Online professional certificate
SQL for Business Analytics
Turn business questions into SQL results you can run, check and explain. Practise with original retail data, reusable work products and a decision-ready final analysis.
- Format
- 100% online, self-paced text lessons
- Study time
- Up to 1 month
- Curriculum
- 4 modules, 20 lessons and an applied capstone
- Language
- English
Practical capability
Turn a data question into a checked decision.
Learn the SQL and analytical controls that make a business result useful to another person: a clear question, permitted data, correct row grain, tested measures and an honest handoff.
Clarify the question, owner, population, period and measure before querying.
Inspect tables, keys, access boundaries and quality problems in a reproducible SQLite practice database.
Use filters, grouped measures and joins while testing row counts and totals.
Compare valid segments and cohorts, then inspect CTE and window calculations.
Review query plans and AI-assisted SQL against actual rows, edge cases and source rules.
Specify a reporting dataset and scorecard, then deliver a checked result with limits and a decision owner.
Who this course is for
For professionals who need reliable SQL evidence.
The course starts with a business question and a supplied practice database, so prior SQL experience is not required. It is useful when your work involves reports, operational decisions or checked analytical handoffs.
The operating cycle
Follow the analyst's working cycle.
Move from the first request to a result someone else can review, with an owner at each decision point.
Curriculum
Four modules. Twenty applied lessons.
Module 1 — Frame the Question and Read the Data
Good SQL work begins with the question and the data a team is allowed to use. You will identify the decision, set up a reproducible SQLite practice environment, confirm source access, and map how records relate before relying on a query result. By the end of this module, you can make a scoped extraction, explain which rows it includes, and show another analyst the source, grain, filters, and run evidence behind the answer.
01 Turn a Business Request into a Bounded SQL Question
Clarify an ambiguous request in an Analytical Request Brief before writing SQL.
Five practical steps
- Identify the requester, intended decision and reader.
- Ask which population and time period matter.
- Separate the proposed measure from its unconfirmed definition.
- Name the metric and decision owners and record open questions.
- Write an acceptance question before drafting SQL.
Primary deliverable: Analytical Request Brief
02 Run and Record a Reproducible SQLite Query
Run a saved SQLite query and record a reproducible result and local fallback.
Five practical steps
- Open the supplied SQL Practice Dataset and identify its tables.
- Load the original fixture through the documented local SQLite route.
- Run the saved starter query and record the exact result.
- Rerun the query and note the SQLite version and source used.
- Recognize SQLite-specific syntax and retain the local CLI fallback.
Primary deliverable: SQL Execution Record
03 Map Authorized Sources and Data Boundaries
Map approved data sources, sensitive fields and access owners before analysis.
Five practical steps
- List candidate tables and the purpose of each field.
- Confirm the local permission rule and access owner.
- Record source freshness and any restricted field.
- Exclude out-of-scope fields from the planned analysis.
- Route an unresolved permission or definition to its owner.
Primary deliverable: Source and Access Map
04 Identify Row Grain, Keys, and Relationships
Identify each table’s row grain, keys and relationship risks before joining data.
Five practical steps
- State what one row represents in each source table.
- Identify candidate primary and foreign keys.
- Trace one-to-many relationships before a join.
- Predict where rows may multiply or disappear.
- Write a grain and key check for the intended output.
Primary deliverable: Grain and Key Map
05 Extract the Right Rows with a Scoped SQL Query
Write a scoped SQL extraction with clear filters and date boundaries.
Five practical steps
- Confirm the agreed population and date boundaries.
- Select only the fields needed for the question.
- Write explicit SQL filters, including null treatment.
- Run the scoped query and inspect representative rows.
- Save the query and record the included and excluded rows.
Primary deliverable: Scoped Extraction Query
Module 2 — Define Measures and Control Multi-Table Results
A query can run and still give a misleading number. You will profile source data, agree on metric definitions, calculate grouped KPIs, and design joins with explicit row controls. The module ends with a multi-table result whose grain, unmatched records, and totals have been checked. You can explain both the business measure and the tests that make it usable.
06 Profile Data and Record Quality Findings
Profile source data and record quality findings that could change the answer.
Five practical steps
- Count source rows and check key uniqueness.
- Measure missing and invalid values in relevant fields.
- Inspect a small set of duplicate or unexpected records.
- Record which issue could alter the business answer.
- Assign the issue to a source owner or state its limit.
