5 Insights from My first maritime Data Analytics Project

Publication Date

December 29, 2024

Introduction

Embarking on my first Maritime data analytics project, SHIP FUEL CONSUMPTION & CO2 EMISSION ANALYSIS, was both exciting and insightful. Using a simulated dataset, I explored the relationship between ship types, fuel consumption, and emissions. Here are five key insights I gained from this project—lessons that shaped my approach as a data analyst.

Insight 1: Domain Knowledge Adds Value

Having a background in marine engineering gave me a deeper understanding of the context behind the data. For instance, knowing the fuel preferences of tanker ships and fishing trawlers added credibility to my analysis.

Insight 2: Visualization Drives Understanding

Visualizing the data was pivotal in uncovering trends. Bar charts and scatter plots revealed patterns, such as the high fuel consumption of tanker ships compared to other vessel types, making insights easier to digest.

Insight 3: Simulation Can Be Powerful

Although the dataset was simulated, it mimicked real-world scenarios effectively. Designing realistic ship types, fuel types, and consumption metrics taught me the importance of thoughtful simulation when real data isn’t available.

Insight 4: Insights Require Context

Identifying that tanker ships consumed significantly more fuel than other types wasn’t just about the numbers—it was about the operational context. Large-scale fuel consumption translates to higher emissions, which is a critical concern for the maritime industry.

Insight 5: Storytelling Matters

Sharing the results was as important as the analysis itself. I crafted a compelling narrative linking fuel consumption to CO2 emissions, making the findings relatable to audiences concerned with environmental impact.

Conclusion

My first Maritime project was a great start to my analytics journey, combining technical skills and domain knowledge to produce actionable insights. It reinforced the importance of context, storytelling, and the role of simulation in data projects.

Call to Action

Have you ever worked on a simulated dataset? How did it shape your learning experience? I'd love to hear your thoughts—share them in the comments or connect with me!