By Haorui Lyu (haoruil2@illinois.edu)
Professor: Andrew Uhe
Semester: Spring 2024
I recently started exploring various datasets that I might use. The first dataset I found was "fuel.csv", but I quickly abandoned it in favor of "flights_sample_3m.csv". Initially, I intended to use the entire dataset for analysis and visualization but soon realized it was too large. Consequently, I extracted all the flight data from 2023 for analysis, but then encountered a new issue. I found that the flight data from September to December 2023 was missing. Eventually, I used all the flight data from 2022 extracted from the original dataset for my analysis and visualization. I named the datasets for 2023 and 2022 as "data_2023.csv" and "data_2022.csv", respectively. All datasets can be found in the Data folder.
The biggest gain I gained from this data visualization course was how to transform abstract data into intuitive visual expressions. Although the course is not complicated, it is full of challenges. Every assignment is an improvement of my ability. Through practice, I not only learned basic charting skills but also explored a variety of data visualization tools and techniques, such as using ipywidgets to enhance interactivity.
More importantly, this course enhanced my problem-solving skills and enabled me to integrate and leverage data in my workflow more effectively. I learned how to better evaluate and select appropriate visual elements such as color and layout to improve the delivery of information.
My understanding of data visualization is no longer limited to the appearance of charts but extends to the process of building them and the thinking behind them. Now, when faced with any data display, I am able to more keenly identify its structure and design intent, which will greatly help my future learning and career development. Overall, this course has greatly enriched my knowledge base and skills, and laid a solid foundation for my future academic and career paths.
For future students taking this data visualization course, here are a few enhancements to consider to improve their learning experience and outcomes: Introducing more advanced visualization tools like Tableau, Power BI, or more advanced Python libraries like Bokeh and Plotly. These will help students master industry-standard tools and build more complex, interactive visualizations; include real-world datasets and projects, allowing students to better understand the practical challenges and nuances of data visualization by working on real data problems; invite guest lectures by professionals working in the data visualization industry to provide insights on current trends, best practices, and practical applications of data visualization; implement a structured peer review process that promotes learning through feedback and deepens knowledge of multiple visualization techniques and understanding of methods; conduct workshops on aesthetic design, such as selecting color palettes, layout, and typography, understanding these elements can significantly improve the effectiveness of visual presentation; provide modules on processing and visualizing big data challenges, learning to effectively process and visualize big datasets are critical skills in many fields; provide access to and training in different visualization software and tools, allowing students to become proficient in multiple platforms; deepen the theoretical content of the course on data visualization principles, such as information hierarchy, user interface design, and cognitive load management; implement a strong feedback mechanism to regularly collect student insights and suggestions on course content and teaching methods for continuous course improvement. By incorporating these enhancements, courses can become more comprehensive, engaging, and more directly applicable to real-world scenarios, better preparing students for careers in data science, analytics, and more.