Primary deliverable: Data Profile and Findings Log
07 Define a Business Metric Before Calculating It
Define a business metric with its population, denominator, period and owner.
Five practical steps
- Name the decision the measure supports.
- Specify numerator, denominator and eligible population.
- Set period, grain, exclusions and missing-data treatment.
- Identify the owner who confirms the definition.
- Record a test case before calculating the measure.
Primary deliverable: Metric Definition Sheet
08 Calculate Grouped KPIs and Reconcile Totals
Calculate grouped KPIs and reconcile them to the checked base population.
Five practical steps
- Start from the checked eligible base population.
- Group rows at the required business grain.
- Calculate the selected count or rate in SQL.
- Reconcile group totals to the independent base count.
- Record exceptions and the measure definition used.
Primary deliverable: Grouped KPI Query and Control
09 Design Joins with Row-Count Controls
Plan a join with explicit keys, unmatched-row treatment and row-count tests.
Five practical steps
- Draw the intended join path and output grain.
- Choose keys and join type for the question.
- Predict unmatched and multiplied rows.
- Write pre-join and post-join row-count checks.
- State how a failed control will be investigated.
Primary deliverable: Join Design and Row-Control Plan
10 Build a Checked Multi-Table Result
Build a multi-table result and verify its intended grain and row controls.
Five practical steps
- Run the planned multi-table SQL join.
- Inspect duplicate keys and unmatched records.
- Compare row counts before and after each join.
- Calculate the bounded result at the intended grain.
- Save the checked result and evidence table.
Primary deliverable: Joined Result Query and Evidence Table
Module 3 — Investigate Patterns with Inspectable SQL
When a result changes, you need to know whether the change belongs to the business or the calculation. You will reconcile important measures, compare valid segments and cohorts, and keep observation periods and denominators consistent. You will then structure longer SQL with CTEs and use a window calculation where it helps. Each stage has a test, so you can find and repair a wrong result rather than presenting a plausible number.
11 Reconcile SQL Measures to a Trusted Control
Reconcile an SQL measure to a trusted control and explain any material difference.
Five practical steps
- Choose an independent trusted control total.
- Run the measure query with a recorded period and definition.
- Compare SQL output with the separate control.
- Investigate the material difference by grain, filter or source.
- Record a resolved explanation or named unresolved owner.
Primary deliverable: Reconciliation Record
12 Compare Valid Business Segments with SQL
Compare business segments using explicit membership rules and compatible denominators in SQL.
Five practical steps
- Define segment membership without overlap ambiguity.
- Use a common time window and compatible denominators.
- Calculate each segment result in SQL.
- Check small or missing groups before comparison.
- Write a proportionate interpretation and its limit.
Primary deliverable: Segment Comparison Sheet
13 Build a Cohort Analysis with Consistent Windows
Build a cohort table with consistent follow-up periods and cautious interpretation.
Five practical steps
- Define cohort entry and fixed membership.
- Set equal follow-up windows for each cohort.
- Build the cohort table with explicit denominators.
- Check incomplete observation periods and missing events.
- Describe the pattern without claiming its cause.
Primary deliverable: Cohort Analysis Table
14 Use CTEs to Make SQL Stages Inspectable
Use CTEs to make multi-stage SQL easier to inspect and repair.
Five practical steps
- Break a long query into named SQL stages.
- State the grain and output of each CTE.
- Inspect intermediate row counts and key values.
- Locate and repair a seeded stage error.
- Compare the final result with the checked reference.
Primary deliverable: Staged CTE Query
15 Use Window Functions Without Losing Row Context
Use a window function for a within-group calculation while preserving row context.
Five practical steps
- Choose a within-group measure that needs row context.
- Define partition, order and frame deliberately.
- Run the window calculation without collapsing rows.
- Test ties, nulls and boundary records.
- Record the result and the edge-case checks.
Primary deliverable: Window Measure Query and Test Table
Module 4 — Deliver SQL Evidence for a Decision
Business analysis is useful when the next reader can trust and act on it. You will review query efficiency without changing the answer, specify a reusable reporting dataset, and choose the measures and caveats a reader needs. You will also test AI-assisted SQL against the source and edge cases. The final work is a concise handoff that links the finding to its query and checks, names uncertainty, and identifies who makes the business decision.
16 Review Query Efficiency Without Changing the Answer
Review query efficiency and confirm that a rewrite keeps the same business answer.
