Understanding the Research Process

This project highlighted to me the process of academic research. For the 6-week project, 4 weeks were spent creating the data set, 1 week analysing the data and 1 week writing up the results. I now appreciate how much work is goes into producing just one graph in an academic paper. This has highlighted the importance of academic methods, as I appreciate the significance of the methodology in deriving the final output. 

I also learned that you need to be focused on what you are trying to analyse/show. The data set I created had many uses, but it was important I only try and show 2/3 of the most interesting insights from it and not show everything that is interesting. I then needed to communicate these outputs in a clear way for readers. 

Development of Data Analysis Skills

In my first term this year I took an introductory class in R for data analysis, as well as a similar course in Python in the summer term. I was keen to further my ability to use Python on data analysis and so I chose this language to complete this project. This was the first time I had completed a data analysis project of this size in Python.

I was able to develop new graph techniques, such as heatmaps and Sankey diagrams, as well as how to perform statistical testing in Python. I did rely on generative AI at the start to help me with the coding of these graphs, but now I feel confident doing them independently.

Another skill I developed with data exploration. As the aims of the study were formulated after creating data, I had no preconceived way I wanted to use the data. I was pleased with my ability to go down a particular data analysis path, once I had found an interesting pattern in the exploration phase. The executive prioritisation of SDG 12 and 13 was a result of this data exploration after I created some simple bar charts showing how common SDG 12 and 13 was selected. This also taught more that it is more interesting when you find a pattern to perform more analysis to explain the pattern, instead doing more analysis that finds a similar pattern. 

Conclusion

This research experience transformed my understanding of the dedication and methodological rigor required in academic research. It solidified my Python data analysis skills and emphasized the value of exploratory data analysis in uncovering unexpected insights.