After learning Python for five weeks, I took the liberty of participating in a Edinburgh Festival Hackathon, which required me to do data analysis and come up with insights and business strategies. Although I was confused at first, it turned out to be surprisingly fun.
My main reflections are:
1. I completely overcame the mentality of being embarrassed to copy and paste code.
2. I realized that mastering data analysis involves understanding the underlying thought process and knowing how to leverage existing libraries. The transition from “thinking” to “application” often involves adapting existing solutions.
3. Quantitative analysis is indeed more likely to be replaced by AI than qualitative research.
If you can do quantitative analysis like ChatGPT, you are an adequate quantitative analyst;
But if you do qualitative analysis like ChatGPT, you basically deserve to be unemployed.
Qualitative researchers need comprehensive practice to come up with sharp insight. There are many skills that need to be internalised throughout the research process, such as aligning research questions with business challenges, collecting accurate qualitative data with effective method, and taking into account the systematic nature of explanations and the possibility of commercial applications when generating insights.
It should not be difficult to convert these skills to quantitative analysis, but the reverse is not necessarily true. I have seen many analysis charts that are so fancy that they dazzle my eyes, but the insights and applications that come out in the end are basically nonsense.
I also find it hard to believe that any purely quantitative conclusion can guide business strategy. All Killer Insights must always return to human, and then there can be strategies, applications, and implementation.
In our school, half of the conversations are always about technology and AI. However, I’m increasingly feeling that as these technologies become foundational and their potential surpasses majority of human capabilities, fields where human evolution and progress are slower will become increasingly important. Fields without correct answers or standard solutions, where personal insight, experience, exploration, interpretation, and creativity are key, will become more irreplaceable.