Five practical steps
- State the business result that must remain unchanged.
- Read SQLite plan evidence for the original query.
- Try a bounded equivalent query form.
- Compare rows and values for answer parity.
- Explain plan evidence and its speed or cost limits.
Primary deliverable: Query Performance Review Note
17 Specify a Reusable Reporting Dataset
Specify a reusable reporting dataset with definitions, lineage and ownership.
Five practical steps
- Specify the reporting dataset grain and permitted fields.
- Document metric definitions and source lineage.
- Name refresh, access and change owners.
- Set validation controls and a change trigger.
- Write an explicit handoff rather than building a pipeline.
Primary deliverable: Reporting Dataset Contract
18 Design a Decision-Ready Scorecard
Design a compact scorecard that makes checked measures and limits easy to read.
Five practical steps
- Choose only measures that support the decision.
- Specify period, comparison and display grain.
- Attach definitions, caveats and freshness information.
- Identify the reader and refresh owner.
- Test whether the scorecard supports one clear question.
Primary deliverable: Scorecard Evidence Specification
19 Challenge AI-Assisted SQL with Independent Tests
Test AI-assisted SQL against source rules, edge cases and independent query checks.
Five practical steps
- Take an AI-assisted SQL suggestion as a draft.
- Check table names, permitted fields and intended grain.
- Test joins, filters and edge cases against source rows.
- Run an independent calculation or control.
- Keep the corrected query and an accountable review record.
Primary deliverable: AI SQL Review Record
20 Hand Off a Checked SQL Finding for Decision
Hand off a checked SQL finding with uncertainty and a named decision owner.
Five practical steps
- State the checked result in plain language.
- Link the reproducible SQL, source period and definitions.
- Summarize material checks and unresolved uncertainty.
- Separate recommendation from the owner’s decision.
- Hand off the memo with the next action and named owner.
Primary deliverable: Decision Evidence Handoff Memo
Applied capstone
Explain a possible returns delay with checked SQL.
Use the supplied synthetic retail data and the SQL methods relevant to this decision.
The situation
A home-goods operations lead sees possible return-processing delays after a routing change.
Your task
Use the supplied synthetic retail data and the relevant course methods to compare the agreed delay measure across two periods. Treat customer-acquisition channel mix as limited context while checking output grain and material totals. Explain what the delay comparison supports and what remains uncertain.
The people behind MTF
Meet MTF faculty and the learner community.
Explore the professional backgrounds of MTF faculty and learn more about the international community studying with the Institute.
Enrollment
Enroll in Professional Certificate in SQL for Business Analytics
One-time course price: €10, including applicable taxes. Payment is processed securely by Stripe. No card details are stored on the MTF Institute website.
You will receive an email with access to the course. If you have any difficulties, please write to welcome@gtf.pt.
Questions and details
Frequently asked questions
Open the sections that matter to you, including delivery format, AI-supported practice and the evidence used to design the curriculum.
Do I need SQL experience to begin?
No. The course starts with a business request and a guided SQLite setup. You then move from scoped queries to joins, measures and decision handoffs using the supplied practice data.
Which data and tools will I use?
You will use an original synthetic relational retail dataset in SQLite. The course provides a text-based local command-line route and an optional browser route, so employer database access is not required.
What will I make?
The 20 lessons each build a distinct work product, from an Analytical Request Brief and checked SQL queries to a Reporting Dataset Contract and Decision Evidence Handoff Memo. The separate applied capstone produces one SQL-backed decision readout.
How is AI used in the practical work?
Lessons offer prompts for drafting and critique. You compare suggestions with the supplied schema, run the SQL yourself, check source rows and calculations, and keep the reviewed version as your work record.
How is the course assessed?
Each lesson has a practical artifact and observable checks. In the final case, you compare return-processing delays, validate the measure and give an operations lead a reproducible, bounded conclusion.
What evidence informed the curriculum?
The course draws on MTF Institute research into 100 selected U.S. analyst and business-intelligence vacancies plus a separate review of current SQL and analytics platform changes. The vacancy sample is purposive and is not a measure of national hiring prevalence.
How long is the course and how do I access it?
The course is online and self-paced, with a study-time guide of up to one month. After enrollment, you receive access through the MTF learning platform to four modules, 20 lessons, the Role Starter Pack and the applied capstone.