<p>This study presents a data-driven methodology to enhance aircraft piloting proficiency using flight simulator data from a diverse group of participants. Principal component analysis was applied to reduce data dimensionality and extract core components of piloting skill. Clustering analysis was performed to identify distinct pilot proficiency groups and highlight variables exhibiting statistically significant differences across clusters. A Bayesian network was constructed to visualize interrelationships among key variables and simulate how changes affect overall proficiency. Two critical parameters—standard deviation of indicated airspeed (std_IAS) and mean bank angle (mean_Roll)—were identified as significant contributors to cluster differentiation. Simulation results indicated that reducing these parameters may help pilots transition from lower-performing to higher-performing clusters, reflecting improved control and stability. Based on these findings, targeted training interventions can be designed. Real-time feedback systems may help pilots recognize and suppress unnecessary control inputs to reduce airspeed variability, while scenario-based exercises can promote rapid correction of unintended roll angles. This integrative approach enables quantitative and interpretable evaluation of pilot skills and supports personalized training program design. The methodology benefits aviation training and may extend to other domains requiring high-precision operational skills.</p>