Excel, SQL and AI Skills: A 30-Day Learning Plan for Job Seekers

You do not need to master Excel, SQL, and AI in a month. You can, however, build a useful working foundation in 30 days if you practise on small problems instead of watching tutorials from morning to night. The goal is to finish the month with skills you can demonstrate, not a folder full of course certificates.

What to aim for by day 30

By the end of the month, you should be comfortable cleaning a simple dataset in Excel, writing basic SQL queries, and using AI to support your work without blindly accepting its answers. A small portfolio project that combines the three is more valuable than knowing a hundred isolated features.

Week 1: Build your Excel base

Days 1–2: Core formulas

Start with SUM, AVERAGE, COUNT, IF, SUMIF/SUMIFS, COUNTIF/COUNTIFS, and basic text and date functions. Practise on a real-looking dataset such as sales, customer tickets, or job applications so the formulas answer a question instead of becoming an exercise with no context.

Days 3–4: Lookups and cleaning

Learn XLOOKUP and the basics of matching information between tables. Then practise removing duplicates, cleaning inconsistent values, splitting text, checking blanks, and turning messy dates into a consistent format.

Days 5–7: Pivot tables and charts

Build a pivot table and answer three simple business questions. Which category is growing? Where are the biggest gaps? Which month changed the most? Turn the answers into a small chart and write a few sentences explaining what you found.

Week 2: Learn SQL by answering questions

Start with the small set that matters most

Focus on SELECT, WHERE, ORDER BY, GROUP BY, aggregate functions, CASE, and JOIN. You do not need advanced database administration skills to demonstrate that you can work with data.

Use business questions as practice

Instead of copying query examples, write questions first: “Which products had the most orders?”, “Which customers placed more than three orders?”, or “What was monthly revenue?” Then write the query that answers the question. When the result looks wrong, trace the logic one clause at a time.

Week 3: Add AI to the workflow

Use an AI tool as a helper, not as an answer machine. Ask it to explain a SQL error, suggest edge cases, create practice data, or review your explanation. Then test the result yourself. You should be able to explain what a query does even if AI helped you draft it.

Keep private data out of unapproved tools

When practising, use public, synthetic, or anonymised data unless your organisation has explicitly approved the tool and workflow. Convenience is not a reason to upload customer records or internal information.

Week 4: Build one small project

Choose a problem with a clear question

For example, analyse a retail dataset and identify which products are underperforming, or study support-ticket data and find the categories with the longest resolution times. Clean the data in Excel, answer deeper questions in SQL, and use AI only where it genuinely saves time.

Document the result

Your project should show the problem, the data you used, the cleaning steps, the analysis, the main findings, and what someone should do next. Include screenshots or a simple dashboard when they make the result easier to understand.

A realistic daily routine

Give each session a clear job. Thirty minutes of learning, 45 minutes of hands-on work, and 15 minutes reviewing mistakes is enough for many beginners. On a busy day, do less rather than skipping the week entirely.

What to put on your resume

Do not simply write “Excel, SQL, AI” under skills. Add evidence. A project showing data cleaning, joins, reporting, or process analysis gives the recruiter something concrete to ask about in the interview.

Conclusion

Thirty days is enough to build momentum, not mastery. Focus on the parts of Excel and SQL that help you answer real questions, use AI as a second pair of eyes, and finish with one project you can explain from beginning to end. That gives you a much stronger foundation for entry-level analyst and operations roles.