100 Prompts for Data Analysts to Take Work to the Next Level Practical AI Prompt Templates for Excel, Power BI, SQL, Data

Data analysts spend significant time on tasks that benefit from clearer thinking and better-structured communication - cleaning datasets, writing reports, designing dashboards, explaining findings, and ensuring data quality. This book gives analysts 100 practical AI prompt pages, one per page, each focused on a specific and common analyst work task.The prompts in this book cover ten core areas of data analyst work:Chapter 01 covers data cleaning - detecting messy data issues, standardizing text values, handling duplicates, cleaning date formats, and preparing data for dashboard use.Chapter 02 covers Excel productivity - choosing the right formula, building lookup strategies, planning pivot table reports, designing monthly reports, and reviewing workbook structure.Chapter 03 covers Power BI dashboard work - defining dashboard objectives, selecting KPI cards, planning layouts, choosing visuals, building filter logic, and preparing handover notes.Chapter 04 covers data visualization - selecting chart types, improving confusing charts, highlighting outliers, creating visual story flows, and simplifying overloaded visuals.Chapter 05 covers business insights - finding data patterns, explaining KPI movement, generating management insights, converting findings into decisions, and building insight review frameworks.Chapter 06 covers automation and workflow - identifying repetitive tasks, designing file naming systems, creating report refresh checklists, drafting automation requirements, and comparing workflows.Chapter 07 covers SQL and database thinking - translating business questions into SQL logic, planning join strategies, creating group by summaries, debugging query concepts, and documenting report logic.Chapter 08 covers reporting and presentation - writing monthly summaries, creating executive summaries, converting analysis into slide points, drafting stakeholder emails, and turning findings into data stories.Chapter 09 covers quality checks and audits - creating data QA checklists, finding outliers, reconciling data sources, validating KPI calculations, auditing formula logic, and building final review checklists.Chapter 10 covers analyst professional growth - building portfolio ideas, structuring case study outlines, preparing interview examples, designing learning plans, and creating 30-day growth plans.Each page in the book contains a prompt description, a tool suggestion, workflow steps, a copy-paste AI prompt with bracketed placeholders, an expected output description, and a practical analyst tip. Every page is self-contained and ready to use with any AI tool.This book is for educational and informational purposes only. AI outputs require analyst review and verification before use in any professional or business context. Results from using AI prompts may vary.

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