Students Question Big Data

Computer Science students recently leveraged their emerging fluency working with the pandas library in Python to conduct an in-depth data analysis centered on topics that interested them.

To build upon students’ Python programming skills, Computer Science teacher Brianna Ifft has been introducing the pandas software library, which gives Python users the ability to analyze, clean and manipulate large datasets.

To demonstrate their proficiency, students had to identify a complex dataset of their choosing from any field that would allow for more nuanced analysis and present it with preliminary questions they wanted to explore. Approved datasets were loaded into DataFrames so students could familiarize themselves with their structures and contents. Students then had to devise at least five specific questions to analyze beyond surface-level insights and include patterns, trends, outliers and relationships within the data.

In anticipation of presenting their findings in a compelling manner, students developed visualizations for each of their questions to reinforce the importance of visual storytelling in data science—enabling the young data-scientists to communicate their insights more effectively with their audience.

Students recently shared presentations of their work with each other and invited guests during two class periods. Each student offered their own personal analytical journey, beginning with the dataset they used and the questions they asked, including the analysis they conducted to answer each question, providing insights they gained from the work and a summary of their key findings with supporting visualizations.

The range of information in these presentations reflected the students’ interests, including Chicago crime data, the 2020 Presidential election information, WNBA vs. NBA stats, Formula One racing results, Pokeman characters and more.

Regardless of the dataset, this experience helped students gain firsthand experience using powerful digital tools to work with big data in a practical way.

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