This lecture centers around two main practical exercises.
For the first part, we will discuss where to obtain shipping data, what potential it offers for analytics, and the limitations you should be aware of when using it. We will explore real-world examples understand how data analytics could be used to address various challenges in the shipping industry. The practical exercise will focus on how to detect and handle outliers that could affect the quality of the data, as well as methods for aggregating and segregating data for analysis.
In the second part, we will introduce the basic concepts of machine learning and explain how these can be applied to shipping data. The practical exercise for this part will involve comparing the performance of traditional statistical models with machine learning models, helping you understand their respective pros and cons.