The primary objective was to accurately quantify the volume of training sessions and participant numbers to assess overall performance. The analysis aimed to identify specific trends in course popularity, facilitator workload, and geographic reach to guide decision-making for the upcoming year. All analysis utilized internal training records from the "TKI-ED-2025-Editions-Delivered" dataset, which contained detailed logs of session dates, locations, methods, and course titles.

The analysis successfully validated a total of 246 unique training sessions attended by 4,150 participants during the year. Key findings highlighted Saudi Arabia as the leading market, accounting for 76 sessions and 1,517 participants, while the "Certified KPI Professional" course emerged as the most popular program. The study also revealed a balanced delivery mix, with 136 live online sessions compared to 110 face-to-face engagements, providing a solid evidence base for optimizing the 2026 resource allocation strategy.

I utilized Python and the Pandas library to process and sanitize the raw training data. The methodology involved implementing robust date parsing algorithms to standardize inconsistent formats and rigorously removing duplicates to define unique sessions. I further enriched the dataset by mapping course acronyms to their full titles and aggregating the data into targeted summaries. This structured approach allowed for the precise calculation of key performance indicators across multiple dimensions, such as delivery method and location.