SQL: Write queries to pull, filter, and organize data straight from company databases.
Excel: Use pivot tables, VLOOKUP/XLOOKUP, and basic formulas for fast data checks.
Data Cleaning: Find and fix errors, duplicates, and missing values in messy datasets.
Visualization Tools: Build clear dashboards in Power BI or Tableau to present trends.
Programming: Use Python or R for deep statistical analysis and heavy data crunching.
Critical Thinking: Ask the right questions and look past the basic numbers to find the true meaning.
Communication: Turn complex stats into simple, easy-to-read stories for non-technical managers.
Problem-Solving: Figure out how to fix broken data pipelines or answer vague business goals.
Business Acumen: Understand how your specific industry works so your insights are actually useful.
