goal was to minimize service disruptions by identifying low-activity windows during standard working hours (8 a.m. to 5 p.m.). By analyzing rental trends, we were able to suggest ideal maintenance windows when the demand was consistently low—helping the city maintain operational efficiency without affecting user experience.
Dashboards
What I learnt
I began to grasp some of the fundamental principles that sit at the intersection of data visualization and human-computer interaction. I realized that building a dashboard isn’t just about placing charts on a screen—it’s about designing an interface where the user’s cognitive load is minimized, and the insights are surfaced at just the right level of abstraction.
By experimenting with filters, interactive timelines, and comparative views, I learned how interactivity can shift the role of a dashboard from being a static report to becoming a decision-support tool. More importantly, I saw how visualization acts as a bridge between complex datasets and human reasoning: the way patterns emerge on a heatmap or a time-series chart directly shapes how a decision-maker interprets and trusts the information.
Making a dashboard isn't hard - there are more analysts than the world can make use of - and now we also have AI. But the right techniques make an effective dashboard - which requires thinking like both an analyst and an HCI designer—structuring not just the data, but also the experience of discovery from the user's standpoint.
Working on this project, even with its straightforward scope, has been surprisingly revealing. I'm beginning to appreciate that data visualization isn't just about representing numbers; it's fundamentally a human-computer interaction challenge. It's fascinating to see how even minor adjustments—like how a filter behaves or how data points are highlighted—can significantly reduce a user's cognitive load and make insights feel more intuitive. This small taste has made me deeply curious about the more subtle principles at play. Making a dashboard isn't hard - there are more analysts than the world can make use of - and now we also have AI - so It’s entirely one thing to make data visible, but another to make it understandable, and I'm intrigued by how much depth there is to explore in bridging that gap.