Excel, SQL, and Data Science

Last reviewed May 28, 2026 Content v20260528

Track mode

none

Means

Read / quiz

Reading

~1 min

Level

intermediate

This lesson

This lesson teaches Excel, SQL, and Data Science: Excel concepts, formulas, and analysis patterns you recreate locally in a workbook.

Teams apply Excel, SQL, and Data Science in every serious Excel workflow—skipping it leaves errors in models stakeholders trust.

You will apply Excel, SQL, and Data Science in contexts like: Handoff between analysts in Excel and engineers in SQL/Python pipelines.

Read the lesson, type formulas in your local Excel or compatible spreadsheet, rebuild examples in excel-practice.xlsx, and complete MCQs—no in-browser runner. Also export a Table to CSV and open the next track on /sql/intro or /pandas/intro.

When formulas, lookups, pivots, and charts feel familiar—or when interviewing for analyst and business-facing roles.

Use the right tool per layer: Excel for stakeholder models, SQL for warehouse truth, Python for scale and ML.

Flow

SQL aggregates daily metrics in warehouse
Export CSV or connect Power Query to database
Excel pivots/charts for leadership review
Python notebooks for experiments and production models

Links

Continue on SQL, Data Science, Pandas.

Important interview questions and answers

Q: Source of truth?
A: Often SQL warehouse—not a emailed xlsx version 7.
Q: Excel role?
A: Presentation and what-if, not petabyte ETL.

Self-check

Where should metrics be authoritative?
Excel's role after SQL export?

Tip: Agree which file version is "source of truth" before debating numbers.

Interview prep

Source of truth?: Often SQL warehouse, not emailed xlsx v7.

Discussion

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Starter discussion topics

Source of truth?
Next tracks?

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