Excel to SQL for MIS analysts: a 16-week path, month by month

If you run MIS reports in Excel, the move to analyst work is shorter than it looks, but it isn't one course or one tool. Excel to SQL for MIS analysts comes down to four steps, in this order: make your Excel reports refresh instead of being rebuilt, learn to pull and join the data yourself in SQL, learn the SQL that answers "compared with what" questions, and then learn to check every number, including the ones an AI assistant writes for you. At about eight to ten hours a week, that is roughly sixteen weeks of work, one month per step. After that come the BI tool and the rest of the stack, which the next notes in this series take one layer at a time.

Why SQL is the next layer, not Python or a dashboard tool

In the last note we counted what employers ask analysts for, across three samples of job posts (What employers ask data analysts for in 2026). SQL was the most consistent signal in every count that ranked it. Excel came out on top in one Indian sample and much lower in another.

LinkedIn's Skills on the Rise 2026 list for India names querying and database optimisation in its Data & Analytics stack, next to data storytelling and data-driven decision-making ¹. The report doesn't describe LinkedIn's method, so treat it as a direction, not a measurement.

SQL is not niche outside analytics either. In Stack Overflow's 2025 Developer Survey, 61.3% of professional developers said they had used SQL in the past year, third after JavaScript and HTML/CSS; MySQL was used by about 40% and Microsoft SQL Server by about 31% ³. That survey is of developers, not analysts, and it is from mid-2025. The point still holds: the database behind your MIS exports is very likely one of these.

One caution before you plan around demand. Indeed reported that Indian data and analytics postings were down 14% in recent months ². Fewer postings means more applicants looking at each one, so what you can show matters more than what you list.

Excel to SQL for MIS analysts: the 16-week path

Month	What you learn	What you build	Where the series picks it up
1 (weeks 1–4)	Excel done properly: tables, lookups, Power Query, pivots, error checks	Your own monthly MIS report, rebuilt to refresh	—
2 (weeks 5–8)	SQL foundations: SELECT, filters, GROUP BY, joins, subqueries	20 business questions answered from a sample database	The query layer
3 (weeks 9–12)	SQL for analysis: CTEs, window functions, indexes, execution plans; automating the Excel side	A month-on-month report fed by SQL	The query layer
4 (weeks 13–16)	Copilot and SQL assistants, and checking their output; one end-to-end project	A project with a one-page write-up	The BI tool

Month 1: stop rebuilding the report

Start with the report you already own. Most MIS work is the same export, the same paste, the same VLOOKUP, every week. Rebuild it so the raw data lands in an Excel table, Power Query does the cleaning, and pivots and lookups sit on top. Add checks: row counts, totals that should match the source, flags for blanks.

Milestone: you change the source file, press Refresh, and the report is right. If you can't do that yet, don't move on.

Month 2: pull the data yourself

Install MySQL or SQL Server Express and load a sample database. Learn SELECT, WHERE and GROUP BY, then joins and nested queries. Write each query to answer a real question ("Which branches missed target in three of the last four months?"), not to practise syntax.

Then join the two worlds. Excel can run a SQL statement directly against SQL Server through Data > Get Data > From Databases, with your query typed under Advanced Options, and load the result into Power Query ⁴. If you run a query someone else wrote, Excel asks you to confirm it, because it runs with your credentials ⁴. That is a good habit to keep at work.

Milestone: 20 questions answered, each with the query and one sentence saying what the answer means.

Month 3: answer "compared with what"

Managers rarely ask for a total. They ask how this month compares with last month, or which product ranks where within each region. That is window-function work. MySQL's manual puts the difference plainly: a GROUP BY aggregate turns a group of rows into a single result row, while a window function "produces a result for each query row" ⁵. Running totals, month-on-month change and top-N per group all come from that idea, usually written as CTEs so a reviewer can follow them.

This is also the month to learn why a query is slow (indexes, reading an execution plan) and to automate the Excel end with VBA or Office Scripts, so the SQL output reaches its readers without you.

Milestone: a report showing month-on-month change and rank within group, fed by SQL, refreshed without copy-paste.

Month 4: check the machine, then ship one project

Copilot in Excel can write formulas, create and edit PivotTables and charts, and apply sorting, filtering and conditional formatting ⁶. It needs an eligible licence, which your employer may or may not have ⁶. Assistants write SQL too. Use them, and treat every result as a draft: reconcile it against a total you trust, read the join conditions, and check what happens to blanks and duplicates. Don't paste company data into a tool your employer hasn't approved.

Finish with one project end to end: a question, the SQL, the Excel output, and a one-page write-up of what you found and how you checked it.

Milestone: someone who doesn't know you can read the write-up and rerun your work.

Common wrong turns

• Starting with Python or a dashboard tool. Both sit on top of clean, joined data. Without SQL you'll be back in Excel preparing it by hand.
• Collecting syntax instead of answers. A folder of 200 queries on toy data shows less than ten that answer business questions.
• Skipping the checks. In MIS, a wrong number does more damage than a late one. That stays true when the number came from Copilot.
• Moving on before the milestone. Each month depends on the one before it.

After week 16: the rest of the stack

The BI tool is the natural next layer, and its certification shows what employers expect from it. Microsoft's study guide for the PL-300 Power BI Data Analyst exam weights preparing the data, modelling it, and visualising and analysing it at 25–30% each, and managing and securing Power BI at 15–20%, and expects candidates to be proficient in Power Query and DAX ⁷. The Power Query you learned in month 1 carries straight over.

The rest of this series follows that order: the query layer, the BI tool, its certification, data preparation at scale, the platform, and Python beyond the BI tool. TTFA's Data Analyst with Excel and SQL course covers the four months above, including Copilot in Excel and checking AI output.

What to do with this

This week, pick the one report you rebuild most often and write down every manual step. That list is your month 1 plan. Then block the hours in your calendar before you sign up for anything.

The next note, "SQL interview questions for analysts: the twelve patterns behind most of them", takes months 2 and 3 and turns them into the query patterns analyst interviews keep returning to, from joins and aggregation to window functions, with how to talk through each one.

Frequently asked questions

Can I do this in 16 weeks while working full time?

At eight to ten hours a week, sixteen weeks is a realistic estimate for the four stages here. If you can give only four or five hours, plan for about twice as long. Moving on before a milestone is what usually makes it take longer.

Should I learn MySQL or SQL Server first?

Learn whichever your company's data sits in. If you don't know, either works: SELECT, JOIN and GROUP BY carry across both, and only some functions and syntax differ.

Do I still need Excel once I know SQL?

Yes. SQL pulls and shapes the data; Excel is still where many teams read it, check it and pass it on. Excel can run your SQL query and refresh the result through Power Query.

Is Copilot enough instead of learning SQL?

No. It can draft a formula or a query, but you have to be able to read it to know whether it is right. Checking its output is a skill in itself, and it depends on knowing SQL.