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SQL Analysis Verified with AI and Excel Dashboard

We validate AI-proposed SQL with row-level checks, refunds, time periods, denominators, and counterexamples. We connect the verified, cleaned data to Excel formulas, selection values, charts, and web dashboards, then practice applying design skills and rerunning the analysis with new data.

1 learners are taking this course

Level Basic

Course period Unlimited

SQL
SQL
Excel
Excel
SQL
SQL
Excel
Excel

What you will gain after the course

  • Reconcile duplicate orders and refunds using CTEs and window functions, then verify monthly net amounts and customer metrics.

  • Practice with Excel dashboard filters, new orders, printing, web design skills, and report generation

SQL Analysis and Excel Dashboards Verified with AI

24 lessons · approximately 77 minutes. A hands-on exercise using Python’s standard library and sample order data for learning.

Rather than trusting AI-proposed queries as-is, define the row-level unit, refund boundaries, time period, and denominator, then verify the results using query outputs and counterexamples. Use CTEs and window functions to calculate monthly net amounts and channel changes, break down order counts and average order value, and measure customer metrics. Connect the verified, cleaned data to Excel formulas, dropdowns, charts, and print-ready sheets, then broaden its application with web examples, new 2027 data, and a 48-hour inquiry resolution rate.

Who This Is For and What You’ll Need

Suitable for business professionals familiar with basic SQL SELECT/JOIN. This course does not cover the full range of SQL syntax from scratch. Prepare Python 3 and an environment for using Excel or Excel for the web, and run the AI prompts in your own tool. Check the terms of the service you use for account requirements, features, and any additional fees.

Provided materials

We provide raw and cleaned sample CSVs and an SQLite database, executable SQL, validation commands, Excel starter and finished files, before-and-after web examples, a self-made alternative skill, five AI prompts and a log template, an editable and saveable five-slide report template, new materials and independent assignments, and a self-check against publicly available figures.

Practice Screen and Scope of Support

The video explains actual local SQL and command execution and results in an enlarged view. The Excel web content consists of step-by-step screenshots of actual operations and saves, while Claude skill records are distinguished as evidence of past actual calls and recognition. We distinguish the execution evidence for the alternative skill we provide from that of the original private skill, and do not distribute the original private skill. The existing Excel file covers 2,000 orders and static dropdown ranges, while the new 2027 data is for SQL and web exercises.

AI Usage Notice

The data are custom-created synthetic data and do not represent actual company projects or sales or employment outcomes. AI assisted with drafts of the script, code, and design, and the narration uses an AI-generated voice. Numerical consistency alone does not certify independent learning or practical skills.

Recommended for
these people

Who is this course right for?

  • SQL learners looking to move beyond the basics of SELECT and JOIN and apply their skills to real-world business analysis

  • Professionals who need to verify AI analysis results and share them via Excel, web dashboards, and reports

Need to know before starting?

  • Basic concepts of SELECT and JOIN, a PC capable of running Python 3, and Excel or Excel for the web

  • For the new AI prompt exercise, use your own AI service account; no usage credits will be provided.

Hello
This is dorian05202988

Career Verified

A data craftsman with over 20 years of experience in databases and IT. As a DBA and manager of a DB Platform team, I work with database platforms and specialize in MySQL, PostgreSQL, Oracle, MongoDB, SQL tuning, AWS, Terraform, and Python. Drawing on experience in the gaming, finance, and IT services industries, I explain database and cloud architecture. I have published 『Oracle Practical Tuning: Learning SQL Patterns』 and 『Oracle Pro*C Practical Project Guide』, and have presented on serverless architectures, database selection, and multi-database strategies at AWS Summit 2018 and 2019 and DataYa Nolja 2020. Recently, I have been working on slow-query analysis and tuning recommendations using Bedrock LLM, automated DB specification sizing, and query workflows through a DB metadata MCP server and Slack bot. I explain database automation for developers and AI applications for non-developers through hands-on exercises tailored to the audience and objectives of each course. Rather than applying AI suggestions as-is, I teach a decision-making framework that verifies result equivalence, execution plans, and observability conditions, and defers action when the supporting evidence is insufficient. Company operational data and credentials are not included in the course materials.

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Curriculum

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24 lectures ∙ (1hr 16min)

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