I need a thorough, data-driven look at current unemployment figures so I can shape stronger policies to improve our economy. My focus is squarely on economic data analysis, not broader training or SME development, and I want the findings delivered as a well-structured, detailed report. You will source the most recent unemployment datasets (national and, where possible, provincial or city-level), clean and verify the numbers, then apply solid statistical techniques—time-series modeling, trend decomposition, correlation checks with inflation and trade indicators, and any other relevant tests that strengthen the conclusions. Python (pandas, statsmodels, seaborn) or R (tidyverse, forecast) are perfectly fine; feel free to suggest alternatives if they produce clearer insights. The report should read like a decision-maker’s handbook: concise executive summary up front, methodology explained in plain language, charts embedded where they illuminate patterns, and practical recommendations tied directly to the evidence uncovered. Cite every data source, note limitations, and flag any anomalies that deserve follow-up. I’ll consider the job complete when I receive: • An editable, well-formatted report (Word or Google Docs) with all visuals in place • The cleaned dataset and reproducible scripts/notebooks • A short slide deck distilling the key takeaways for non-technical stakeholders With these materials I’ll be ready to discuss concrete steps to tackle unemployment and, by extension, strengthen overall economic performance.